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The Impact of Generative AI on Syllabus Design and Learning

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Title: The Impact of Generative AI on Syllabus Design and Learning
Language: English
Authors: Hyoseok Kim (ORCID 0009-0007-2347-1357), Thomas K. B. Koo (ORCID 0000-0002-2668-2770)
Source: Journal of Marketing Education. 2026 48(1):20-41.
Availability: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
Peer Reviewed: Y
Page Count: 22
Publication Date: 2026
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Artificial Intelligence, Educational Quality, Technology Uses in Education, Course Descriptions, Marketing, Business Education, Student Attitudes, Material Development, Authors, Disclosure, Undergraduate Students, Foreign Countries, Conventional Instruction
Geographic Terms: North America
DOI: 10.1177/02734753241299024
ISSN: 0273-4753
1552-6550
Abstract: This research examines the impact of generative artificial intelligence (AI) on the perception of educational content quality, specifically by comparing AI-generated and human-generated course syllabi in marketing education. Results from four studies indicate a general preference for AI-generated syllabi, attributed to their greater perceived objectivity. This preference is more pronounced in conventional courses but diminishes in unconventional ones, suggesting that the unique aspects of these courses may reduce the advantages of generative AI. In addition, disclosing the AI authorship of syllabi significantly affects their perceived quality negatively, underscoring the impact of transparency on the acceptance of AI-generated educational materials. These findings highlight the potential of generative AI in educational content creation and its limitations in certain contexts. They offer valuable insights for enhancing educational practices and shaping policy decisions to enrich student experiences in the era of AI integration.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1499495
Database: ERIC
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  Value: <anid>AN0191808988;mke01apr.26;2026Feb25.04:50;v2.2.500</anid> <title id="AN0191808988-1">The Impact of Generative AI on Syllabus Design and Learning </title> <p>This research examines the impact of generative artificial intelligence (AI) on the perception of educational content quality, specifically by comparing AI-generated and human-generated course syllabi in marketing education. Results from four studies indicate a general preference for AI-generated syllabi, attributed to their greater perceived objectivity. This preference is more pronounced in conventional courses but diminishes in unconventional ones, suggesting that the unique aspects of these courses may reduce the advantages of generative AI. In addition, disclosing the AI authorship of syllabi significantly affects their perceived quality negatively, underscoring the impact of transparency on the acceptance of AI-generated educational materials. These findings highlight the potential of generative AI in educational content creation and its limitations in certain contexts. They offer valuable insights for enhancing educational practices and shaping policy decisions to enrich student experiences in the era of AI integration.</p> <p>Keywords: generative AI; AI integration; marketing education; course syllabus; student perceptions; educational practices</p> <p>In recent years, the world has undergone a transformative shift with the emergence of generative artificial intelligence (AI), marking the dawn of a new era in advanced technology. This evolution has not only fundamentally reshaped our approach to efficiency and automation but has also extended its influence across various fields, inspiring reflection on how generative AI could redefine and augment human capabilities. The continuous exploration of generative AI has led to groundbreaking innovations, such as ChatGPT, a chatbot developed by OpenAI ([<reflink idref="bib30" id="ref1">30</reflink>]). Powered by natural language processing, ChatGPT generates human-like responses, representing a significant advancement in AI-human interactions ([<reflink idref="bib16" id="ref2">16</reflink>]).</p> <p>Generative AI has sparked a transformative wave across diverse fields, with each domain benefiting from its unique capabilities. In education, this technology is profoundly reshaping learning experiences ([<reflink idref="bib4" id="ref3">4</reflink>]; [<reflink idref="bib11" id="ref4">11</reflink>]). By providing personalized materials and language learning applications tailored to each student's unique needs, it enhances engagement and understanding, fostering a more inclusive and effective educational environment ([<reflink idref="bib2" id="ref5">2</reflink>]). These AI-driven tools leverage data analysis to assess students' academic strengths, weaknesses, and preferences, enabling the development of customized learning paths and resources.</p> <p>However, despite these advancements, the role of human experts remains irreplaceable. Human educators bring a depth of understanding, empathy, and personalized teaching methods that AI cannot fully replicate. These qualities are crucial for fostering meaningful interactions and addressing diverse learning styles ([<reflink idref="bib8" id="ref6">8</reflink>]). Thus, while generative AI can offer creative and efficient recommendations, it is essential to recognize how human expertise complements AI's potential.</p> <p>Given this context, assessing the comparison between AI and human experts is vital for understanding their unique strengths and limitations, and for optimizing their integration to enhance educational effectiveness. Therefore, the overall aim of this paper is to offer valuable insights into human-generated versus AI-generated syllabi as a first step in that direction. Specifically, our first research question addresses how AI's performance, relative to that of human experts, influences students' perceptions of course content, such as the syllabus. We predict that students are more likely to perceive an AI-generated syllabus as superior to a human-generated one due to its higher perceived objectivity. This aligns with previous research, which indicates that AI-generated content is often viewed as more objective and credible ([<reflink idref="bib6" id="ref7">6</reflink>]). Our second research question explores how the conventionality of the course influences this perception. Specifically, we predict that the perceived superiority of an AI-generated syllabus will be more pronounced for conventional courses (e.g., "Introduction to Marketing") but will diminish for unconventional ones (e.g., "Design Thinking in Marketing and Life"). Finally, our third research question seeks to uncover how disclosing the authorship of the syllabus might reduce the perceived superiority of an AI-generated syllabus compared to a human-generated one, with transparency potentially impacting students' perceptions and satisfaction.</p> <p>This research significantly contributes to understanding the dynamics between human and generative AI contributions within the domain of marketing education. By empirically validating hypotheses regarding student preferences for AI-generated syllabi based on perceived objectivity, this paper reveals the underlying mechanism driving these preferences. In addition, the present research enhances our understanding of the course context by exploring how course conventionality influences student perceptions of syllabus quality. This nuanced understanding of how contextual factors influence preferences for AI-generated educational materials provides valuable guidance for educators, curriculum designers, and policymakers. It can help them enhance educational practices and enrich student learning experiences by integrating AI effectively while appreciating the irreplaceable value of human expertise.</p> <hd id="AN0191808988-2">Theoretical Background</hd> <p></p> <hd id="AN0191808988-3">Generative AI in Marketing Education</hd> <p>ChatGPT, launched on November 30, 2022, represents a pivotal moment in generative AI—a type of machine learning that produces human-like text, code, images, videos, and sounds based on existing data ([<reflink idref="bib25" id="ref8">25</reflink>]). It set a record for the fastest user growth, reaching 100 million active users within just two months of its launch ([<reflink idref="bib15" id="ref9">15</reflink>]). This rapid adoption foreshadows the anticipated expansion of the generative AI market, which is projected to grow at an annual rate of approximately 36% ([<reflink idref="bib24" id="ref10">24</reflink>]). The market value is expected to increase from $10.5 billion in 2022 to an estimated $191.8 billion by 2032 ([<reflink idref="bib1" id="ref11">1</reflink>]). These projections underscore the transformative potential of generative AI, extending far beyond traditional technological boundaries.</p> <p>As generative AI continues to advance, it introduces significant changes and efficiencies across various domains. For instance, generative AI significantly enhances efficiency and creativity in fashion design ([<reflink idref="bib35" id="ref12">35</reflink>]), music composition ([<reflink idref="bib38" id="ref13">38</reflink>]), health care ([<reflink idref="bib21" id="ref14">21</reflink>]), and marketing management ([<reflink idref="bib5" id="ref15">5</reflink>]; [<reflink idref="bib17" id="ref16">17</reflink>]). This progress also extends into education, where generative AI is playing a pivotal role in transforming the learning landscape. Its diverse applications encompass reviewing and assessing student work, delivering personalized learning experiences, creating interactive content, and facilitating adaptive assessments ([<reflink idref="bib2" id="ref17">2</reflink>]; [<reflink idref="bib18" id="ref18">18</reflink>]). [<reflink idref="bib27" id="ref19">27</reflink>] provide a practical example of AI-generated courseware, highlighting its efficacy in enhancing student outcomes and faculty teaching practices.</p> <p>In marketing education specifically, generative AI enhances learning by providing personalized materials and language applications that adapt to individual styles and progress ([<reflink idref="bib11" id="ref20">11</reflink>]). Research by [<reflink idref="bib4" id="ref21">4</reflink>] and [<reflink idref="bib36" id="ref22">36</reflink>] indicates that generative AI can further enrich student learning experiences, such as by serving as an assistant to improve writing skills in marketing courses. [<reflink idref="bib7" id="ref23">7</reflink>] highlight that generative AI is particularly valuable for ideation, especially in developing marketing strategies. In addition, generative AI can offer counterarguments and alternative perspectives, helping students in refining and strengthening their own arguments ([<reflink idref="bib3" id="ref24">3</reflink>]).</p> <p>Despite generative AI's significant contributions to marketing education, the historical context highlights the enduring role of human experts. These experts not only excel in imparting knowledge and facilitating creative learning experiences but also stand out in fostering meaningful interactions between students and educators. Their profound impact is evident in their nuanced insights and empathetic understanding, which shape both the intellectual and emotional growth of students ([<reflink idref="bib8" id="ref25">8</reflink>]). Furthermore, a key aspect of human expertise in education is their ability to tailor teaching tools to meet the unique needs of individual students ([<reflink idref="bib19" id="ref26">19</reflink>]). This adaptability highlights how effectively human educators address the diverse learning styles and preferences in educational settings ([<reflink idref="bib34" id="ref27">34</reflink>]).</p> <p>Prior research highlights that while generative AI excels in data processing and personalizing learning experiences, human expertise remains essential for fostering meaningful interactions and emotional growth ([<reflink idref="bib8" id="ref28">8</reflink>]). Acknowledging the strengths of both generative AI and human expertise is essential as we advance in marketing education. However, there is a gap in understanding how generative AI's performance compares to that of human experts in producing educational materials. Which one is better in producing high-quality course content: generative AI or human experts? In addition, what psychological mechanisms influence students' perceptions of the quality of course content generated by each? This work addresses these questions by exploring how the objectivity of AI-generated content influences student perceptions of the quality of course content, specifically within the context of course syllabi.</p> <hd id="AN0191808988-4">Heuristics and the Perception of AI-Generated Contents</hd> <p>Prior research has shown that the perception of AI-generated content depends on several factors, including the perceived objectivity, credibility, and reliability of the content. Generative AI is often viewed as providing more objective recommendations compared to human educators ([<reflink idref="bib13" id="ref29">13</reflink>]). This perception arises from the belief that AI systems, which rely on algorithms and data processing, are less influenced by personal biases and ideological perspectives than human counterparts. This view is also supported by [<reflink idref="bib31" id="ref30">31</reflink>] concept of machine heuristics. According to the machine heuristic concept, machines are seen as more objective, accurate, and impartial because they operate without the emotional and ideological biases that can affect human decision-making ([<reflink idref="bib32" id="ref31">32</reflink>]).</p> <p>This heuristic of viewing AI as a neutral and unbiased entity enhances its credibility and contributes to the belief that AI-generated content offers a more objective basis for evaluating materials. Empirical evidence supports this notion. [<reflink idref="bib9" id="ref32">9</reflink>] conducted an online experiment to compare human-written and computer-generated news. They measured a 4-item scale to assess perceptions of credibility, focusing on accuracy, trustworthiness, fairness, and reliability. Their findings demonstrated that machine-generated content was perceived as more credible, reliable, and authoritative than content produced by humans. Similarly, [<reflink idref="bib6" id="ref33">6</reflink>] investigated how people perceive news in terms of informativeness, trustworthiness, objectivity, and descriptiveness. His experimental setting differed in that authorship disclosure was absent, leading to higher perceptions of these attributes for AI-generated news compared to human-written news. These insights are especially relevant to our work in the marketing education context, where the perceived objectivity of course syllabi can significantly influence students' preferences and evaluations.</p> <hd id="AN0191808988-5">Hypotheses Development</hd> <p>Based on previous research and the prevalent belief that AI can provide objective, unbiased, and accurate information, we hypothesize that students are more inclined to prefer a syllabus generated by AI over one created by a human. This preference is attributed to the perception that AI-generated content embodies greater objectivity and reliability. Students may unconsciously perceive AI-generated syllabi as being less susceptible to personal biases and subjective judgments due to the technological and analytical nature of AI systems. Consequently, students are likely to favor AI-generated syllabi due to their perceived objectivity and consistency.</p> <p></p> <ulist> <item> <emph>H1: Students perceive the AI-generated syllabus as superior to the human-generated syllabus.</emph> </item> <p></p> <item> <emph>H2: Students' perceptions of the superiority of the AI-generated syllabus are mediated by greater perceived objectivity.</emph> </item> </ulist> <hd id="AN0191808988-6">Moderating Role of Course (Un) Conventionality</hd> <p>If courses are placed on a continuum ranging from conventional to unconventional, conventional courses are generally more foundational, while unconventional courses tend to be more advanced or specialized. To explore this distinction, we conducted a pretest in which participants reviewed and rated a course outlined in a syllabus categorized as either conventional or unconventional. Ratings were based on four dimensions: conventionality (1 = not conventional to 7 = conventional), foundation (1 = not foundational to 7 = foundational), advancement (1 = not advanced to 7 = advanced), and specialization (1 = not specialized to 7 = specialized). Our pretest (<emph>N</emph> = 160) supported this distinction. Specifically, "Introduction to Marketing" was perceived as more conventional (<emph>p</emph> =.002) and foundational (<emph>p</emph> =.038) but less advanced (<emph>p</emph> =.076) and specialized (<emph>p</emph> =.010) than "Design Thinking in Marketing and Life." For simplicity, we will refer to the first group of courses as conventional and the latter as unconventional, as this scale item showed the greatest perceptual difference.</p> <p>Although generative AI models like ChatGPT do not disclose their specific data sources, it is highly likely that more data is available for conventional courses than for unconventional ones. As a result, generative AI should be capable of creating a syllabus for a conventional course that feels familiar to students. In educational settings, students often encounter standardized topics or curricula, predefined learning objectives, and familiar instructional methods such as lectures, readings, and assignments ([<reflink idref="bib23" id="ref34">23</reflink>]). These conventional approaches provide a structured framework that offers students a predictable path for navigating their academic experiences ([<reflink idref="bib28" id="ref35">28</reflink>]). This consistency facilitates student engagement with course materials and allows them to progress through their studies with a sense of familiarity and confidence. Given generative AI's strength in objectivity, as discussed in the previous section, it is likely to outperform human experts in these areas.</p> <p>In contrast, unconventional courses often move beyond traditional norms, offering innovative content and alternative methods of learning. These approaches frequently break away from standard practices, encouraging creativity, and critical thinking. Such dynamic learning environments foster curiosity and deeper engagement with complex ideas ([<reflink idref="bib22" id="ref36">22</reflink>]; [<reflink idref="bib26" id="ref37">26</reflink>]). Therefore, the perceived quality of unconventional courses is less likely to depend only on objectivity and more on factors such as their ability to empower students to shape their own educational journeys. While generative AI is capable of generating creative and innovative solutions, any idiosyncrasies it produces are likely to be valued similarly to those of human experts. Thus, generative AI is no more likely to outperform human experts in these contexts.</p> <p></p> <ulist> <item> <emph>H3a: Students perceive the AI-generated syllabus as superior to the human-generated syllabus when the course is conventional.</emph> </item> <p></p> <item> <emph>H3b: The perceived superiority of the AI-generated syllabus diminishes when the course is unconventional.</emph> </item> </ulist> <hd id="AN0191808988-7">Authorship Disclosure</hd> <p>Our argument thus far has contended that students would likely favor an AI-generated course syllabus over a human-generated one for a conventional course due to perceived objectivity. However, this preference presupposes that the authorship of the syllabus remains undisclosed. Yet, it is imperative to recognize the ethical implications of revealing the true authorship to students. Previous research underscores the importance of transparency in course content authorship, as it fosters trust, enhances accountability, and promotes informed decision-making ([<reflink idref="bib29" id="ref38">29</reflink>]). When instructors provide clear and accessible information about materials, objectives, and assessment criteria, it empowers students to actively engage with their learning process and take ownership of their educational journey. Moreover, transparency in content authorship cultivates accountability, facilitating effective communication between instructors and students ([<reflink idref="bib14" id="ref39">14</reflink>]).</p> <p>However, students' reactions to disclosing that their syllabus was created by generative AI, and how this disclosure alters their perceptions, remain uncertain. We hypothesize that, if students are unaware of the syllabus's authorship, they are more likely to view the AI-generated syllabus as superior to the human-generated one, due to its perceived objectivity. However, when students learn that their syllabus is AI-generated, they may scrutinize it more critically. Recognizing AI's strengths in data processing but limitations in creativity and contextual understanding, students might reassess the perceived superiority of the AI-generated syllabus, diminishing its appeal. In addition, knowing that the content was created by AI may lead students to question its relevance and applicability to their specific educational needs and goals, further reducing the initial perception of AI-generated content's superiority.</p> <p></p> <ulist> <item> <emph>H4a: Students perceive the AI-generated syllabus as superior to the human-generated syllabus when the authorship is undisclosed.</emph> </item> <p></p> <item> <emph>H4b: The perceived superiority of the AI-generated syllabus diminishes when the authorship is disclosed.</emph> </item> </ulist> <hd id="AN0191808988-8">Study Overview</hd> <p>We conducted four studies to examine students' perceptions of the quality of course syllabi generated by AI versus human experts. Study 1A tested H1, which proposed that students would perceive an AI-generated syllabus as superior to a human-generated one. In Study 1B, we replicated the findings of Study 1A and further examined the underlying psychological mechanism, focusing on perceived objectivity. Study 2 investigated how the conventionality of the syllabus affects perceived quality. Finally, Study 3 explored how disclosing the authorship of the syllabus—whether it is generated by AI or humans—moderates these perceptions. The materials, data, and code can be found at https://osf.io/v726q/?view_only=31efd04c73fc491c9bf517d4dc17c3f8.</p> <hd id="AN0191808988-9">Studies 1A and 1B</hd> <p>The objective of Study 1A was to test whether university students would perceive an AI-generated syllabus as superior in quality to a human-generated one. In Study 1B, we aimed to replicate the findings of Study 1A and explore the psychological mechanisms driving the preference for AI-generated syllabi over human-generated ones. We hypothesize that this preference is due to the perceived objectivity inherent in AI-generated syllabi.</p> <hd id="AN0191808988-10">Study 1A: Method</hd> <p>We recruited 200 U.S. participants through Prolific, with participants completing the study in exchange for nominal monetary compensation. As pre-registered (https://aspredicted.org/88Z%5fKKK), two participants were excluded from the data analysis based on three criteria: completion of the study, current non-enrollment as undergraduate students at a university, and prior completion of an equivalent course to "Introduction to Marketing." Ultimately, we received 198 responses (<emph>M</emph><subs>Age</subs> = 26.33; 54.5% female). We employed a two-cell (syllabus authorship: generative AI vs. human) between-subjects design and participants were randomly assigned to one of the two conditions.</p> <p>First, participants reviewed a syllabus for "Introduction to Marketing." In the human-generated syllabus condition, they examined a course syllabus created by a marketing professor with over 20 years of teaching experience at a major North American University, who had received numerous teaching awards and honors and had an "amazing lectures" rating on ratemyprofessor.com. The human-generated syllabus included eight sections: Instructor Details, Contact Information, Office Hours, Course Overview, Objectives, Session-by-Session Outline, Required Textbooks, and Grading Policies. While all sections were included, the Instructor Details, Contact Information, and Office Hours were left blank. In the AI-generated syllabus condition, one of the authors used ChatGPT to create the course syllabus, ensuring that the sections and their order matched those of the human-generated version (see Appendix for details). For each section, we provided specific prompts to ChatGPT (e.g., "create the course overview") and incorporated the generated content directly into the syllabus. The general look and feel of the two syllabi (e.g., font, spacing) were maintained as closely as possible. Participants were not informed about the creator of the syllabus in this study.</p> <p>Drawing from the syllabus design guidelines outlined by relevant literature on students' syllabus evaluation ([<reflink idref="bib33" id="ref40">33</reflink>]), we measured six dimensions to assess students' perceptions of the course syllabi, with the first one capturing the overall quality perception and the remaining addressing other essential factors of a good syllabus. Participants provided responses in terms of how they perceived the syllabus to be excellent ("Overall, this syllabus is excellent."), current ("This syllabus is consistent with the current marketing trend."), interesting ("This course, as indicated by the syllabus, appears interesting"), engaging ("This course, as outlined in the syllabus, seems engaging."), clear ("Based on this syllabus, it is clear what this course is about."), and reasonable in performance assessment ("The performance assessment in this syllabus is reasonable."). Participants provided responses on a Likert-type scale ranging from 1 (strongly disagree) to 7 (strongly agree).</p> <hd id="AN0191808988-11">Study 1A: Results</hd> <p>As shown in Table 1, we conducted a series of independent samples t-tests to examine whether students perceived the AI-generated syllabus to be superior to the human-generated syllabus in terms of the following dimensions: excellent, current, interesting, engaging, clear, and reasonable in performance assessment.</p> <p>Table 1. Syllabus Evaluation by Authorship for Study 1A.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th align="center">Dimensions (<italic>N</italic> = 198)</th><th align="center">AI-generated vs. Human-generated</th><th align="center"><italic>p</italic>-value</th></tr></thead><tbody><tr><td>Excellent</td><td>M = 5.32 (<italic>SD</italic> = 1.07) vs. M = 4.92 (<italic>SD</italic> = 1.12)</td><td><italic>p</italic> =.010<xref ref-type="table-fn" rid="tfn1">**</xref></td></tr><tr><td>Current</td><td>M = 5.23 (<italic>SD</italic> = 1.04) vs. M = 4.81 (<italic>SD</italic> = 1.06)</td><td><italic>p</italic> =.005<xref ref-type="table-fn" rid="tfn1">**</xref></td></tr><tr><td>Interesting</td><td>M = 4.53 (<italic>SD</italic> = 1.49) vs. M = 4.25 (<italic>SD</italic> = 1.49)</td><td><italic>p</italic> =.186</td></tr><tr><td>Engaging</td><td>M = 4.63 (<italic>SD</italic> = 1.50) vs. M = 4.77 (<italic>SD</italic> = 1.46)</td><td><italic>p</italic> =.516</td></tr><tr><td>Clear<xref ref-type="table-fn" rid="tfn1">**</xref></td><td>M = 6.18 (<italic>SD</italic> = 0.99) vs. M = 5.41 (<italic>SD</italic> = 1.15)</td><td><italic>p</italic> <.001<xref ref-type="table-fn" rid="tfn1">**</xref></td></tr><tr><td>Reasonable in Assessment<xref ref-type="table-fn" rid="tfn1">*</xref></td><td>M = 5.41 (<italic>SD</italic> = 1.27) vs. M = 5.09 (<italic>SD</italic> = 1.26)</td><td><italic>p</italic> =.075<xref ref-type="table-fn" rid="tfn1">*</xref></td></tr></tbody></table> </ephtml> </p> <p>1 <emph>p</emph> <.10, **<emph>p</emph> <.05.</p> <hd id="AN0191808988-12">Excellent</hd> <p>Students perceived the AI-generated syllabus (<emph>M</emph> = 5.32, <emph>SD</emph> = 1.07) to be more excellent than the human-generated syllabus, <emph>M</emph> = 4.92, <emph>SD</emph> = 1.12; <emph>t</emph>(<reflink idref="bib196" id="ref41">196</reflink>) = -2.60, <emph>p</emph> =.010.</p> <hd id="AN0191808988-13">Current</hd> <p>Students perceived the AI-generated syllabus (<emph>M</emph> = 5.23, <emph>SD</emph> = 1.04) to be more current than the human-generated syllabus, <emph>M</emph> = 4.81, <emph>SD</emph> = 1.06; <emph>t</emph>(<reflink idref="bib196" id="ref42">196</reflink>) = -2.84, <emph>p</emph> =.005.</p> <hd id="AN0191808988-14">Interesting</hd> <p>There was no significant effect for syllabus authorship on how interesting the syllabus was between the AI-generated syllabus (<emph>M</emph> = 4.53, <emph>SD</emph> = 1.49) and the human-generated syllabus, <emph>M</emph> = 4.25, <emph>SD</emph> = 1.49; <emph>t</emph>(<reflink idref="bib196" id="ref43">196</reflink>) = -1.33, <emph>p</emph> =.186.</p> <hd id="AN0191808988-15">Engaging</hd> <p>There was no significant effect for syllabus authorship on how engaging the syllabus was between the AI-generated syllabus (<emph>M</emph> = 4.63, <emph>SD</emph> = 1.50) and the human-generated syllabus, <emph>M</emph> = 4.77, <emph>SD</emph> = 1.46; <emph>t</emph>(<reflink idref="bib196" id="ref44">196</reflink>) = 0.65, <emph>p</emph> =.516.</p> <hd id="AN0191808988-16">Clear</hd> <p>Students perceived the AI-generated syllabus (<emph>M</emph> = 6.18, <emph>SD</emph> = 0.99) to be clearer than the human-generated syllabus, <emph>M</emph> = 5.41, <emph>SD</emph> = 1.15; <emph>t</emph>(<reflink idref="bib196" id="ref45">196</reflink>) = -5.08, <emph>p</emph> <.001.</p> <hd id="AN0191808988-17">Reasonable in Performance Assessment</hd> <p>Students perceived the AI-generated syllabus (<emph>M</emph> = 5.42, <emph>SD</emph> = 1.27) to be marginally more reasonable in performance assessment than the human-generated syllabus, <emph>M</emph> = 5.09, <emph>SD</emph> = 1.26; <emph>t</emph>(<reflink idref="bib196" id="ref46">196</reflink>) = -1.79, <emph>p</emph> =.075.</p> <hd id="AN0191808988-18">Study 1B: Method</hd> <p>We recruited 240 U.S. participants (<emph>M</emph><subs>Age</subs> = 34.80; 54.2% female) through Prolific, with participants completing the study in exchange for nominal monetary compensation. Employing a two-cell between-subjects design (syllabus authorship: generative AI vs. human), participants were randomly assigned to one of the two conditions.</p> <p>The manipulation and measurement procedures remained consistent with those in Study 1A. In addition, to explore the mediators for the quality perceptions of the syllabi, participants were asked to provide ratings on objectivity ("To what extent do you consider the course syllabus you examined earlier to be objective?"), data-driven insights of the course syllabus ("To what extent do you think the course syllabus you examined earlier offers insights driven by data?"), and novelty ("To what extent do you consider the course syllabus you examined earlier to be novel?"), on a scale from 1 (not at all) to 7 (very much).</p> <hd id="AN0191808988-19">Study 1B: Results</hd> <p>As shown in Table 2, we first conducted a series of independent sample t-tests to examine whether students perceived the AI-generated syllabus to be superior to the human-generated syllabus in terms of the following dimensions: excellent, current, interesting, engaging, clear, and reasonable in performance assessment.</p> <p>Table 2 Syllabus Evaluation by Authorship for Study 1B.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th align="center">Dimensions (<italic>N</italic> = 240)</th><th align="center">AI-generated vs. Human-generated</th><th align="center"><italic>p</italic>-value</th></tr></thead><tbody><tr><td>Excellent</td><td>M = 5.38 (<italic>SD</italic> = 1.06) vs. M = 5.04 (<italic>SD</italic> = 1.12)</td><td><italic>p</italic> =.018<xref ref-type="table-fn" rid="tfn2">**</xref></td></tr><tr><td>Current</td><td>M = 5.26 (<italic>SD</italic> = 0.94) vs. M = 4.89 (<italic>SD</italic> = 1.00)</td><td><italic>p</italic> =.003<xref ref-type="table-fn" rid="tfn2">**</xref></td></tr><tr><td>Interesting</td><td>M = 4.91 (<italic>SD</italic> = 1.39) vs. M = 4.80 (<italic>SD</italic> = 1.43)</td><td><italic>p</italic> =.548</td></tr><tr><td>Engaging</td><td>M = 4.89 (<italic>SD</italic> = 1.36) vs. M = 5.09 (<italic>SD</italic> = 1.45)</td><td><italic>p</italic> =.356</td></tr><tr><td>Clear</td><td>M = 6.18 (<italic>SD</italic> = 1.08) vs. M = 5.76 (<italic>SD</italic> = 1.17)</td><td><italic>p</italic> =.004<xref ref-type="table-fn" rid="tfn2">**</xref></td></tr><tr><td>Reasonable in Assessment</td><td>M = 5.80 (<italic>SD</italic> = 0.99) vs. M = 5.28 (<italic>SD</italic> = 1.23)</td><td><italic>p</italic> <.001<xref ref-type="table-fn" rid="tfn2">**</xref></td></tr><tr><td>Objective</td><td>M = 5.48 (<italic>SD</italic> = 1.12) vs. M = 4.92 (<italic>SD</italic> = 1.30)</td><td><italic>p</italic> <.001<xref ref-type="table-fn" rid="tfn2">**</xref></td></tr><tr><td>Data-Driven Insights</td><td>M = 4.44 (<italic>SD</italic> = 1.34) vs. M = 4.17 (<italic>SD</italic> = 1.45)</td><td><italic>p</italic> =.144</td></tr><tr><td>Novel</td><td>M = 3.21 (<italic>SD</italic> = 1.44) vs. M = 3.62 (<italic>SD</italic> = 1.50)</td><td><italic>p</italic> =.030<xref ref-type="table-fn" rid="tfn2">**</xref></td></tr></tbody></table> </ephtml> </p> <p>2 <emph>p</emph> <.10, **p <.05.</p> <hd id="AN0191808988-20">Excellent</hd> <p>Students perceived the AI-generated syllabus (<emph>M</emph> = 5.38, <emph>SD</emph> = 1.06) to be more excellent than the human-generated syllabus, <emph>M</emph> = 5.04, <emph>SD</emph> = 1.12; <emph>t</emph>(<reflink idref="bib238" id="ref47">238</reflink>) = -2.39, <emph>p</emph> =.018.</p> <hd id="AN0191808988-21">Current</hd> <p>Students perceived the AI-generated syllabus (<emph>M</emph> = 5.26, <emph>SD</emph> = 0.94) to be more current than the human-generated syllabus, <emph>M</emph> = 4.89, <emph>SD</emph> = 1.00; <emph>t</emph>(<reflink idref="bib238" id="ref48">238</reflink>) = -2.96, <emph>p</emph> =.003.</p> <hd id="AN0191808988-22">Interesting</hd> <p>There was no significant effect for syllabus authorship on how interesting the syllabus was between the AI-generated syllabus (<emph>M</emph> = 4.91, <emph>SD</emph> = 1.39) and the human-generated syllabus, <emph>M</emph> = 4.80, <emph>SD</emph> = 1.43; <emph>t</emph>(<reflink idref="bib238" id="ref49">238</reflink>) = -0.60, <emph>p</emph> =.548.</p> <hd id="AN0191808988-23">Engaging</hd> <p>There was no significant effect for syllabus authorship on how engaging the syllabus was between the AI-generated syllabus (<emph>M</emph> = 4.89, <emph>SD</emph> = 1.36) and the human-generated syllabus, <emph>M</emph> = 5.09, <emph>SD</emph> = 1.45; <emph>t</emph>(<reflink idref="bib238" id="ref50">238</reflink>) = 0.93, <emph>p</emph> =.356.</p> <hd id="AN0191808988-24">Clear</hd> <p>Students perceived the AI-generated syllabus (<emph>M</emph> = 6.18, <emph>SD</emph> = 1.08) to be clearer than the human-generated syllabus, <emph>M</emph> = 5.76, <emph>SD</emph> = 1.17; <emph>t</emph>(<reflink idref="bib238" id="ref51">238</reflink>) = -2.91, <emph>p</emph> =.004.</p> <hd id="AN0191808988-25">Reasonable in Performance Assessment</hd> <p>Students perceived the AI-generated syllabus (<emph>M</emph> = 5.80, <emph>SD</emph> = 0.99) to be more reasonable in performance assessment than the human-generated syllabus, <emph>M</emph> = 5.28, <emph>SD</emph> = 1.23; <emph>t</emph>(<reflink idref="bib238" id="ref52">238</reflink>) = -3.65, <emph>p</emph> <.001.</p> <p>Next, we conducted independent samples t-tests for the additional measures, objective, offering data-driven insights, and novel.</p> <hd id="AN0191808988-26">Objective</hd> <p>Students perceived the AI-generated syllabus (<emph>M</emph> = 5.48, <emph>SD</emph> = 1.12) to be more objective than the human-generated syllabus, <emph>M</emph> = 4.92, <emph>SD</emph> = 1.30; <emph>t</emph>(<reflink idref="bib238" id="ref53">238</reflink>) = -3.57, <emph>p</emph> <.001.</p> <hd id="AN0191808988-27">Offering Data-Driven Insights</hd> <p>There was no significant effect for syllabus authorship on how much the syllabus offers data-driven insights between the AI-generated syllabus (<emph>M</emph> = 4.44, <emph>SD</emph> = 1.34) and the human-generated syllabus, <emph>M</emph> = 4.17, <emph>SD</emph> = 1.45; <emph>t</emph>(<reflink idref="bib238" id="ref54">238</reflink>) = -1.47, <emph>p</emph> =.144.</p> <hd id="AN0191808988-28">Novel</hd> <p>Students perceived the AI-generated syllabus (<emph>M</emph> = 3.21, <emph>SD</emph> = 1.44) to be less novel than the human-generated syllabus, <emph>M</emph> = 3.62, <emph>SD</emph> = 1.50; <emph>t</emph>(<reflink idref="bib238" id="ref55">238</reflink>) = -2.18, <emph>p</emph> =.030.</p> <hd id="AN0191808988-29">Mediation Analysis Via Objectivity</hd> <p>To test the mediating role of perceived objectivity, we conducted a mediation analysis with excellence as the dependent variable, syllabus authorship as the independent variable, and perceived objectivity as the meditator (PROCESS model 4; 5,000 bootstrapping; [<reflink idref="bib12" id="ref56">12</reflink>]). As shown in Figure 1, the analysis revealed a significant indirect effect of syllabus authorship on excellence through perceived objectivity (<emph>b</emph> = 0.13, <emph>SE</emph> = 0.05, 95% CI = [.04 to.25]), while the direct effect of syllabus authorship on excellence was not significant (<emph>b</emph> = 0.21, <emph>SE</emph> = 0.14, <emph>p</emph> =.135 95% CI = [-.07 to.49]). We also conducted mediation analyses with other dimensions—current (<emph>b</emph> = 0.13, <emph>SE</emph> = 0.05, 95% CI = [.04 to.23]), interesting (<emph>b</emph> = 0.15, <emph>SE</emph> = 0.06, 95% CI = [.05 to.28]), engaging (<emph>b</emph> = 0.14, <emph>SE</emph> = 0.06, 95% CI = [.04 to.27]), clear (<emph>b</emph> = 0.14, <emph>SE</emph> = 0.05, 95% CI = [.05 to.25]), and reasonable (<emph>b</emph> = 0.23, <emph>SE</emph> = 0.07, 95% CI = [.10 to.38])—and found significant indirect effects for each of these dimensions. These findings support H1 and H2, indicating that perceived objectivity drives the effect of higher quality ratings of an AI-generated syllabus versus a human-generated one.</p> <p>Graph: Figure 1. Perceptions of Superiority of the AI-Generated Syllabus Mediated by Greater Perceived Objectivity (H2).* p <.10, ** p <.05.</p> <hd id="AN0191808988-30">Discussion</hd> <p>The findings of Studies 1A and 1B provide initial support for H1 and H2, indicating that students would perceive an AI-generated syllabus as superior in quality when compared to a human-generated one. Specifically, students rated the AI-generated syllabus more highly in terms of being excellent, current, clear, and reasonable in performance assessment than the human-generated syllabus. However, students rated the AI-generated syllabus as less interesting and engaging compared to the human-generated syllabus. This difference may stem from personal experiences or distinctive teaching approaches embedded within human-generated syllabi.</p> <p>Study 1B replicated the findings of Study 1A and directly evidenced that the perceived strengths of the AI-generated syllabus, particularly its objectivity, were the key mediating factor. Interestingly, participants perceived the human-generated syllabus as more novel than the AI-generated one. We believe this is due to any idiosyncrasies and deviations from the norm, which are likely more prominent in the human-generated syllabus and can be discerned from the syllabus alone. However, this perception of novelty did not mediate other quality assessments. We also eliminated the alternative explanation, which was data-driven insights, in this study.</p> <p>While our hypothesis was tested within the context of a common, conventional course, we have yet to determine if the same preference would hold in different contexts, particularly for a less common, unconventional course. Our next study aims to address this knowledge gap.</p> <hd id="AN0191808988-31">Study 2</hd> <p>Recognizing the need to expand our understanding beyond the conventional course context explored in previous studies, Study 2 tested if quality perceptions may differ for an unconventional course. We hypothesize that in the context of a conventional course, students will perceive the AI-generated syllabus to be superior to the human-generated one. However, in the context of an unconventional course, such perceived excellence is expected to diminish.</p> <hd id="AN0191808988-32">Method</hd> <p>We recruited 475 U.S. participants (<emph>M</emph><subs>Age</subs> = 25.33; 56.0% female) through Prolific, with participants completing the study in exchange for nominal monetary compensation. We employed a 2 (syllabus authorship: AI-generated vs. human-generated) × 2 (course context: conventional vs. unconventional) between-subjects design, and participants were randomly assigned to one of the four conditions.</p> <p>In the conventional course context condition, participants reviewed a syllabus for "Introduction to Marketing" from Studies 1A and 1B, which was either AI-generated or human-generated. However, in the unconventional course context condition, participants reviewed a syllabus for a course titled "Design Thinking in Marketing and Life." This course, offered by a professor affiliated with a major North American University, was designed to acquaint students with the principles of design thinking, which specifically highlights the applications of design thinking in marketing while emphasizing its relevance to the personal lives and professional pursuits of university students. Following the same procedure in Studies 1A and 1B, we created an AI-generated version of this syllabus using ChatGPT, aligning the sections and their order with the human-generated syllabus (for further information, see the Appendix). The measurement procedures remained consistent with those in Study 1A.</p> <hd id="AN0191808988-33">Results</hd> <p>As shown in Table 3, we conducted a series of 2 × 2 ANOVAs, followed by pairwise comparisons within each course context.</p> <p>Table 3. Moderation by Course (Un) Conventionality for Study 2.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th align="center">Dimensions(<italic>N</italic> = 475)</th><th align="center">ConventionalAI vs. human</th><th align="center">UnconventionalAI vs. human</th><th align="center">Interaction effects</th></tr></thead><tbody><tr><td>Excellent</td><td>M = 5.40 vs. M = 4.81(<italic>p</italic> <.001)<xref ref-type="table-fn" rid="tfn3">**</xref></td><td>M = 5.22 vs. M = 5.12(<italic>p</italic> =.487)</td><td><italic>p</italic> =.025<xref ref-type="table-fn" rid="tfn3">**</xref></td></tr><tr><td>Current</td><td>M = 5.20 vs. M = 4.78(<italic>p</italic> =.005)<xref ref-type="table-fn" rid="tfn3">**</xref></td><td>M = 5.14 vs. M = 4.96(<italic>p</italic> =.237)</td><td><italic>p</italic> =.252</td></tr><tr><td>Interesting</td><td>M = 4.74 vs. M = 4.18(<italic>p</italic> =.007)<xref ref-type="table-fn" rid="tfn3">**</xref></td><td>M = 4.74 vs. M = 4.80(<italic>p</italic> =.778)</td><td><italic>p</italic> =.036<xref ref-type="table-fn" rid="tfn3">**</xref></td></tr><tr><td>Engaging</td><td>M = 4.79 vs. M = 4.43(<italic>p</italic> =.074)<xref ref-type="table-fn" rid="tfn3">*</xref></td><td>M = 4.98 vs. M = 5.03(<italic>p</italic> =.799)</td><td><italic>p</italic> =.148</td></tr><tr><td>Clear</td><td>M = 6.21 vs. M = 5.67(<italic>p</italic> <.001)<xref ref-type="table-fn" rid="tfn3">**</xref></td><td>M = 5.84 vs. M = 5.66(<italic>p</italic> =.242)</td><td><italic>p</italic> =.094<xref ref-type="table-fn" rid="tfn3">*</xref></td></tr><tr><td>Reasonable inAssessment</td><td>M = 5.75 vs. M = 5.13(<italic>p</italic> <.001)<xref ref-type="table-fn" rid="tfn3">**</xref></td><td>M = 5.43 vs. M = 5.43(<italic>p</italic> =.988)</td><td><italic>p</italic> =.007<xref ref-type="table-fn" rid="tfn3">**</xref></td></tr></tbody></table> </ephtml> </p> <p>3 <emph>p</emph> <.10, **<emph>p</emph> <.05.</p> <hd id="AN0191808988-34">Excellent</hd> <p>There was a significant interaction effect between syllabus authorship and course context, <emph>F</emph>(<reflink idref="bib1" id="ref57">1</reflink>, 471) = 5.06, <emph>p</emph> =.025; <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><msubsup><mi>η</mi><mi>p</mi><mn>2</mn></msubsup></mrow></math> </ephtml> = 0.01. The main effect of syllabus authorship was significant, <emph>F</emph>(<reflink idref="bib1" id="ref58">1</reflink>, 471) = 10.45, <emph>p</emph> =.001; <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><msubsup><mi>η</mi><mi>p</mi><mn>2</mn></msubsup></mrow></math> </ephtml> = 0.02, but the main effect of course context was not significant, <emph>F</emph>(<reflink idref="bib1" id="ref59">1</reflink>, 471) = 0.32, <emph>p</emph> =.572. As shown in Figure 2, in the conventional course context, the AI-generated syllabus (<emph>M</emph> = 5.40, <emph>SD</emph> = 1.04) was perceived to be significantly more excellent than the human-generated syllabus, <emph>M</emph> = 4.81, <emph>SD</emph> = 1.10; <emph>F</emph>(<reflink idref="bib1" id="ref60">1</reflink>, 471) = 15.00, <emph>p</emph> <.001. However, in the unconventional course context, there was no significant difference between the AI-generated syllabus (<emph>M</emph> = 5.22, <emph>SD</emph> = 1.25) and the human-generated syllabus, <emph>M</emph> = 5.12, <emph>SD</emph> = 1.32; <emph>F</emph>(<reflink idref="bib1" id="ref61">1</reflink>, 471) = 0.48, <emph>p</emph> =.487.</p> <p>Graph: Figure 2. Mean Perceived Excellence. Note. Error bars represent ±1 SE.</p> <hd id="AN0191808988-35">Current</hd> <p>There was no significant interaction effect between syllabus authorship and course context, <emph>F</emph>(<reflink idref="bib1" id="ref62">1</reflink>, 471) = 1.32, <emph>p</emph> =.252. The main effect of syllabus authorship was significant, <emph>F</emph>(<reflink idref="bib1" id="ref63">1</reflink>, 471) = 7.95, <emph>p</emph> =.005; <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><msubsup><mi>η</mi><mi>p</mi><mn>2</mn></msubsup></mrow></math> </ephtml> = 0.02, but the main effect of course context was not significant, <emph>F</emph>(<reflink idref="bib1" id="ref64">1</reflink>, 471) = 0.32, <emph>p</emph> =.570. In the conventional course context, the AI-generated syllabus (<emph>M</emph> = 5.20, <emph>SD</emph> = 1.13) was perceived to be significantly more current than the human-generated syllabus, <emph>M</emph> = 4.78, <emph>SD</emph> = 1.03; <emph>F</emph>(<reflink idref="bib1" id="ref65">1</reflink>, 471) = 7.85, <emph>p</emph> =.005. However, in the unconventional course context, there was no significant difference between the AI-generated syllabus (<emph>M</emph> = 5.14, <emph>SD</emph> = 1.29) and the human-generated syllabus, <emph>M</emph> = 4.96, <emph>SD</emph> = 1.17; <emph>F</emph>(<reflink idref="bib1" id="ref66">1</reflink>, 471) = 1.40, <emph>p</emph> =.237.</p> <hd id="AN0191808988-36">Interesting</hd> <p>There was a significant interaction effect between syllabus authorship and course context, <emph>F</emph>(<reflink idref="bib1" id="ref67">1</reflink>, 471) = 4.44, <emph>p</emph> =.036; <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><msubsup><mi>η</mi><mi>p</mi><mn>2</mn></msubsup></mrow></math> </ephtml> = 0.01. The main effect of syllabus authorship was marginally significant, <emph>F</emph>(<reflink idref="bib1" id="ref68">1</reflink>, 471) = 2.91, <emph>p</emph> =.088; <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><msubsup><mi>η</mi><mi>p</mi><mn>2</mn></msubsup></mrow></math> </ephtml> = 0.01, and the main effect of course context was significant, <emph>F</emph>(<reflink idref="bib1" id="ref69">1</reflink>, 471) = 4.67, <emph>p</emph> =.031; <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><msubsup><mi>η</mi><mi>p</mi><mn>2</mn></msubsup></mrow></math> </ephtml> = 0.01. In the conventional course context, the AI-generated syllabus (<emph>M</emph> = 4.74, <emph>SD</emph> = 1.62) was perceived to be significantly more interesting than the human-generated syllabus, <emph>M</emph> = 4.18, <emph>SD</emph> = 1.49; <emph>F</emph>(<reflink idref="bib1" id="ref70">1</reflink>, 471) = 7.25, <emph>p</emph> =.007. However, in the unconventional course context, there was no significant difference between the AI-generated syllabus (<emph>M</emph> = 4.74, <emph>SD</emph> = 1.72) and the human-generated syllabus, <emph>M</emph> = 4.80, <emph>SD</emph> = 1.50; <emph>F</emph>(<reflink idref="bib1" id="ref71">1</reflink>, 471) = 0.08, <emph>p</emph> =.778.</p> <hd id="AN0191808988-37">Engaging</hd> <p>There was no significant interaction effect between syllabus authorship and course context, <emph>F</emph>(<reflink idref="bib1" id="ref72">1</reflink>, 471) = 2.10, <emph>p</emph> =.148. The main effect of syllabus authorship was not significant, <emph>F</emph>(<reflink idref="bib1" id="ref73">1</reflink>, 471) = 1.19, <emph>p</emph> =.277, but the main effect of course context was significant, <emph>F</emph>(<reflink idref="bib1" id="ref74">1</reflink>, 471) = 8.21, <emph>p</emph> =.004; <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><msubsup><mi>η</mi><mi>p</mi><mn>2</mn></msubsup></mrow></math> </ephtml> = 0.02. In the conventional course context, the AI-generated syllabus (<emph>M</emph> = 4.79, <emph>SD</emph> = 1.53) was perceived to be marginally more engaging than the human-generated syllabus, <emph>M</emph> = 4.43, <emph>SD</emph> = 1.41; <emph>F</emph>(<reflink idref="bib1" id="ref75">1</reflink>, 471) = 3.21, <emph>p</emph> =.074. However, in the unconventional course context, there was no significant difference between the AI-generated syllabus (<emph>M</emph> = 4.98, <emph>SD</emph> = 1.64) and the human-generated syllabus, <emph>M</emph> = 5.03, <emph>SD</emph> = 1.49; <emph>F</emph>(<reflink idref="bib1" id="ref76">1</reflink>, 471) = 0.07, <emph>p</emph> =.799.</p> <hd id="AN0191808988-38">Clear</hd> <p>There was a marginally significant interaction effect between syllabus authorship and course context, <emph>F</emph>(<reflink idref="bib1" id="ref77">1</reflink>, 471) = 2.81, <emph>p</emph> =.094; <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><msubsup><mi>η</mi><mi>p</mi><mn>2</mn></msubsup></mrow></math> </ephtml> = 0.01. The main effect of syllabus authorship was significant, <emph>F</emph>(<reflink idref="bib1" id="ref78">1</reflink>, 471) = 11.09, <emph>p</emph> =.001; <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><msubsup><mi>η</mi><mi>p</mi><mn>2</mn></msubsup></mrow></math> </ephtml> = 0.02, and the main effect of course context was marginally significant, <emph>F</emph>(<reflink idref="bib1" id="ref79">1</reflink>, 471) = 3.18, <emph>p</emph> =.075; <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><msubsup><mi>η</mi><mi>p</mi><mn>2</mn></msubsup></mrow></math> </ephtml> = 0.01. In the conventional course context, the AI-generated syllabus (<emph>M</emph> = 6.21, <emph>SD</emph> = 0.97) was perceived to be significantly clearer than the human-generated syllabus, <emph>M</emph> = 5.67, <emph>SD</emph> = 1.16; <emph>F</emph>(<reflink idref="bib1" id="ref80">1</reflink>, 471) = 12.51, <emph>p</emph> <.001. However, in the unconventional course context, there was no significant difference between the AI-generated syllabus (<emph>M</emph> = 5.84, <emph>SD</emph> = 1.12) and the human-generated syllabus, <emph>M</emph> = 5.66, <emph>SD</emph> = 1.36; <emph>F</emph>(<reflink idref="bib1" id="ref81">1</reflink>, 471) = 1.37, <emph>p</emph> =.242.</p> <hd id="AN0191808988-39">Reasonable in Performance Assessment</hd> <p>There was a significant interaction effect between syllabus authorship and course context, <emph>F</emph>(<reflink idref="bib1" id="ref82">1</reflink>, 471) = 7.32, <emph>p</emph> =.007; <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><msubsup><mi>η</mi><mi>p</mi><mn>2</mn></msubsup></mrow></math> </ephtml> = 0.02. The main effect of syllabus authorship was significant, <emph>F</emph>(<reflink idref="bib1" id="ref83">1</reflink>, 471) = 7.21, <emph>p</emph> =.007; <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><msubsup><mi>η</mi><mi>p</mi><mn>2</mn></msubsup></mrow></math> </ephtml> = 0.02, but the main effect of course context was not significant, <emph>F</emph>(<reflink idref="bib1" id="ref84">1</reflink>, 471) = 0.01, <emph>p</emph> =.916. In the conventional course context, the AI-generated syllabus (<emph>M</emph> = 5.75, <emph>SD</emph> = 1.11) was perceived to be significantly more reasonable in performance assessment than the human-generated syllabus, <emph>M</emph> = 5.13, <emph>SD</emph> = 1.26; <emph>F</emph>(<reflink idref="bib1" id="ref85">1</reflink>, 471) = 14.50, <emph>p</emph> <.001. However, in the unconventional course context, there was no significant difference between the AI-generated syllabus (<emph>M</emph> = 5.43, <emph>SD</emph> = 1.40) and the human-generated syllabus, <emph>M</emph> = 5.43, <emph>SD</emph> = 1.25; <emph>F</emph>(<reflink idref="bib1" id="ref86">1</reflink>, 471) = 0.00, <emph>p</emph> =.988.</p> <hd id="AN0191808988-40">Discussion</hd> <p>Overall, our findings support H3a and H3b, indicating that in the context of conventional courses, students perceive the AI-generated syllabus to be superior in quality to the human-generated syllabus. However, this advantage of generative AI disappears in the context of unconventional courses. These results underscore the importance of contextual factors, specifically the course's degree of conventionality, in influencing perceptions of AI-generated syllabi within educational settings. It is likely that the advantage of objectivity associated with generative AI fades when the criteria for objectivity become less clear in an unconventional context.</p> <p>Throughout our studies so far, we have consistently found higher quality ratings for AI-generated syllabi over human-generated ones in conventional course contexts. However, in these instances, the identity of the syllabus author was not disclosed to participants, a scenario that likely diverges from real-world conditions. Thus, Study 3 aims to explore how disclosing the authorship affects perceptions of quality.</p> <hd id="AN0191808988-41">Study 3</hd> <p>In our studies so far, we have focused on comparing the perceived quality of AI-generated syllabi with human-generated ones without disclosing the authorship information to participants. However, this piece of information might play a role in shaping these perceptions. Thus, Study 3 was designed to explore the potential moderating role of authorship information on quality perceptions. We hypothesize that disclosing the generative AI's authorship of the syllabus will lead to a discounting effect on its perceived quality. Conversely, when the authorship information remains undisclosed, we anticipate that the AI-generated syllabus will be perceived to be superior to the human-generated one, in alignment with our previous studies.</p> <hd id="AN0191808988-42">Method</hd> <p>We recruited 477 U.S. participants (<emph>M</emph><subs>Age</subs> = 38.37; 48.0% female) through Prolific, with participants completing the study in exchange for nominal monetary compensation. We employed a 2 (syllabus authorship: AI-generated vs. human-generated) x 2 (authorship disclosure: undisclosed vs. disclosed) between-subjects design, and participants were randomly assigned to one of the four conditions.</p> <p>In the authorship undisclosed condition, the overall procedures and measurements remained consistent with those in Study 1A. In contrast, the only procedural difference in the authorship disclosed condition was that participants were provided with the authorship information explicitly before reviewing the syllabus.</p> <hd id="AN0191808988-43">Results</hd> <p>As shown in Table 4, we conducted a series of 2 × 2 ANOVAs, followed by pairwise comparisons within each authorship disclosure condition.</p> <p>Table 4. Moderation by Authorship Disclosure for Study 3.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th align="center">Dimensions(<italic>N</italic> = 477)</th><th align="center">UndisclosedAI vs. human</th><th align="center">DisclosedAI vs. human</th><th align="center">Interaction effects</th></tr></thead><tbody><tr><td>Excellent</td><td>M = 5.53 vs. M = 4.91(<italic>p</italic> <.001)<xref ref-type="table-fn" rid="tfn4">**</xref></td><td>M = 5.36 vs. M = 5.09(<italic>p</italic> =.071)</td><td><italic>p</italic> =.078<xref ref-type="table-fn" rid="tfn4">*</xref></td></tr><tr><td>Current</td><td>M = 5.35 vs. M = 4.87(<italic>p</italic> =.001)<xref ref-type="table-fn" rid="tfn4">**</xref></td><td>M = 5.23 vs. M = 4.90(<italic>p</italic> =.021)</td><td><italic>p</italic> =.473</td></tr><tr><td>Interesting</td><td>M = 5.22 vs. M = 4.47(<italic>p</italic> <.001)<xref ref-type="table-fn" rid="tfn4">**</xref></td><td>M = 4.76 vs. M = 4.77(<italic>p</italic> =.947)</td><td><italic>p</italic> =.006<xref ref-type="table-fn" rid="tfn4">**</xref></td></tr><tr><td>Engaging</td><td>M = 5.32 vs. M = 4.85(<italic>p</italic> =.013)<xref ref-type="table-fn" rid="tfn4">**</xref></td><td>M = 4.93 vs. M = 4.98(<italic>p</italic> =.789)</td><td><italic>p</italic> =.051<xref ref-type="table-fn" rid="tfn4">*</xref></td></tr><tr><td>Clear</td><td>M = 6.23 vs. M = 5.57(<italic>p</italic> <.001)<xref ref-type="table-fn" rid="tfn4">**</xref></td><td>M = 5.92 vs. M = 5.71(<italic>p</italic> =.169)</td><td><italic>p</italic> =.044<xref ref-type="table-fn" rid="tfn4">**</xref></td></tr><tr><td>Reasonable inAssessment</td><td>M = 5.70 vs. M = 5.47(<italic>p</italic> =.129)</td><td>M = 5.62 vs. M = 5.42(<italic>p</italic> =.184)</td><td><italic>p</italic> =.887</td></tr></tbody></table> </ephtml> </p> <p>4 <emph>p</emph> <.10, **<emph>p</emph> <.05.</p> <hd id="AN0191808988-44">Excellent</hd> <p>There was a marginally significant interaction effect between syllabus authorship and authorship disclosure, <emph>F</emph>(<reflink idref="bib1" id="ref87">1</reflink>, 473) = 3.12, <emph>p</emph> =.078; <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><msubsup><mi>η</mi><mi>p</mi><mn>2</mn></msubsup></mrow></math> </ephtml> = 0.01. The main effect of syllabus authorship was significant, <emph>F</emph>(<reflink idref="bib1" id="ref88">1</reflink>, 473) = 18.64, <emph>p</emph> <.001; <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><msubsup><mi>η</mi><mi>p</mi><mn>2</mn></msubsup></mrow></math> </ephtml> = 0.04, but the main effect of authorship disclosure was not significant, <emph>F</emph>(<reflink idref="bib1" id="ref89">1</reflink>, 473) = 0.00, <emph>p</emph> =.967. As shown in Figure 3, in the authorship undisclosed condition, the AI-generated syllabus (<emph>M</emph> = 5.53, <emph>SD</emph> = 1.18) was perceived to be significantly more excellent than the human-generated syllabus, <emph>M</emph> = 4.91, <emph>SD</emph> = 1.16; <emph>F</emph>(<reflink idref="bib1" id="ref90">1</reflink>, 473) = 18.39, <emph>p</emph> <.001. However, in the authorship disclosed condition, there was only a marginally significant difference between the AI-generated syllabus (<emph>M</emph> = 5.36, <emph>SD</emph> = 1.04) and the human-generated syllabus, <emph>M</emph> = 5.09, <emph>SD</emph> = 1.12; <emph>F</emph>(<reflink idref="bib1" id="ref91">1</reflink>, 473) = 3.27, <emph>p</emph> =.071.</p> <p>Graph: Figure 3. Mean Perceived Excellence. Note. Error bars represent ±1 SE.</p> <hd id="AN0191808988-45">Current</hd> <p>There was no significant interaction effect between syllabus authorship and authorship disclosure, <emph>F</emph>(<reflink idref="bib1" id="ref92">1</reflink>, 473) = 0.52, <emph>p</emph> =.473. The main effect of syllabus authorship was significant, <emph>F</emph>(<reflink idref="bib1" id="ref93">1</reflink>, 473) = 15.89, <emph>p</emph> <.001; <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><msubsup><mi>η</mi><mi>p</mi><mn>2</mn></msubsup></mrow></math> </ephtml> = 0.03, but the main effect of authorship disclosure was not significant, <emph>F</emph>(<reflink idref="bib1" id="ref94">1</reflink>, 473) = 0.20, <emph>p</emph> =.654. In the authorship undisclosed condition, the AI-generated syllabus (<emph>M</emph> = 5.35, <emph>SD</emph> = 1.21) was perceived to be significantly more current than the human-generated syllabus, <emph>M</emph> = 4.87, <emph>SD</emph> = 1.06; <emph>F</emph>(<reflink idref="bib1" id="ref95">1</reflink>, 473) = 11.00, <emph>p</emph> =.001. In the authorship disclosed condition, there was also a significant difference between the AI-generated syllabus (<emph>M</emph> = 5.23, <emph>SD</emph> = 1.06) and the human-generated syllabus, <emph>M</emph> = 4.90, <emph>SD</emph> = 1.09; <emph>F</emph>(<reflink idref="bib1" id="ref96">1</reflink>, 473) = 5.38, <emph>p</emph> =.021.</p> <hd id="AN0191808988-46">Interesting</hd> <p>There was a significant interaction effect between syllabus authorship and authorship disclosure, <emph>F</emph>(<reflink idref="bib1" id="ref97">1</reflink>, 473) = 7.68, <emph>p</emph> =.006; <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><msubsup><mi>η</mi><mi>p</mi><mn>2</mn></msubsup></mrow></math> </ephtml> = 0.02. The main effect of syllabus authorship was significant, <emph>F</emph>(<reflink idref="bib1" id="ref98">1</reflink>, 473) = 7.17, <emph>p</emph> =.008; <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><msubsup><mi>η</mi><mi>p</mi><mn>2</mn></msubsup></mrow></math> </ephtml> = 0.02, but the main effect of authorship disclosure was not significant, <emph>F</emph>(<reflink idref="bib1" id="ref99">1</reflink>, 473) = 0.31, <emph>p</emph> =.576. In the authorship undisclosed condition, the AI-generated syllabus (<emph>M</emph> = 5.22, <emph>SD</emph> = 1.54) was perceived to be significantly more interesting than the human-generated syllabus, <emph>M</emph> = 4.47, <emph>SD</emph> = 1.54; <emph>F</emph>(<reflink idref="bib1" id="ref100">1</reflink>, 473) = 14.76, <emph>p</emph> <.001. However, in the authorship disclosed condition, there was no significant difference between the AI-generated syllabus (<emph>M</emph> = 4.76, <emph>SD</emph> = 1.43) and the human-generated syllabus, <emph>M</emph> = 4.77, <emph>SD</emph> = 1.48; <emph>F</emph>(<reflink idref="bib1" id="ref101">1</reflink>, 473) = 0.00, <emph>p</emph> =.947.</p> <hd id="AN0191808988-47">Engaging</hd> <p>There was a marginally significant interaction effect between syllabus authorship and authorship disclosure, <emph>F</emph>(<reflink idref="bib1" id="ref102">1</reflink>, 473) = 3.84, <emph>p</emph> =.051; <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><msubsup><mi>η</mi><mi>p</mi><mn>2</mn></msubsup></mrow></math> </ephtml> = 0.01. The main effect of syllabus authorship was not significant, <emph>F</emph>(<reflink idref="bib1" id="ref103">1</reflink>, 473) = 2.50, <emph>p</emph> =.114, and the main effect of authorship disclosure was also not significant, <emph>F</emph>(<reflink idref="bib1" id="ref104">1</reflink>, 473) = 0.95, <emph>p</emph> =.330. In the authorship undisclosed condition, the AI-generated syllabus (<emph>M</emph> = 5.32, <emph>SD</emph> = 1.41) was perceived to be significantly more engaging than the human-generated syllabus, <emph>M</emph> = 4.85, <emph>SD</emph> = 1.42; <emph>F</emph>(<reflink idref="bib1" id="ref105">1</reflink>, 473) = 6.23, <emph>p</emph> =.013. However, in the authorship disclosed condition, there was no significant difference between the AI-generated syllabus (<emph>M</emph> = 4.93, <emph>SD</emph> = 1.44) and the human-generated syllabus, <emph>M</emph> = 4.98, <emph>SD</emph> = 1.43; <emph>F</emph>(<reflink idref="bib1" id="ref106">1</reflink>, 473) = 0.07, <emph>p</emph> =.789.</p> <hd id="AN0191808988-48">Clear</hd> <p>There was a significant interaction effect between syllabus authorship and authorship disclosure, <emph>F</emph>(<reflink idref="bib1" id="ref107">1</reflink>, 473) = 4.09, <emph>p</emph> =.044; <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><msubsup><mi>η</mi><mi>p</mi><mn>2</mn></msubsup></mrow></math> </ephtml> = 0.01. The main effect of syllabus authorship was significant, <emph>F</emph>(<reflink idref="bib1" id="ref108">1</reflink>, 473) = 15.72, <emph>p</emph> <.001; <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><msubsup><mi>η</mi><mi>p</mi><mn>2</mn></msubsup></mrow></math> </ephtml> = 0.03, but the main effect of authorship disclosure was not significant, <emph>F</emph>(<reflink idref="bib1" id="ref109">1</reflink>, 473) = 0.64, <emph>p</emph> =.424. In the authorship undisclosed condition, the AI-generated syllabus (<emph>M</emph> = 6.23, <emph>SD</emph> = 1.04) was perceived to be significantly clearer than the human-generated syllabus, <emph>M</emph> = 5.57, <emph>SD</emph> = 1.23; <emph>F</emph>(<reflink idref="bib1" id="ref110">1</reflink>, 473) = 17.81, <emph>p</emph> <.001. However, in the authorship disclosed condition, there was no significant difference between the AI-generated syllabus (<emph>M</emph> = 5.92, <emph>SD</emph> = 1.22) and the human-generated syllabus, <emph>M</emph> = 5.71, <emph>SD</emph> = 1.25; <emph>F</emph>(<reflink idref="bib1" id="ref111">1</reflink>, 473) = 1.90, <emph>p</emph> =.169.</p> <hd id="AN0191808988-49">Reasonable in Performance Assessment</hd> <p>There was no significant interaction effect between syllabus authorship and authorship disclosure, <emph>F</emph>(<reflink idref="bib1" id="ref112">1</reflink>, 473) = 0.02, <emph>p</emph> =.887. The main effect of syllabus authorship was significant, <emph>F</emph>(<reflink idref="bib1" id="ref113">1</reflink>, 473) = 4.07, <emph>p</emph> =.044; <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><msubsup><mi>η</mi><mi>p</mi><mn>2</mn></msubsup></mrow></math> </ephtml> = 0.01, but the main effect of authorship disclosure was not significant, <emph>F</emph>(<reflink idref="bib1" id="ref114">1</reflink>, 473) = 0.37, <emph>p</emph> =.542. In the authorship undisclosed condition, the AI-generated syllabus (<emph>M</emph> = 5.70, <emph>SD</emph> = 1.25) was not perceived to be significantly more reasonable in performance assessment than the human-generated syllabus, <emph>M</emph> = 5.47, <emph>SD</emph> = 1.11; <emph>F</emph>(<reflink idref="bib1" id="ref115">1</reflink>, 473) = 2.32, <emph>p</emph> =.129. In the authorship disclosed condition, there was also no significant difference between the AI-generated syllabus (<emph>M</emph> = 5.62, <emph>SD</emph> = 1.16) and the human-generated syllabus, <emph>M</emph> = 5.42, <emph>SD</emph> = 1.12; <emph>F</emph>(<reflink idref="bib1" id="ref116">1</reflink>, 473) = 1.77, <emph>p</emph> =.184.</p> <hd id="AN0191808988-50">Discussion</hd> <p>Overall, our findings once again replicated our prior studies that participants perceived the AI-generated syllabus to be superior in quality to the human-generated one when the authorship information was undisclosed. However, consistent with H4a and H4b, when authorship information was disclosed, the perceived quality rating of the AI-generated syllabus was attenuated, most probably due to the general discounting effect of machine-generated content.</p> <hd id="AN0191808988-51">General Discussion</hd> <p>The main objective of our studies was to compare the perceived quality of AI-generated syllabi with that of human-generated ones. In Study 1A, we demonstrated that students rated an AI-generated syllabus higher on various quality dimensions than a human-generated syllabus. In Study 1B, we replicated these findings and explored the underlying psychological mechanisms, revealing that the superior quality perception of the AI-generated syllabus stemmed from its perceived objectivity. We also eliminated the alternative explanations, which are novelty and data-driven insights, in this study. Study 2 investigated the moderating role of course conventionality and found that the AI-generated syllabus was perceived as higher in quality than the human-generated syllabus for a conventional course; however, this perceived superiority diminished for an unconventional course. Finally, Study 3 showed that revealing the authorship of the syllabus diminished the perceived quality advantage of the AI-generated syllabus over the human-generated one.</p> <p>Our research offers valuable theoretical insights into the efficacy of generative AI in the creation of marketing education content. It expands on the ongoing dialogue around the optimal deployment of generative AI ([<reflink idref="bib10" id="ref117">10</reflink>]). The first key theoretical contribution focuses on why AI-generated educational content (specifically, the syllabus in our studies) outperforms human-generated content. We argue and provide evidence that perceived objectivity acts as the psychological mechanism driving this effect, leading to higher perceived excellence. This aligns with previous research on AI-generated texts, which have been shown to be viewed as more objective and credible ([<reflink idref="bib6" id="ref118">6</reflink>]; [<reflink idref="bib9" id="ref119">9</reflink>]). Our findings further validate this process by demonstrating that the advantage is particularly noticeable in conventional courses, where AI-generated syllabi are more likely to be free from the subjective biases and peculiarities that often influence human-generated content. However, in unconventional courses where best practices are not well-established, generative AI can face limitations. This is likely due to a lack of data, resulting in outputs that are neither more objective nor necessarily optimized, especially when optimization criteria are ambiguous.</p> <p>Another significant theoretical contribution of this paper addresses the discounting effect associated with AI-generated educational content, contributing to the ongoing discussions on the potential drawbacks of using generative AI in marketing education ([<reflink idref="bib11" id="ref120">11</reflink>]). While biases against AI-generated content may not always be present (e.g., [<reflink idref="bib9" id="ref121">9</reflink>]), they are particularly evident in the context of syllabus generation for marketing courses. This finding adds an important dimension to the conversation about the limitations of generative AI in education, suggesting that future research could further explore specific situations and contexts where this discounting effect is more or less pronounced, ultimately enriching the overall theoretical framework.</p> <p>For educators, curriculum designers, and policymakers, our findings suggest that generative AI holds significant potential as a tool for refining well-established or foundational courses. It can serve as a valuable benchmark, aiding not only new instructors but also experienced ones seeking to rejuvenate and update their course content. This aligns with the framework of transformative marketing education, suggesting that reevaluating the marketing curriculum could enhance student engagement ([<reflink idref="bib20" id="ref122">20</reflink>]). However, for more innovative and non-traditional courses, it is advisable for educators to incorporate more personal insights and unique touches into their materials.</p> <p>Further, prior studies have emphasized that transparency in content authorship fosters accountability, thereby facilitating effective communication between instructors and students ([<reflink idref="bib14" id="ref123">14</reflink>]). This is particularly significant in the context of syllabus design, where students may lose trust in a course if they are aware that it was designed by AI. Our results suggest that the potential increase in student interest and engagement when content is AI-generated and its authorship is known—either explicitly or implicitly, as would likely be the ethical standard in most educational settings—remains minimal at this point. However, our conclusions are drawn from the study of course syllabi alone, and student perceptions might vary depending on the type of educational content being created.</p> <p>It is also important to acknowledge that authorship exists on a continuum. In practice, content creation often involves a blend of generative AI and human contributions. Whether the discounting effect of AI-generated content persists across this continuum remains an open question. This issue becomes particularly relevant as generative AI is increasingly used in collaboration with human input. Understanding how to balance these human and AI contributions in educational content creation is essential for successfully integrating AI into marketing education. Doing so will ensure that the final product is valuable, trusted, and engaging for students.</p> <p>Another important consideration when incorporating generative AI into marketing education is its ethical implications ([<reflink idref="bib10" id="ref124">10</reflink>]). Research has shown that generative AI can produce biased outputs due to biases present in the datasets on which it is trained ([<reflink idref="bib37" id="ref125">37</reflink>]). These biases may be perpetuated and reinforced through the reproduction of educational materials. Although we did not observe any overt biases in the AI-generated content used in our studies, educators must remain vigilant about this possibility and carefully review all AI-generated content with these concerns in mind.</p> <p>In terms of limitations and future directions, this study evaluates the concept of quality from the perspective of non-experts, specifically students, who are the primary audience for the educational content. While understanding this demographic's perception is important, an evaluation of AI-generated content by subject matter experts would provide valuable insights. Future research should include expert assessments across diverse disciplines to evaluate both content accuracy and pedagogical appropriateness. This would offer a deeper understanding of content quality and its alignment with educational standards.</p> <p>There may also be individual differences among participants that were not fully captured in this study. For example, high-performing students may assess the syllabus differently than low-performing students due to varying levels of engagement or expectations. Future research could address this by segmenting participants based on academic performance, learning styles, or technological proficiency to examine how these characteristics influence perceptions of AI-generated content.</p> <p>Another limitation is the focus on a restricted selection of syllabi, specifically in undergraduate marketing education in North America. To evaluate the generalizability of the findings, future research should explore similar dynamics in different educational settings, such as graduate-level courses, vocational training, and other disciplines beyond marketing. Future studies should also consider international and online learning environments. Investigating AI-generated materials in global, hybrid, or vocational contexts would provide a broader understanding of how educational technology impacts diverse student populations and institutional structures. In addition, researchers could assess whether these findings apply to courses that emphasize creativity or critical thinking over content memorization.</p> <p>Finally, the reliance on lab experiments introduces limitations related to external validity. Although a field study may present logistical challenges, it would provide a more realistic assessment of AI-generated content in actual learning environments. Future research should prioritize field studies in varied educational contexts, including traditional and online courses, to better understand how AI-generated materials affect learning outcomes, student satisfaction, and engagement. Research designs that capture real-time student interaction with AI-generated content, including feedback mechanisms and student–teacher dynamics, would further enhance understanding of its effectiveness in everyday educational practice.</p> <p>In conclusion, our research sheds light on students' perceptions of educational materials produced by generative AI, offering significant contributions to the ongoing discourse on AI's role in marketing education and other fields. The detailed examination of how contextual factors influence the reception of AI-generated content provides essential guidance for educators, curriculum designers, and policymakers. These insights aim to enhance educational methodologies and enrich the learning experience for students, marking a step forward in the thoughtful integration of AI technologies in educational settings.</p> <hd id="AN0191808988-52">Appendix A: Human-Generated Syllabus; Conventional Course</hd> <p></p> <hd id="AN0191808988-53">Introduction to Marketing</hd> <p>[Instructor Name, Email Address]</p> <p>Office Hours: [Days and Times]</p> <hd1 id="AN0191808988-54">Course Description:</hd1> <p>The objective of this course is to help you recognize and respond to business opportunities and problems from a general management perspective. Class sessions will consist of lecture-based and case discussion. Upon successful completion of the course, you will be able to:</p> <p></p> <ulist> <item> Assess whether a marketing program fits with the requirements of the marketplace. This involves determining whether a program appeals to a segment of the market better than competitors' offerings.</item> <p></p> <item> Design an internally consistent marketing program.</item> <p></p> <item> Identify forms of market research that can aid in marketing decision making.</item> </ulist> <p>As an introduction to the field of marketing, this course is designed to provide a conceptual and institutional foundation for the more specialized, and, in some cases, quantitative marketing electives.</p> <hd1 id="AN0191808988-55">Course Outline:</hd1> <p>Week Beginning Topic Chapter</p> <p>Sept 4 <bold> Course Introduction </bold> 1<bold></bold></p> <p> <emph>Part I. Strategic Positioning</emph> </p> <p>September 11 <bold> Consumer Analysis</bold> 2,11</p> <p>Group Presentations (Sept 13)</p> <p>TURN IN: FOCUS GROUP ASSIGNMENT (Sept 13)</p> <p>September 18 <bold> Competitor Analysis</bold> 3</p> <p>Group Presentations (September 20)</p> <p>September 25 <bold>Company Analysis</bold> 4</p> <p>Group Presentations (September 27)</p> <p>TURN IN: CASE ASSIGNMENT (September 27)</p> <p>October 2 <bold> Community Analysis</bold> 5</p> <p>No Class: September 30</p> <p>Group Presentations (October 4)</p> <p> <emph>Part II. Managing Policy</emph> </p> <p>October 9 <bold> Product Policy </bold> 6</p> <p>MIDTERM EXAM (October 9)</p> <p>Group Presentations (October 11)</p> <p>October 16 <bold> Pricing Policy</bold> 7</p> <p>No Class: October 14</p> <p>Positioning Statement</p> <p>Group Presentations (October 18)</p> <p>TURN IN: POSITIONING STATEMENT ASSIGNMENT (October 18)</p> <p>October 23 <bold> Product and Pricing Policy (continued) </bold> 10</p> <p>Guest visitor, group meeting, or optional assignment</p> <p>October 30 <bold>Communications Policy</bold> 8, 11</p> <p>Behind the Scenes Video</p> <p>Group Presentations (November 1)</p> <p>TURN IN: SURVEY ASSIGNMENT</p> <p>November 6 <bold>Distribution Policy</bold> 9</p> <p>Group Presentations (November 8)</p> <p>November 13 READING WEEK</p> <p> <emph>Part III. Marketing Analytics</emph> </p> <p>November 20 <bold>Market Research and Financial Analytics </bold> 11</p> <p>Group Presentations (November 20)</p> <p>MIDTERM EXAM (November 22)</p> <p>November 27 <bold>Presentations</bold></p> <p>December 4 <bold> Presentations</bold> (LAST CLASS: Wednesday, December 6)</p> <hd1 id="AN0191808988-56">Course Text:</hd1> <p>[Textbook Title] by (Author[s]), [Publisher], [ISBN Number)</p> <hd1 id="AN0191808988-57">Evaluation:</hd1> <p>Midterm 1 25%</p> <p>Midterm 2 25%</p> <p>Group Work</p> <p>Group Presentation 10%</p> <p>Case Assignment 5%</p> <p>Focus Group Assignment 5%</p> <p>Positioning Statement Exercise 5%</p> <p>Survey Assignment 5%</p> <p>Advertising Exercise 20%</p> <p>Class participation will count in borderline cases.</p> <hd1 id="AN0191808988-58">Case Write-Ups:</hd1> <p>Except for the Case Assignment, you may also turn in write-ups of cases discussed in class for feedback purposes without an assigned grade. A suggested format for such write-ups is given on page 8 of <emph>The Marketing Paradigm</emph>.</p> <hd1 id="AN0191808988-59">Class Participation:</hd1> <p>You are expected to prepare for and behave in class as you would in a work environment. Come prepared and always try to contribute to our group activity in a professional and cordial manner. Vigorous class participation can bring a case or lecture to life. You may be surprised at how much you learn from each other.</p> <p>Class participation is an integral part of our planned activities, particularly for discussion of cases. For cases, you should be ready to succinctly present your recommendations. If we are analyzing someone else's recommendations, find ways of commenting that will contribute to the class' understanding of the case situation. For cases or discussion of lectures, many kinds of useful comments are possible: Tactfully presenting a differing opinion is perhaps the most common form, although not by any means the only useful form. Formulating a question, pointing out relevant parts of a case situation, describing a related business experience, and even introducing relevant humor are other forms of participation. Perhaps the simplest standard for class participation is to try to behave as you would want coworkers to behave. While each person must develop his or her own style of participating, you should avoid the extremes of never participating or monopolizing the discussion (particularly if the line of discussion is not particularly enlightening). After all, what employer would pay a worker either for never contributing or for wasting other people's time? Operationally, you should try to comment at least once every two class sessions. If you start to notice, after three or four comments on a given day, that your classmates or the instructor are becoming annoyed with you, that may be a signal that you are monopolizing the discussion.</p> <p>Please observe emerging academic standards regarding digital media. This includes turning your cell phone off before class. This also includes not browsing the web during or doing unrelated work on a laptop during class. Laptops, of course, are suitable for taking notes and actively adding facts to the class discussion. The difference between desired uses, which enhance class discussion, and "transporting oneself virtually elsewhere" is obvious and distracting to those nearby. This will be noted in the class participation mark. Again, a general standard of behavior is to think of the class time as an active work meeting.</p> <hd1 id="AN0191808988-60">Study Teams:</hd1> <p>You will be asked to form study teams of four or five members. These are the people with whom you will be performing several of the exercises. The learning experience may be enhanced if the teams have one or two quantitatively oriented people and one or two non-quantitatively oriented people. To "spread the wealth" I suggest that no study team have more than two people that have worked in marketing, and, if possible, that each study team have at least one and no more than two people who come from countries other than Canada. At the end of the course, you will be asked to evaluate your other study team members to account for the possibility that some members are contributing less work than others (most groups give equal weight to all members).</p> <hd1 id="AN0191808988-61">Exams:</hd1> <p>The midterms and final will consist of short-answer and essay questions. If you have a conflict for one of the exams, please discuss this with me before the exam to try to reschedule for a time earlier than the scheduled time (we cannot reschedule an exam for a date later than the scheduled time). If a student has not rescheduled and does not show up for the exam, a non-passing grade will be recorded, except in documented emergencies.</p> <hd id="AN0191808988-62">Appendix B: AI-Generated Syllabus; Conventional Course</hd> <p>Generative AI used: ChatGPT 3.5</p> <p>Prompt #1: I am a business school professor. Please suggest a 13-session syllabus for an introductory course in marketing, designed for undergraduate business students. Now, using the above information, please create a complete syllabus including all the components of a good syllabus.</p> <p>Prompt #2: Please elaborate on the "Grading" section.</p> <hd id="AN0191808988-63">Introduction to Marketing</hd> <p> <bold>Instructor:</bold> [Instructor Name]</p> <p> <bold>Contact Information:</bold> [Email Address]</p> <p> <bold>Office Hours:</bold> [Days and Times]</p> <hd1 id="AN0191808988-64">Course Overview:</hd1> <p>This course provides students with a foundational understanding of marketing concepts, strategies, and techniques. It explores the fundamental principles of marketing and their application in contemporary business environments. Through lectures, discussions, case studies, and projects, students will develop a solid foundation in marketing knowledge and skills.</p> <hd1 id="AN0191808988-65">Course Objectives:</hd1> <p>By the end of the course, students should be able to:</p> <p></p> <ulist> <item> Define key marketing concepts and terminology.</item> <p></p> <item> Understand the role of marketing in business and society.</item> <p></p> <item> Analyze consumer behavior and decision-making processes.</item> <p></p> <item> Formulate basic marketing strategies and plans.</item> <p></p> <item> Demonstrate knowledge of product, pricing, distribution, and promotion strategies.</item> <p></p> <item> Apply ethical and social responsibility considerations to marketing decisions.</item> <p></p> <item> Evaluate marketing success through relevant metrics and measurements.</item> </ulist> <hd1 id="AN0191808988-66">Session-by-Session Outline:</hd1> <hd1 id="AN0191808988-67">Session 1 (September 6): Introduction to Marketing</hd1> <p></p> <ulist> <item> - Course overview and expectations.</item> <p></p> <item> - Importance of marketing in business.</item> <p></p> <item> - Core marketing concepts and functions.</item> </ulist> <hd1 id="AN0191808988-68">Session 2 (September 13): Understanding Consumer Behavior</hd1> <p></p> <ulist> <item> - Factors influencing consumer behavior.</item> <p></p> <item> - Consumer decision-making process.</item> <p></p> <item> - Market segmentation and targeting.</item> </ulist> <hd1 id="AN0191808988-69">Session 3 (September 20): Market Research and Analysis</hd1> <p></p> <ulist> <item> - Role of market research.</item> <p></p> <item> - Primary vs. secondary research.</item> <p></p> <item> - Collecting and analyzing data.</item> </ulist> <hd1 id="AN0191808988-70">Session 4 (September 27): Product and Brand Management</hd1> <p></p> <ulist> <item> - Product life cycle and new product development.</item> <p></p> <item> - Branding strategies and brand equity.</item> <p></p> <item> - Packaging and labeling considerations.</item> </ulist> <hd1 id="AN0191808988-71">Session 5 (October 4): Pricing Strategies</hd1> <p></p> <ulist> <item> - Pricing objectives and methods.</item> <p></p> <item> - Pricing strategies (e.g., skimming, penetration, and value-based).</item> <p></p> <item> - Psychological pricing and discounting.</item> </ulist> <hd1 id="AN0191808988-72">Session 6 (October 11): Distribution and Channel Management</hd1> <p></p> <ulist> <item> - Importance of distribution channels.</item> <p></p> <item> - Types of distribution channels.</item> <p></p> <item> - Retailing and e-commerce trends.</item> </ulist> <hd1 id="AN0191808988-73">Session 7 (October 18): Integrated Marketing Communication</hd1> <p></p> <ulist> <item> - Elements of the promotional mix.</item> <p></p> <item> - Advertising, personal selling, public relations, and sales promotion.</item> <p></p> <item> - Creating effective marketing messages.</item> </ulist> <hd1 id="AN0191808988-74">Session 8 (October 25): Digital Marketing and Social Media</hd1> <p></p> <ulist> <item> - Overview of digital marketing.</item> <p></p> <item> - Social media platforms and strategies.</item> <p></p> <item> - Search engine optimization (SEO) and online advertising.</item> </ulist> <hd1 id="AN0191808988-75">Session 9 (November 8): Marketing Ethics and Social Responsibility</hd1> <p></p> <ulist> <item> - Ethical issues in marketing.</item> <p></p> <item> - Corporate social responsibility (CSR) in marketing.</item> <p></p> <item> - Balancing profits and societal impact.</item> </ulist> <hd1 id="AN0191808988-76">Session 10 (November 15): Midterm Review and Exam</hd1> <p></p> <ulist> <item> - Review of key concepts covered in the first half of the course.</item> <p></p> <item> - Midterm exam.</item> </ulist> <hd1 id="AN0191808988-77">Session 11 (November 22): Marketing Strategy and Planning</hd1> <p></p> <ulist> <item> - Developing a marketing plan.</item> <p></p> <item> - SWOT analysis and competitive positioning.</item> <p></p> <item> - Setting marketing objectives and budgets.</item> </ulist> <hd1 id="AN0191808988-78">Session 12 (November 29): International and Global Marketing</hd1> <p></p> <ulist> <item> - Opportunities and challenges in global markets.</item> <p></p> <item> - Adapting marketing strategies for international audiences.</item> <p></p> <item> - Cultural considerations in marketing.</item> </ulist> <hd1 id="AN0191808988-79">Session 13 (December 6): Final Project Presentations and Conclusion</hd1> <p></p> <ulist> <item> - Group project presentations.</item> <p></p> <item> - Reflecting on the course and key takeaways.</item> <p></p> <item> - Final exam review.</item> </ulist> <hd1 id="AN0191808988-80">Required Textbook:</hd1> <p>[Textbook Title] by [Author(s) ]</p> <p>Publisher: [Publisher]</p> <p>ISBN: [ISBN Number]</p> <hd1 id="AN0191808988-81">Grading:</hd1> <p></p> <ulist> <item> - Attendance and Participation: 10%</item> <p></p> <item> - Quizzes and Homework: 20%</item> <p></p> <item> - Group Project: 30%</item> <p></p> <item> - Midterm Exam: 20%</item> <p></p> <item> - Final Exam: 20%</item> </ulist> <p>Attendance and Participation (10%): Attendance and active participation are essential for a comprehensive learning experience. Students are expected to attend all sessions and contribute to class discussions, group activities, and case analyses. Participation includes asking questions, sharing insights, and engaging in discussions that demonstrate an understanding of the material.</p> <p>Quizzes and Homework (20%): Throughout the course, there will be quizzes to assess students' understanding of key concepts and readings. In addition, homework assignments will be assigned to reinforce learning and encourage critical thinking. Quizzes may be both in-class and take-home, and the homework assignments may involve practical applications of marketing principles.</p> <p>Group Project (30%): This project involves a group-based marketing analysis and plan for a real or hypothetical product/service. The project will require students to apply their knowledge to develop a comprehensive marketing strategy, including market research, segmentation, targeting, positioning, and promotional tactics. The project will be evaluated based on creativity, feasibility, and the depth of marketing analysis.</p> <p>Midterm Exam (20%): The midterm exam will cover material from the first half of the course, including core concepts, consumer behavior, market research, product and brand management, pricing strategies, distribution, and integrated marketing communication. The exam will consist of a combination of multiple-choice questions, short-answer questions, and possibly a case study.</p> <p>Final Exam (20%): The final exam will be comprehensive, covering all topics studied throughout the course, including digital marketing, ethics, international marketing, and marketing strategy. It will assess students' understanding of concepts, ability to apply them, and critical thinking skills. Similar to the midterm, the final exam may include various question formats.</p> <hd1 id="AN0191808988-82">Grading Policies:</hd1> <p>Late Assignments: Late assignments will be subject to a deduction of points based on the delay. Late assignments will be accepted up to 48 hours after the due date, with a maximum deduction of 20% of the assignment's total points.</p> <p>Make-up Exams: Make-up exams will only be considered in cases of documented medical or personal emergencies. Students must provide valid documentation and arrange for a make-up within 1 week of the missed exam.</p> <p>Academic Integrity: Plagiarism and cheating are serious offenses and will result in academic penalties. Proper citation and referencing are expected for all sources used. Any instance of academic dishonesty will be reported according to the university's academic integrity policy.</p> <p>Feedback and Grades: Assignments and exams will be returned with constructive feedback to aid in understanding strengths and areas for improvement. Grades will be updated on the course platform, and students are encouraged to seek clarification on their performance during office hours.</p> <hd id="AN0191808988-83">Appendix C: Human-Generated Syllabus; Unconventional Course</hd> <p></p> <hd id="AN0191808988-84">MKT 298 – Design Thinking in Marketing and Life Seminar</hd> <p></p> <hd id="AN0191808988-85">Course Description</hd> <p>Design thinking adopts human-centered methodology to understand the market, product, consumer, and people themselves. It is a process that helps marketers understand what their customers need and how to create innovative solutions for their problems. It is also a process that helps people understand themselves and how to create innovative plan for their lives. This course introduces design thinking, how it applies in marketing, and how it applies to college students' life and work. It'll train students to creatively apply design-thinking approach in marketing area, and design 4 years college and future work and life based on their own interests, motivation, and who they are. It'll also help them establish their own brands and unique values in the job market.</p> <p>Design and innovation are foundational skills in Product Management, Entrepreneurship and Intrapreneurship and are also key to living a successful, fulfilling life for students in all disciplines. This course applies the basic skills of Design Thinking to inspire innovation for all SCSU students when making life decisions on campus and beyond. Design Thinking employs a three-stage process for developing solutions to the wicked problem of designing your life: Empathize, Ideate, and Build. In this class, you apply these steps to developing solutions for building a well-lived and joyful life.</p> <p>This class is inspired by the best-selling book, <emph>"Designing Your Life: How to Build a Well-Lived, Joyful Life,"</emph> by Bill Burnett and Dave Evans from Stanford University.</p> <p>Course Objectives</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /></colgroup><thead><tr><th align="center">Course Objectives:<break />Upon completion of the course,<break />the student will be able to</th><th align="center">Assessments</th></tr></thead><tbody><tr><td><p>1. Develop competency in Design Thinking tools and methods for generative brainstorming and ideation applicable to life design</p></td><td><p>Discussion questions<break />Market View, Work View, Life View<break />Design Journal</p></td></tr><tr><td><p>2. Develop competency in using Design Thinking approach and techniques to prototype experiences relevant to personal growth, professional networking, market research, and career exploration.</p></td><td><p>Discussion questions<break />Complete 2 prototypes</p></td></tr><tr><td><p>3. Harness the power of radical collaboration through design teams</p></td><td><p>Discussion questions<break />Prototyping Creation</p></td></tr><tr><td><p>4. Apply proven storytelling techniques to persuade and influence others</p></td><td><p>Discussion questions<break />Showcase "My Future Self"</p></td></tr><tr><td><p>5. Integrate their career experiences with their own vocational vision.</p></td><td><p>Market View, Work View, Life View<break />Discussion questions<break />3 Odyssey Plans</p></td></tr><tr><td><p>6. Develop a strong supportive network of instructors and fellow students to help you achieve your goals.</p></td><td><p>Discussion questions<break />Prototype Creation</p></td></tr></tbody></table> </ephtml> </p> <hd1 id="AN0191808988-86">Course Materials</hd1> <p>Burnett, W. & Evans, D. (2016). Designing your life: How to build a well-lived, joyful life. Knopf: New York, NY.</p> <p>Burnett, W. & Evans, D. (2020). Designing your work life: How to thrive and change and find happiness at work. Knopf: New York, NY.</p> <hd1 id="AN0191808988-87">Course Assignments</hd1> <p></p> <ulist> <item> Post on discussion board: submit 4 to 7 posts on the Blackboard.</item> <p></p> <item> Develop <emph>Marketing View, College View, Work View, and Life View</emph> statements: write 2 to 3 short paragraphs for each of these views.</item> <p></p> <item> Maintain a Design Journal: maintain a log of design ideas, visual explanations, stories, and reflections.</item> <p></p> <item> Develop Odyssey Plans: create three visual plans of what your life will look like over the next 5 years.</item> <p></p> <item> Develop two Prototypes: conduct design interviews or participate in experiences that will inform your decisions.</item> <p></p> <item> Showcase "My Future Self": present an integrated life design project, including three Odyssey Plans and prototype outcomes.</item> </ulist> <hd1 id="AN0191808988-88">Grading</hd1> <p>Design Journal 20%</p> <p>BB/Miro Discussion participation 30%</p> <p>Create 3 Odyssey plans 10%</p> <p>Complete 2 prototypes 10%</p> <p>Final Presentation 15%</p> <p>DTL Activities Completion 15%</p> <p>Total 100%</p> <p></p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th align="left" colspan="4">Excellent</th><th align="center" colspan="2">Above Average</th><th align="center" colspan="2">Average</th><th align="center">Below Average</th><th align="center" colspan="2">Unsatisfactory</th></tr></thead><tbody><tr><td><p>Level</p></td><td><p>A</p></td><td><p>A-</p></td><td><p>B+</p></td><td><p>B</p></td><td><p>B-</p></td><td><p>C+</p></td><td><p>C</p></td><td><p>C-</p></td><td><p>D</p></td><td><p>F</p></td></tr><tr><td><p>%</p></td><td><p>100-93</p></td><td><p>92.9-90</p></td><td><p>89.9-87</p></td><td><p>86.9-83</p></td><td><p>82.9-80</p></td><td><p>79.9-77</p></td><td><p>76.9-73</p></td><td><p>72.9-70</p></td><td><p>69.9-60</p></td><td><p>Below 60</p></td></tr></tbody></table> </ephtml> </p> <p></p> <ulist> <item> Requirements: full class participation (1 excused non-emergent absence), homework assignments.</item> <p></p> <item> NOTE: Regular participation is critical to course success. Since much of the most important work is done in section groups, class absences affect not only the individual but also the entire section group as it maintains an ongoing and cumulative conversation.</item> </ulist> <hd1 id="AN0191808988-89">Recommended Reading</hd1> <p></p> <ulist> <item> The Design of Everyday Things by Donald A. Norman</item> <p></p> <item> Authentic Happiness: Using the New Positive Psychology to Realize Your Potential for Lasting Fulfillment by Martin Seligman</item> <p></p> <item> Big Questions, Worthy Dreams: Mentoring Young Adults in Their Search for Meaning, Purpose, and Faith by Sharon Daloz Parks</item> <p></p> <item> Cool Careers for Dummies by Marty Nemko</item> <p></p> <item> Finding a Job You Can Love by Ralph T. Mattson and Arthur F. Miller</item> <p></p> <item> Finite and Infinite Games by James P. Carse</item> <p></p> <item> Flow—the Psychology of Optimal Experience by Mihalyi Csikszentmihalyi</item> <p></p> <item> Good Work by E.F. Schumacher</item> <p></p> <item> Habits of the Heart: Individualism and Commitment in American Life by Robert N. Bellah, Richard Madsen, William M. Sullivan, and Ann Swidler</item> <p></p> <item> "Ode to Things" from Odes to Common Things, Bilingual Edition (Hardcover), by Pablo Neruda (Author), Ferris Cook (Illustrator), Ken Krabbenhoft</item> <p></p> <item> Still Life With Oysters and Lemon: On Objects and Intimacy by Mark Doty</item> <p></p> <item> Transitions: Making Sense of Life's Changes by William Bridges</item> <p></p> <item> Walden by Henry David Thoreau</item> <p></p> <item> What Color is Your Parachute 2009: A Practical Manual for Job-Hunters and Career-Changers, by Richard Bolles</item> <p></p> <item> What Should I Do with My Life? The True Story of People Who Answered the Ultimate Question, by Po Bronson</item> <p></p> <item> Working: People Talk About What They Do All Day and How They Feel About What They Do, by Studs Terkel</item> <p></p> <item> Crowdsourcing: Why the Power of the Crowd is Driving the Future of Business by Jeff Howe</item> <p></p> <item> The Odyssey Years, David Brooks, article, October 9, 2007. <emph>New York Times</emph></item> </ulist> <p>Course Outline and Tentative Schedule</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th align="center">Week</th><th align="center">Activities</th><th align="center">Homework</th></tr></thead><tbody><tr><td><p>Week 1</p></td><td><p>• Welcome to DYML<break />• Introduction<break />• Mini design thinking sprint<break />• Using design thinking process in marketing and life</p></td><td><p>Recommended read DYL: Intro, Ch. 1, 11<break />Take the test at: <ext-link ext-link-type="url" href="http://www.16personalities.com" title="www.16personalities.com" /><break />Predictive Index<break />Reflect on the test outcome</p></td></tr><tr><td><p>Week 2</p></td><td><p>• The wicked problem of figuring out your future<break />• Empathy in design thinking<break />• Why are you in college?<break />• Reframing misconceptions that impede life design<break />• Marketing View, Life and Work View</p></td><td><p>Recommended read DYL: Ch. 2</p></td></tr><tr><td><p>Week 3</p></td><td><p>• Empathy for product design and life design<break />• Share and discuss Marketing View, Life and Work View<break />• Visual thinking for problem-solving<break />• Visualizing your engagement moments<break />• Design Journal</p></td><td><p>Start your Design Journal and submit images from it.<break />Recommended read DYL: Ch. 4 & 5<break />Start good time journal<break />Create 3 possible career mind maps</p></td></tr><tr><td><p>Week 4</p></td><td><p>• Design interviews for wayfinding (student entrepreneur as guest)<break />• Practice design interviews with individual group members<break />• Getting unstuck: Mind-mapping and brainstorming for new experiences and career options</p></td><td><p>Continue with Design Journal and submit images from it.<break />Interview a senior or working professional</p></td></tr><tr><td><p>Week 5</p></td><td><p>• Asking the right questions<break />• Identifying alternative paths panel discussion</p></td><td><p>Continue with Design Journal.<break />Reflection paper on panel discussion.<break />Research the Career Center Website, Advising First or student groups and identify resources that interests you most</p></td></tr><tr><td><p>Week 6</p></td><td><p>• Share outcomes of design interviews<break />• Reflect on major findings from your Design Journal<break />• Designing your way forward</p></td><td><p>Create an Odyssey Plan and identify resources needed to accomplish it<break />Make an appointment for an interview with a professor in a major you or a working professional</p></td></tr><tr><td><p>Week 7</p></td><td><p>• 3 Odysseys<break />• Decision-Making</p></td><td /></tr><tr><td><p>Week 8</p></td><td><p>• Midterm work: Design Journal<break />• 3 Odysseys<break />• Take a Break and Self-care</p></td><td><p>No Class</p></td></tr><tr><td><p>Week 9</p></td><td><p>• Share prototype ideas and get feedback<break />• Guest speaker</p></td><td><p>Read: "How to get the most out of college"—Bruni<break />Have prototype conversations with instructor or professor about their major choices and outcomes</p></td></tr><tr><td><p>Week 10</p></td><td><p>• Share prototype conversation<break />• Refine prototype<break />• Resources on campus: enriching your life</p></td><td><p>Identify areas in your life to improve upon and resources on campus to support these<break />Have prototype conversations with instructor or professor about their major choices and outcomes</p></td></tr><tr><td><p>Week 11</p></td><td><p>• What's your future story<break />• Design Thinking your way forward</p></td><td /></tr><tr><td /><td><p>• Share Odyssey Plans<break />• Brainstorm prototype ideas for all three plans<break />• Guest speaker</p></td><td><p>Have prototype experience or conversations and reflect on major findings</p></td></tr><tr><td><p>Week 12</p></td><td><p>• Share prototype experiences<break />• Revisit prototype ideas<break />• Finding the Unicorn: discovering hidden opportunities<break />• Guest speaker on networking</p></td><td><p>Have prototype experience or interview for prototype<break />Identify strategies to find the unicorn in your company of interest</p></td></tr><tr><td><p>Week 13</p></td><td><p>• Share prototype experiences<break />• 4-step decision making process</p></td><td><p>Prepare for final presentation</p></td></tr><tr><td><p>Week 14</p></td><td><p>• Using stories to contextualize experiences<break />• Using stories to create new opportunities<break />• Collecting stories for compelling presentations</p></td><td><p>Prepare "Meet My Future Selves" presentation</p></td></tr><tr><td><p>Week 15</p></td><td><p>• "Meet My Future Selves" showcase<break />• Products to Design/Change Presentation</p></td><td /></tr><tr><td><p>Week 16</p></td><td><p>Final Reflection</p></td><td /></tr></tbody></table> </ephtml> </p> <hd id="AN0191808988-90">Appendix D: AI-Generated Syllabus; Unconventional Course</hd> <p>Generative AI used: ChatGPT 3.5</p> <p>Prompt #1: I am a marketing professor at a business school. Please create a detailed syllabus for a new course titled "design thinking in marketing and life," with an emphasis on "life," designed for upper year undergraduate business students (it is to be a seminar course). Please create a syllabus, using the following section headings:</p> <p>course title</p> <p>course description (at least two paragraphs with details)</p> <p>course objectives (create a table with course objectives and assessments as the two columns)</p> <p>course materials</p> <p>course assignments</p> <p>grading (offer percentage breakdown of assessment components, as well as a table for the letter grades)</p> <p>recommended reading (offer at least 15 mostly books and some articles)</p> <p>course outline and tentative schedule (create a weekly table for 16 weeks with week, activities, and homework as the columns; include sufficient details in activities and homework columns)</p> <p>Prompt #2: Complete the last two sections.</p> <p>Prompt #3: Give more details for each week's activities and homework using bullet points.</p> <hd id="AN0191808988-91">MKT 298 – Design Thinking in Marketing and Life Seminar</hd> <p></p> <hd id="AN0191808988-92">Course Description</hd> <p>This course explores the application of design thinking principles to marketing practices, with a strong emphasis on its applicability in various life scenarios. Design thinking, traditionally a user-centric approach for problem-solving and innovation, is broadened in scope to equip students with the ability to navigate complex marketing challenges as well as personal and societal issues. By delving into a variety of case studies, interactive seminars, and hands-on projects, students will not only appreciate the intersectionality of design thinking, marketing, and life but also cultivate a mind-set of continuous learning and reflection.</p> <p>The seminar-style course encourages an environment of active participation and discussion among students. Each session is structured to provoke critical thinking, challenge conventional marketing paradigms, and inspire a new way of approaching life's uncertainties. As future marketers and leaders, students will learn to embrace a holistic and empathetic approach toward understanding consumer behavior, developing marketing strategies, and making life decisions.</p> <p>Course Objectives</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /></colgroup><thead><tr><th align="center">Course Objectives</th><th align="center">Assessments</th></tr></thead><tbody><tr><td><p>Understand the core principles of design and its application in marketing and life scenarios</p></td><td><p>Class Participation, Case Analysis Reports</p></td></tr><tr><td><p>Develop problem-solving and critical thinking skills through the lens of design thinking</p></td><td><p>Individual and Group Projects, Reflection Essays</p></td></tr><tr><td><p>Foster a culture of continuous learning, reflection, and personal and professional development</p></td><td><p>Reflection Essays, Class Discussions</p></td></tr><tr><td><p>Engage in collaborative learning and cross-disciplinary thinking</p></td><td><p>Group Projects, Class Discussions</p></td></tr><tr><td><p>Apply design thinking methodologies to real-world marketing problems and personal life challenges</p></td><td><p>Case Analysis Reports, Group Projects</p></td></tr></tbody></table> </ephtml> </p> <hd1 id="AN0191808988-93">Course Materials</hd1> <p></p> <ulist> <item> Brown, Tim. "Change by Design: How Design Thinking Transforms Organizations and Inspires Innovation."</item> <p></p> <item> Kelley, David, and Tom Kelley. "Creative Confidence: Unleashing the Creative Potential Within Us All."</item> <p></p> <item> A variety of academic articles, case studies, and multimedia materials provided throughout the course.</item> </ulist> <hd1 id="AN0191808988-94">Course Assignments</hd1> <p></p> <ulist> <item> Weekly Reflection Essays</item> <p></p> <item> Case Analysis Reports</item> <p></p> <item> Group Projects</item> <p></p> <item> Final Presentation</item> </ulist> <hd1 id="AN0191808988-95">Grading</hd1> <p>Assessment Components</p> <p></p> <ulist> <item> Class Participation: 20%</item> <p></p> <item> Weekly Reflection Essays: 20%</item> <p></p> <item> Case Analysis Reports: 20%</item> <p></p> <item> Group Projects: 25%</item> <p></p> <item> Final Presentation: 15%</item> <p></p> </ulist> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /></colgroup><thead><tr><th align="center">Percentage</th><th align="center">Grade</th></tr></thead><tbody><tr><td><p>90-100</p></td><td><p>A</p></td></tr><tr><td><p>80-89</p></td><td><p>B</p></td></tr><tr><td><p>70-79</p></td><td><p>C</p></td></tr><tr><td><p>60-69</p></td><td><p>D</p></td></tr><tr><td><p>0-59</p></td><td><p>F</p></td></tr></tbody></table> </ephtml> </p> <hd1 id="AN0191808988-96">Recommended Reading</hd1> <p></p> <ulist> <item> Martin, Roger L. "The Design of Business: Why Design Thinking is the Next Competitive Advantage."</item> <p></p> <item> Kolko, Jon. "Well-Designed: How to Use Empathy to Create Products People Love."</item> <p></p> <item> Ries, Eric. "The Lean Startup: How Today's Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses."</item> <p></p> <item> Brown, Tim. "Design Thinking: A Manual for Innovation."</item> <p></p> <item> Liedtka, Jeanne, and Tim Ogilvie. "Designing for Growth: A Design Thinking Tool Kit for Managers."</item> <p></p> <item> Kelley, Tom, and Jonathan Littman. "The Art of Innovation: Lessons in Creativity from IDEO, America's Leading Design Firm."</item> <p></p> <item> Moore, Geoffrey A. "Crossing the Chasm: Marketing and Selling High-Tech Products to Mainstream Customers."</item> <p></p> <item> Tondreau, Beth. "Layout Essentials: 100 Design Principles for Using Grids."</item> <p></p> <item> Silberman, Mel. "The Handbook of Experiential Learning."</item> <p></p> <item> Sutton, Robert I., and Huggy Rao. "Scaling Up Excellence: Getting to More Without Settling for Less."</item> <p></p> <item> Sinek, Simon. "Start With Why: How Great Leaders Inspire Everyone to Take Action."</item> <p></p> <item> Pink, Daniel H. "Drive: The Surprising Truth About What Motivates Us."</item> <p></p> <item> Duhigg, Charles. "The Power of Habit: Why We Do What We Do in Life and Business."</item> <p></p> <item> Coyle, Daniel. "The Culture Code: The Secrets of Highly Successful Groups."</item> <p></p> <item> Grant, Adam. "Originals: How Non-Conformists Move the World."</item> <p></p> <item> Article: Brown, Tim, and Jocelyn Wyatt. "Design Thinking for Social Innovation." Stanford Social Innovation Review.</item> <p></p> <item> Article: Liedtka, Jeanne. "Why Design Thinking Works." Harvard Business Review.</item> </ulist> <p>Course Outline and Tentative Schedule</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th align="center">Week</th><th align="center">Activities</th><th align="center">Homework</th></tr></thead><tbody><tr><td><p>1</p></td><td><p>- Introduction to Design Thinking: Principles and Processes<break />- Overview of course objectives and expectations<break />- Introduction to seminar discussion protocols</p></td><td><p>- Reading: Chapters 1-2 of "Change by Design"</p></td></tr><tr><td><p>2</p></td><td><p>- Exploration: Market and Life Challenges through the Lens of Design Thinking<break />- Group discussion on common challenges in marketing and life<break />- Guest speaker (if available)</p></td><td><p>- Case Analysis: Choose a market or life challenge and apply design thinking principles</p></td></tr><tr><td><p>3</p></td><td><p>- Empathy in Marketing: Understanding Consumers on a Deeper Level<break />- Workshop on empathy mapping and customer persona creation<break />- Case study discussion on empathy in marketing</p></td><td><p>- Reading: Chapters 3-4 of "Creative Confidence"</p></td></tr><tr><td><p>4</p></td><td><p>- Ideation and Prototyping in Marketing Strategies<break />- Brainstorming session<break />- Workshop on rapid prototyping techniques</p></td><td><p>- Group Project: Ideation and Prototyping Exercise</p></td></tr><tr><td><p>5</p></td><td><p>- Storytelling and Branding<break />- Discussion on the importance of storytelling in branding<break />- Group activity on crafting compelling brand stories</p></td><td><p>- Case Analysis: Analyzing storytelling in selected brands</p></td></tr><tr><td><p>6</p></td><td><p>- Designing Customer Experiences<break />- Workshop on customer journey mapping<break />- Case study discussion on memorable customer experiences</p></td><td><p>- Reading: Chapters 1-3 of "Well-Designed"</p></td></tr><tr><td><p>7</p></td><td><p>- Iteration and Feedback in Marketing and Life<break />- Workshop on collecting and analyzing feedback<break />- Group discussion on iterating marketing strategies</p></td><td><p>- Reflection Essay: Iterative Processes in Personal Life Challenges</p></td></tr><tr><td><p>8</p></td><td><p>- Cross-disciplinary Collaboration<break />- Panel discussion with professionals from different fields<break />- Group activity on cross-disciplinary problem-solving</p></td><td><p>- Group Project: Cross-disciplinary Collaboration Exercise</p></td></tr><tr><td><p>9</p></td><td><p>- Digital Marketing through a Design Thinking Lens<break />- Workshop on digital marketing tools and strategies<break />- Group discussion on the challenges and opportunities in digital marketing</p></td><td><p>- Case Analysis: Digital Marketing Campaign Evaluation</p></td></tr><tr><td><p>10</p></td><td><p>- Social Innovation and Design Thinking<break />- Case study discussion on social innovation projects<break />- Group activity on designing for social impact</p></td><td><p>- Reading: "Design Thinking for Social Innovation" article</p></td></tr><tr><td><p>11</p></td><td><p>- Sustainable Marketing Practices<break />- Discussion on the role of sustainability in marketing<break />- Workshop on developing sustainable marketing strategies</p></td><td><p>- Reflection Essay: Applying Design Thinking to Sustainable Marketing</p></td></tr><tr><td><p>12</p></td><td><p>- Leadership and Design Thinking<break />- Discussion on the qualities of design-thinking leaders<break />- Group activity on leadership challenges and solutions</p></td><td><p>- Reading: Chapters 1-2 of "Originals"</p></td></tr><tr><td><p>13</p></td><td><p>- Design Thinking in Crisis Management<break />- Simulation exercise on crisis management scenarios<break />- Debrief and discussion on crisis management strategies</p></td><td><p>- Group Project: Crisis Management Simulation</p></td></tr><tr><td><p>14</p></td><td><p>- Future Trends: Where Design Thinking meets AI and Big Data<break />- Discussion on the impact of technology on design thinking<break />- Group activity on leveraging technology for marketing innovation</p></td><td><p>- Case Analysis: Future of Design Thinking in Marketing</p></td></tr><tr><td><p>15</p></td><td><p>- Review and Reflection<break />- Course recap and reflection discussion<break />- Feedback session on course learning experience</p></td><td><p>- Preparation for Final Presentation</p></td></tr><tr><td><p>16</p></td><td><p>- Final Presentations<break />- Course closure and future learning pathways discussion</p></td><td><p>- Final Presentation on Design Thinking Application in a Marketing Problem or Life Scenario</p></td></tr></tbody></table> </ephtml> </p> <p>The authors would like to thank Paul Messinger and Xin Ge for their valuable feedback and support throughout this research. 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Koo contributed equally to the manuscript, and their names appear in alphabetical order.</bibtext> </blist> <blist> <bibtext> The data reported in this paper have been deposited at OSF and can be accessed using the following link: https://osf.io/v726q/?view_only=31efd04c73fc491c9bf517d4dc17c3f8.</bibtext> </blist> <blist> <bibtext> The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.</bibtext> </blist> <blist> <bibtext> The author(s) received no financial support for the research, authorship, and/or publication of this article.</bibtext> </blist> <blist> <bibtext> All procedures used in studies involving human participants are in accordance with the tenets of the Declaration of Helsinki or comparable ethical standards. This study was approved by Southern Connecticut State University's IRB (Approval no. #953) on September 11, 2023.</bibtext> </blist> <blist> <bibtext> Informed consent was obtained from all participants included in the studies.</bibtext> </blist> <blist> <bibtext> Hyoseok Kim</bibtext> </blist> <blist> <bibtext>Graph</bibtext> </blist> <blist> <bibtext>https://orcid.org/0009-0007-2347-1357 Thomas K.B. Koo</bibtext> </blist> <blist> <bibtext>Graph https://orcid.org/0000-0002-2668-2770</bibtext> </blist> </ref> <aug> <p>By Hyoseok Kim and Thomas K. B. Koo</p> <p>Reported by Author; Author</p> </aug> <nolink nlid="nl1" bibid="bib30" firstref="ref1"></nolink> <nolink nlid="nl2" bibid="bib16" firstref="ref2"></nolink> <nolink nlid="nl3" bibid="bib11" firstref="ref4"></nolink> <nolink nlid="nl4" bibid="bib25" firstref="ref8"></nolink> <nolink nlid="nl5" bibid="bib15" firstref="ref9"></nolink> <nolink nlid="nl6" bibid="bib24" firstref="ref10"></nolink> <nolink nlid="nl7" bibid="bib35" firstref="ref12"></nolink> <nolink nlid="nl8" bibid="bib38" firstref="ref13"></nolink> <nolink nlid="nl9" bibid="bib21" firstref="ref14"></nolink> <nolink nlid="nl10" bibid="bib17" firstref="ref16"></nolink> <nolink nlid="nl11" bibid="bib18" firstref="ref18"></nolink> <nolink nlid="nl12" bibid="bib27" firstref="ref19"></nolink> <nolink nlid="nl13" bibid="bib36" firstref="ref22"></nolink> <nolink nlid="nl14" bibid="bib19" firstref="ref26"></nolink> <nolink nlid="nl15" bibid="bib34" firstref="ref27"></nolink> <nolink nlid="nl16" bibid="bib13" firstref="ref29"></nolink> <nolink nlid="nl17" bibid="bib31" firstref="ref30"></nolink> <nolink nlid="nl18" bibid="bib32" firstref="ref31"></nolink> <nolink nlid="nl19" bibid="bib23" firstref="ref34"></nolink> <nolink nlid="nl20" bibid="bib28" firstref="ref35"></nolink> <nolink nlid="nl21" bibid="bib22" firstref="ref36"></nolink> <nolink nlid="nl22" bibid="bib26" firstref="ref37"></nolink> <nolink nlid="nl23" bibid="bib29" firstref="ref38"></nolink> <nolink nlid="nl24" bibid="bib14" firstref="ref39"></nolink> <nolink nlid="nl25" bibid="bib33" firstref="ref40"></nolink> <nolink nlid="nl26" bibid="bib196" firstref="ref41"></nolink> <nolink nlid="nl27" bibid="bib238" firstref="ref47"></nolink> <nolink nlid="nl28" bibid="bib12" firstref="ref56"></nolink> <nolink nlid="nl29" bibid="bib10" firstref="ref117"></nolink> <nolink nlid="nl30" bibid="bib20" firstref="ref122"></nolink> <nolink nlid="nl31" bibid="bib37" firstref="ref125"></nolink>
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  Data: The Impact of Generative AI on Syllabus Design and Learning
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  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Hyoseok+Kim%22">Hyoseok Kim</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0007-2347-1357">0009-0007-2347-1357</externalLink>)<br /><searchLink fieldCode="AR" term="%22Thomas+K%2E+B%2E+Koo%22">Thomas K. B. Koo</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-2668-2770">0000-0002-2668-2770</externalLink>)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Journal+of+Marketing+Education%22"><i>Journal of Marketing Education</i></searchLink>. 2026 48(1):20-41.
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  Label: Availability
  Group: Avail
  Data: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
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  Label: Peer Reviewed
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  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 22
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2026
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Audience
  Label: Education Level
  Group: Audnce
  Data: <searchLink fieldCode="EL" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="EL" term="%22Postsecondary+Education%22">Postsecondary Education</searchLink>
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Quality%22">Educational Quality</searchLink><br /><searchLink fieldCode="DE" term="%22Technology+Uses+in+Education%22">Technology Uses in Education</searchLink><br /><searchLink fieldCode="DE" term="%22Course+Descriptions%22">Course Descriptions</searchLink><br /><searchLink fieldCode="DE" term="%22Marketing%22">Marketing</searchLink><br /><searchLink fieldCode="DE" term="%22Business+Education%22">Business Education</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Attitudes%22">Student Attitudes</searchLink><br /><searchLink fieldCode="DE" term="%22Material+Development%22">Material Development</searchLink><br /><searchLink fieldCode="DE" term="%22Authors%22">Authors</searchLink><br /><searchLink fieldCode="DE" term="%22Disclosure%22">Disclosure</searchLink><br /><searchLink fieldCode="DE" term="%22Undergraduate+Students%22">Undergraduate Students</searchLink><br /><searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink><br /><searchLink fieldCode="DE" term="%22Conventional+Instruction%22">Conventional Instruction</searchLink>
– Name: Subject
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22North+America%22">North America</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1177/02734753241299024
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 0273-4753<br />1552-6550
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This research examines the impact of generative artificial intelligence (AI) on the perception of educational content quality, specifically by comparing AI-generated and human-generated course syllabi in marketing education. Results from four studies indicate a general preference for AI-generated syllabi, attributed to their greater perceived objectivity. This preference is more pronounced in conventional courses but diminishes in unconventional ones, suggesting that the unique aspects of these courses may reduce the advantages of generative AI. In addition, disclosing the AI authorship of syllabi significantly affects their perceived quality negatively, underscoring the impact of transparency on the acceptance of AI-generated educational materials. These findings highlight the potential of generative AI in educational content creation and its limitations in certain contexts. They offer valuable insights for enhancing educational practices and shaping policy decisions to enrich student experiences in the era of AI integration.
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  Data: 2026
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  Data: EJ1499495
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        Value: 10.1177/02734753241299024
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      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 22
        StartPage: 20
    Subjects:
      – SubjectFull: Artificial Intelligence
        Type: general
      – SubjectFull: Educational Quality
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      – SubjectFull: Technology Uses in Education
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      – SubjectFull: Marketing
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      – SubjectFull: Student Attitudes
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      – TitleFull: The Impact of Generative AI on Syllabus Design and Learning
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