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Generative Artificial Intelligence's Integration for Data Analysis in Conducting Academic Research: Understanding the Perspective of Research Supervisors

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Title: Generative Artificial Intelligence's Integration for Data Analysis in Conducting Academic Research: Understanding the Perspective of Research Supervisors
Language: English
Authors: Amandeep Sehmi (ORCID 0000-0002-1584-6055), Isra Sarfraz (ORCID 0000-0003-3803-4276), Muzammil Hussain (ORCID 0000-0001-8188-2014)
Source: Journal of Advanced Academics. 2025 36(4):788-815.
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: 28
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Artificial Intelligence, Technology Uses in Education, Technology Integration, Data Analysis, Educational Research, Higher Education, Ethics, Supervisors, Research Methodology, Graduate Students, Student Research, Foreign Countries
Geographic Terms: Australia
DOI: 10.1177/1932202X251365312
ISSN: 1932-202X
2162-9536
Abstract: This special issue article explores the role of generative artificial intelligence (GenAI) in the data analysis phase of academic research degrees, focusing on its adoption by research students in master's and doctor of philosophy programmes in the business and management disciplines, as viewed through the lens of research supervisors. A qualitative research methodology was adopted, involving semi-structured interviews with research supervisors. The findings revealed that while current familiarity with the use of GenAI tools for data analysis is limited among research supervisors, there is a growing recognition of their potential value and anticipated future acceptance in academic research. The findings of this study recommend the integration of GenAI training modules into research degrees. Furthermore, this study serves as a guide for future research studies exploring the role of GenAI in the data analysis processes of academic research. This study proposes guidelines to raise awareness and educate research students on the ethical use of GenAI, aiming to maintain integrity and enabling them to understand the scope and potential of emerging technologies for data analysis. Emphasising the ethical integration of GenAI, the enhancement of critical thinking, and the development of clear institutional policies are identified as key strategies to support the responsible use of GenAI in research and education.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1485752
Database: ERIC
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  Value: <anid>AN0188422899;[261p]01nov.25;2025Oct06.06:29;v2.2.500</anid> <title id="AN0188422899-1">Generative Artificial Intelligence's Integration for Data Analysis in Conducting Academic Research: Understanding the Perspective of Research Supervisors </title> <p>This special issue article explores the role of generative artificial intelligence (GenAI) in the data analysis phase of academic research degrees, focusing on its adoption by research students in master's and doctor of philosophy programmes in the business and management disciplines, as viewed through the lens of research supervisors. A qualitative research methodology was adopted, involving semi-structured interviews with research supervisors. The findings revealed that while current familiarity with the use of GenAI tools for data analysis is limited among research supervisors, there is a growing recognition of their potential value and anticipated future acceptance in academic research. The findings of this study recommend the integration of GenAI training modules into research degrees. Furthermore, this study serves as a guide for future research studies exploring the role of GenAI in the data analysis processes of academic research. This study proposes guidelines to raise awareness and educate research students on the ethical use of GenAI, aiming to maintain integrity and enabling them to understand the scope and potential of emerging technologies for data analysis. Emphasising the ethical integration of GenAI, the enhancement of critical thinking, and the development of clear institutional policies are identified as key strategies to support the responsible use of GenAI in research and education.</p> <p>Keywords: generative artificial intelligence; GenAI; higher education research; data analysis; ethical use; research supervisor; research student; research degree; qualitative</p> <p>Generative artificial intelligence (GenAI) has been around for over five decades and has contributed to various sectors, including education, healthcare, entertainment, transportation, media, and tourism, among others. With the numerous opportunities available to users in the education sector, educators have been exploring and incorporating pedagogical opportunities of GenAI to facilitate teaching and learning activities ([<reflink idref="bib72" id="ref1">72</reflink>]) and enhance students' learning experiences. However, the success of integrating GenAI in learning and teaching activities depends on students' adaptation and acceptance ([<reflink idref="bib16" id="ref2">16</reflink>]).</p> <p>GAI, in its modern form, emerged around 2014 with the introduction of generative adversarial networks. However, it truly entered the mainstream in 2022 with the rise of tools like ChatGPT and other large-language models (AI programs designed to process and generate natural language). The utilisation of GenAI in the higher education sector also observed a surge in interest from educators and learners after the release of ChatGPT in November 2022 ([<reflink idref="bib34" id="ref3">34</reflink>]). This brought a paradigm shift in the education sector and opened new avenues for learners and educators alike. However, students have been observed to exploit the use of this system, which is attributed to a lack of knowledge and awareness, raising concerns about authenticity and integrity ([<reflink idref="bib5" id="ref4">5</reflink>]). To combat these challenges, educators need to incorporate GenAI-based tools into the learning and teaching processes, enabling students to utilise the system effectively.</p> <p>Researchers have identified various applications of GenAI in the education sector, including content development, tutoring, assessment and evaluation, student profiling and performance prediction, personalisation, student engagement, scheduling, and other management tasks ([<reflink idref="bib72" id="ref5">72</reflink>]; [<reflink idref="bib77" id="ref6">77</reflink>]). The potential adaptation of GenAI has proven to improve learning outcomes and enhance productivity by providing personalised learning experience, enhancing access to educational resources, increasing student engagement and peer collaboration ([<reflink idref="bib21" id="ref7">21</reflink>]; [<reflink idref="bib30" id="ref8">30</reflink>]; [<reflink idref="bib68" id="ref9">68</reflink>]). These developments underscore the growing need to integrate GenAI into higher education, as it contributes to enhanced outcomes at individual, institutional, and societal levels.</p> <p>In the current era where GenAI tools provide new avenues for knowledge creation, analysis, and synthesis, the cognitive demands placed on research students are both heightened and transformed. During meaningful enquiry, the ability to effectively integrate GenAI into research processes becomes a new marker of giftedness, which emphasises not only cognitive ability but also digital fluency, creativity, and adaptability ([<reflink idref="bib67" id="ref10">67</reflink>]).</p> <p>It is evident from the literature review that researchers have defined gifted students in diverse ways, and there is a lack of consensus on a single definition. Traditionally, during the early 20th century, schools used to serve a small portion of students by enrolling them in a special programme known as talented and gifted programmes. These students were selected based on statistical data, primarily their intellectual quotient (IQ) scores. Gifted students demonstrate excellent academic performance, high motivation, creativity, and thinking ([<reflink idref="bib50" id="ref11">50</reflink>]). Over time, experts have recognised the limitations of relying solely on IQ scores for identifying giftedness and have called for more comprehensive, multidimensional criteria. [<reflink idref="bib50" id="ref12">50</reflink>] suggested that gifted students can be determined based on who makes high contributions towards society, significant contributions in innumerable fields, has multidimensional and multifaceted personality, even fall off a trajectory of academic excellence and mark in an excellence in the educational field, even if their IQ score is low. Therefore, in line with [<reflink idref="bib31" id="ref13">31</reflink>] reasoning, we argue that students pursuing research through master's or doctor of philosophy (PhD) studies are gifted as pursuing these studies requires individuals to possess higher cognitive capabilities in comparison to their peers, particularly in academic achievement, must be open to new experiences and think critically and explore new knowledge (a lower score on agreeableness). [<reflink idref="bib4" id="ref14">4</reflink>] noted that gifted individuals may significantly impact the future of society; therefore, there is a need to pay special attention to these individuals and develop strategies, services, and programmes that enable them to reach their full potential ([<reflink idref="bib1" id="ref15">1</reflink>]). Thus, the conceptualisation of research students as gifted learners is not only relevant but increasingly essential for curriculum design and supervision models that seek to cultivate high-level, future-ready scholars.</p> <p>Research degree students (students conducting a master's research-based thesis or doctoral dissertation) who are new to the field of research often find it challenging to navigate the research world, despite their academic abilities, due to lack of experience and support. To facilitate the research process for these students, educators, supervisors, and mentors must introduce interventions that help students in their research activities (including data collection, and analysis of literature and data). GenAI has been a proven tool for conducting literature reviews and analysing data; however, such practices are not standard in the higher education sector, particularly in the social sciences. Hence, there is a need to further delve into this topic within the realm of advanced academics—that is, opportunities for gifted students that are not offered in typical educational settings and may help these students portray their exceptional capabilities.</p> <p>From the lens of research-based learning, GenAI is believed to have vast potential, as it can aid research students in generating ideas, synthesising information, summarising literature, and developing write-ups, while also contributing to efficiency in publications ([<reflink idref="bib10" id="ref16">10</reflink>]; [<reflink idref="bib19" id="ref17">19</reflink>]; [<reflink idref="bib71" id="ref18">71</reflink>]). GenAI is gradually modifying the role of supervisors in learning and research activities by selecting appropriate AI tools to monitor progress, offer personalisation, provide prompt feedback, act as virtual assistants, offer diverse learning opportunities, and develop higher learning abilities ([<reflink idref="bib73" id="ref19">73</reflink>]). [<reflink idref="bib18" id="ref20">18</reflink>] identified that postgraduate students have an interest in the potential utilisation of GenAI to assist in managing research projects, data collection and analysis, which could potentially lead to saving time, and enhancing the efficiency of the outcomes. Similarly, [<reflink idref="bib56" id="ref21">56</reflink>] also mentioned that the utilisation of GenAI can facilitate labour-intensive tasks, freeing up time for other activities. However, [<reflink idref="bib77" id="ref22">77</reflink>] have argued that despite the massive potential of GenAI in the education industry, its application is limited, and there is a need to explore and implement new ways of incorporating GenAI for the benefit of educators, learners, and researchers.</p> <p>Despite the increasing interest among educators in leveraging GenAI for teaching and learning activities ([<reflink idref="bib20" id="ref23">20</reflink>]; [<reflink idref="bib45" id="ref24">45</reflink>]; [<reflink idref="bib57" id="ref25">57</reflink>]), its use for research purposes is still unclear. There has been considerable interest in the impact of GenAI on researchers and the research landscape in higher education ([<reflink idref="bib3" id="ref26">3</reflink>]). [<reflink idref="bib9" id="ref27">9</reflink>] argued that social science researchers must work on evaluating the potential of GenAI in research processes despite its limitations and biases. Studies ([<reflink idref="bib7" id="ref28">7</reflink>]; [<reflink idref="bib12" id="ref29">12</reflink>]; [<reflink idref="bib61" id="ref30">61</reflink>]) have been conducted to understand the role of GenAI in the research process but have failed to provide a deeper understanding. Hence a research gap has been identified, limiting the researchers' knowledge of the potential use of GenAI in the data analysis process, as adopted by business and management research students. The current research study aims to fill this gap in the literature by exploring the perspective of research supervisors on the potential role GenAI can play in the data analysis process, when adopted by business and management research students. This study also aims to explore the possible benefits and challenges associated with incorporating GenAI into the academic research process. To address this research gap, the following research question is defined.</p> <p> <emph>Research Question:</emph> <emph>What is the research supervisors' perception of using GenAI for data analysis by business and management research students in Australia?</emph> </p> <p>To answer this research question, the following key objectives are defined:</p> <p></p> <ulist> <item> Examine the role of GenAI in data analysis for conducting research in higher education.</item> <p></p> <item> Investigate the research supervisors' perception of using GenAI tools in the research process.</item> <p></p> <item> Analyse the benefits and challenges associated with GenAI's use in data analysis.</item> </ulist> <p>Mezirow's theory of transformative learning is applied as an overarching theoretical framework to investigate the perspective of research supervisors in the current study. Transformative learning has been extensively employed in literature to define the attitudes and habits of mind ([<reflink idref="bib36" id="ref31">36</reflink>]) and to understand the process and knowledge acquisition for reflective and conscious learning experiences ([<reflink idref="bib51" id="ref32">51</reflink>]).</p> <hd id="AN0188422899-2">Literature Review</hd> <p>The rapid evolution of GenAI tools has generated substantial interest within the academic community due to their potential to enhance research productivity, simplify complex analytical tasks, and foster innovative knowledge creation ([<reflink idref="bib35" id="ref33">35</reflink>]; [<reflink idref="bib39" id="ref34">39</reflink>]). However, this technological advancement also prompts critical discussions around research integrity, authorship, and the fundamental nature of academic work ([<reflink idref="bib21" id="ref35">21</reflink>]; [<reflink idref="bib61" id="ref36">61</reflink>]). Pertaining to these challenges, some scholars recommend against the use of such technologies in academia ([<reflink idref="bib6" id="ref37">6</reflink>]); however, this is not a sustainable approach towards combating these challenges and higher education sector must engage in harnessing the positive potential of modern technologies ([<reflink idref="bib28" id="ref38">28</reflink>]; [<reflink idref="bib70" id="ref39">70</reflink>]). As institutions increasingly emphasise research output and rankings, the imperative to utilise GenAI for productivity gains could inadvertently undermine core principles of academic integrity and strain existing academic communication systems. To combat this, there is a need for principles and protocols to dictate the use of GenAI in the research process. Recently, publishers have started to accept the use of GenAI in article development; however, they have cautioned that GenAI must be used with continuous and consistent human oversight of the content ([<reflink idref="bib75" id="ref40">75</reflink>]) to ensure transparency and integrity.</p> <hd id="AN0188422899-3">Benefits of GenAI in Academic Research</hd> <p>More recently, GenAI has garnered considerable attention in academic research. This is due to the ability of GenAI tools to significantly automate specific tasks for researchers, including data analysis, knowledge discovery, and problem-solving ([<reflink idref="bib3" id="ref41">3</reflink>]). This can potentially enhance scientific progress by improving productivity, saving time, and enabling discoveries that would be tedious to do without technological intervention. Some researchers including [<reflink idref="bib35" id="ref42">35</reflink>] posited that the considerable value of GenAI tools such as ChatGPT and large-language models in academic research has hardly been considered, ignoring the potential of these tools to process extensive data and generate insights which can lead to better outcomes acquired within a shorter amount of time.</p> <p>[<reflink idref="bib33" id="ref43">33</reflink>], while discussing UNESCO's guidelines for responsible and ethical use of GenAI tools in research, highlighted their application in creating research questions, suggesting methodologies, and analysing and interpreting data. [<reflink idref="bib76" id="ref44">76</reflink>] explored the role of ChatGPT as a collaborative tool in thematic analysis, particularly in improving coding efficiency and rapidly analysing large volumes of textual data, identifying patterns and recurring themes, and highlighting anomalies that might otherwise escape human observation ([<reflink idref="bib38" id="ref45">38</reflink>]). [<reflink idref="bib66" id="ref46">66</reflink>] also highlighted GenAI's potential to support coding tasks, contributing to code generation and refinement in qualitative research. As [<reflink idref="bib14" id="ref47">14</reflink>] mentioned, incorporating GenAI in the research processes can lead to long run benefits by giving it the role of research assistance. These benefits include enhanced productivity among researchers by diverting their attention away from repetitive, labour-intensive, lower-level tasks and redirecting it towards functions that require critical thinking and creativity ([<reflink idref="bib14" id="ref48">14</reflink>]).</p> <p>[<reflink idref="bib25" id="ref49">25</reflink>], from his research on improving data quality using GenAI, shared that GenAI cannot be used as a replacement for traditional methods, but instead should be used in conjunction with them to enhance data quality and accuracy. This results in a reliable foundation for data analysis, streamlining workflows, and improving productivity. This highlights the potential benefits that can be reaped from using the latest GenAI tools and techniques in conducting already cumbersome research processes.</p> <hd id="AN0188422899-4">Concerns Regarding GenAI in Academic Research</hd> <p>Acknowledging the importance of GenAI in systematic analysis for selection of relevant articles, data extraction, synthesis, and analysis, researchers have raised concerns on the reliability of GenAI in generating outputs, data integrity, and authorship ([<reflink idref="bib17" id="ref50">17</reflink>]; [<reflink idref="bib62" id="ref51">62</reflink>]). Highlighting the stress imposed by institutions on researchers for publishing research in high-ranked journals, [<reflink idref="bib61" id="ref52">61</reflink>] further stated that the use of GenAI tools compromises the central concept of academic integrity. However, the ability of GenAI tools to enhance productivity through innovative approaches has also attracted considerable attention from educational researchers ([<reflink idref="bib39" id="ref53">39</reflink>]).</p> <p>Despite the vast potential of GenAI's use in academic research, researchers and practitioners have raised many concerns regarding its use in the research process. Many researchers have argued that this emerging technology poses significant risks regarding its ethical use and transparency ([<reflink idref="bib64" id="ref54">64</reflink>]). [<reflink idref="bib26" id="ref55">26</reflink>] and [<reflink idref="bib61" id="ref56">61</reflink>] have highlighted the potential of producing non-replicable research results due to the use of GenAI. Researchers have also expressed their concerns regarding the originality and authenticity of data produced by GenAI ([<reflink idref="bib49" id="ref57">49</reflink>]; [<reflink idref="bib52" id="ref58">52</reflink>]). This can be attributed to the GenAI's ability to "hallucinate"—that is, the ability to produce convincing but incorrect information ([<reflink idref="bib41" id="ref59">41</reflink>]), "false positives or false negatives" ([<reflink idref="bib63" id="ref60">63</reflink>]), or "distortion of reality" ([<reflink idref="bib24" id="ref61">24</reflink>]). GenAI hallucinations can lead to an output that is untrue or might not exist. [<reflink idref="bib24" id="ref62">24</reflink>] recommend that researchers perform a full hallucination check when applying GenAI tools in the research process to ensure the integrity of the information produced can be preserved. This is consistent with [<reflink idref="bib75" id="ref63">75</reflink>] recommendation of using GenAI with complete human oversight to avoid integrity issues.</p> <p>Apart from the systematic concerns, there is a possibility of over-reliance on GenAI's use by researchers, which may lead to their inability to understand the subject at hand thoroughly ([<reflink idref="bib40" id="ref64">40</reflink>]). This can result in a lack of understanding and insufficiency of original critical analysis, which is a key to produce original scholarly outcomes. Another issue with the use of GenAI in data analysis is the system's inability to understand and incorporate deep human, social, and contextual factors (e.g., understanding cultural elements and interpreting non-verbal cues) into analysis ([<reflink idref="bib61" id="ref65">61</reflink>]). To overcome this, the computational power of GenAI and the creative aspects of human intelligence need to be combined, resulting in a synergistic outcome that is far greater than what either of these two can achieve alone ([<reflink idref="bib37" id="ref66">37</reflink>]). This aligns with the outcome shared by [<reflink idref="bib25" id="ref67">25</reflink>] and coined as "co-intelligence" by [<reflink idref="bib53" id="ref68">53</reflink>].</p> <hd id="AN0188422899-5">Considerations for the Use of GenAI in Academic Research</hd> <p>While discussing the potential role of GenAI in academic research, it is crucial to consider the perception and level of readiness research supervisors possess to facilitate students' adaptation of these technologies in the research process. [<reflink idref="bib61" id="ref69">61</reflink>] have noted that supervision, monitoring, and expert input are crucial when a new and inexperienced researcher (i.e., a research student) utilises GenAI in their research. [<reflink idref="bib22" id="ref70">22</reflink>], p. 74) highlight the research supervisor's role shifts to a "strategic direction and high-level guidance" provider when the research students use ChatGPT. This highlights the significance of a research supervisor's readiness and ability to supervise their students while helping them utilise new systems to enhance efficiency and productivity.</p> <p>To address the issue of researchers' readiness to utilise GenAI in research processes, it is necessary to enhance GenAI literacy through educational programmes and community support ([<reflink idref="bib23" id="ref71">23</reflink>]), targeting both research supervisors and students. [<reflink idref="bib15" id="ref72">15</reflink>] revealed from their research that many PhD supervisors are unaware of the potential of GenAI to be used in research processes. This leads to supervisors not being prepared to provide students with the relevant support that can help them utilise GenAI in their research. Despite the lack of institutional support available, researchers today have access to plenty of open access resources including software packages, GitHub repositories (cloud-based collaboration platform for coding projects), publications, guides, and use cases that offers a collaborative learning environment to stay informed of the latest advancements, applications, and challenges of GenAI, paving the way for better integration of GenAI in research ([<reflink idref="bib23" id="ref73">23</reflink>]).</p> <p>From a review of the literature discussing the use of GenAI by researchers, it has been identified that a large volume of discussion is limited to scholarly research and publications ([<reflink idref="bib44" id="ref74">44</reflink>]), neglecting its potential for use by higher degree research students. [<reflink idref="bib32" id="ref75">32</reflink>] have discussed the importance of incorporating GenAI technology in research supervision to ensure research transparency and integrity; however, the focus of this study was the impact of GenAI tools on research supervisor/student relationship and does not consider the supervisor's perspective on the use of these tools by the research students.</p> <hd id="AN0188422899-6">Gap in Research on the Use of GenAI in the Data Analysis Process</hd> <p>Further examination of the literature reveals that relatively few studies have explored the specific application of GenAI in data analysis. [<reflink idref="bib12" id="ref76">12</reflink>], in their qualitative research study on the implementation of ChatGPT in data analysis, found that students require support to develop the skills necessary to frame prompts that generate accurate and effective results. [<reflink idref="bib7" id="ref77">7</reflink>] researched the perceptions of research students regarding the use of GenAI in five research processes. Results of this study identified a positive response towards the data analysis process, along with only one other research process. GenAI was regarded as a "research accelerator" as research found its use leading to higher productivity ([<reflink idref="bib7" id="ref78">7</reflink>]). Despite its significance, Andersen's study failed to provide an in-depth understanding of the use of GenAI in the research data analysis process from either the researcher's or research supervisor's perspective.</p> <p>Universities have begun to permit the use of GenAI tools in research processes, subject to specific guidelines ([<reflink idref="bib61" id="ref79">61</reflink>]). In addition to the studies mentioned above, [<reflink idref="bib3" id="ref80">3</reflink>] reported a positive attitude among university students towards using GenAI tools for research purposes. However, the participants involved in this study were identified as non-active researchers, highlighting the need to research the views of active researchers.</p> <p>Another research gap observed from the literature review is the lack of research on the understanding and acceptance of GenAI tools from the perspective of research supervisors. Research supervisors play a significant role in the research journey of students, and their acceptance of these tools can significantly improve the adoption rate among research students. [<reflink idref="bib7" id="ref81">7</reflink>] reported that junior researchers are more comfortable with GenAI and hence use it more than senior researchers. This further highlights the importance of understanding the perspective of research supervisors (i.e., senior researchers), as they play a crucial role in the academic research journey of research students.</p> <p>The review of the literature, as mentioned above, identified that most of these studies were conducted in the fields of computer science, data science, or other related fields. Hence, there is a lack of understanding among social sciences research students on how to adopt GenAI. [<reflink idref="bib46" id="ref82">46</reflink>] emphasised that embracing GenAI tools can facilitate the capture of human experiences in the qualitative research process. Several researchers ([<reflink idref="bib61" id="ref83">61</reflink>]; [<reflink idref="bib66" id="ref84">66</reflink>]; [<reflink idref="bib74" id="ref85">74</reflink>]) have highlighted the application of the GenAI tool in both qualitative and quantitative data analysis. Hence, this research will fill the gap by understanding the perspective of research supervisors on the utilisation of GAI in the academic research data analysis process in the field of social sciences.</p> <hd id="AN0188422899-7">Methods</hd> <p>In the context of the present study, a qualitative methodology is employed to collect data from research supervisors across higher education institutions in Australia. Snowball sampling is a purposive sampling technique used when it is difficult to find participants who satisfy the inclusion criteria ([<reflink idref="bib55" id="ref86">55</reflink>]). In this technique, a small number of research participants are identified based on the defined inclusion criteria and these initially chosen participants then refer to further potential participants ([<reflink idref="bib60" id="ref87">60</reflink>]). Since the use of GenAI is still emerging and the target population is specific and not easily accessible, we employed snowball sampling to recruit relevant participants. Additionally, as this study is exploratory and the boundaries of the population were not clearly defined, snowball sampling provided a flexible and practical approach to identify information-rich individuals.</p> <hd id="AN0188422899-8">Inclusion Criteria</hd> <p>Precise inclusion criteria for participants were established to ensure the relevance and depth of data that would reach the unit of analysis. Moreover, the inclusion criteria for this study were the selection of research supervisors who understand GenAI in general and its application in the academic research process in particular. The research supervisors who are currently supervising or have supervised research students (business and/or management) were invited.</p> <hd id="AN0188422899-9">Participants</hd> <p>The participants in this study were from three different Australian universities, all of which are currently supervising postgraduate research students, including PhD candidates. Furthermore, these five participants held academic positions ranging from senior lecturer to associate dean (research), reflecting a diversity of institutional roles and responsibilities. Their professional experience in higher education ranged from 5 to 15 years of supervising research projects. The inclusion of five experienced research supervisors as participants provides rich, context-specific perspectives on the use of GenAI in education. It enables a nuanced understanding of individual experiences and emerging themes. Although the study includes a small sample of five research supervisors, this is consistent with the goals of qualitative research, which focuses on depth rather than breadth of understanding. Participants were selected through purposive sampling technique (snowball sampling) to ensure they had relevant experience supervising research students and engaging with GenAI-related issues. As such, their insights offer rich, contextually grounded perspectives that reflect key themes and concerns within the broader research supervision community. The sample enables an in-depth exploration of complex viewpoints and helps identify emerging patterns that can inform future, larger-scale investigations. Thus, this purposive selection ensured that participants had substantial experience in student supervision, making them well-suited to provide informed insights relevant to the study's focus.</p> <hd id="AN0188422899-10">Ethics Approval</hd> <p>Before starting the research process for this study, this project was approved by the Swinburne University Human Research Ethics Committee.</p> <hd id="AN0188422899-11">Data Collection</hd> <p>Qualitative data was collected using semi-structured interviews. The focus of the interviews was to understand the research supervisors' perspective on the integration of GenAI in the data analysis process by their research students, and to explore any benefits and challenges, as well as the ethical implications for using GenAI in the research process that must be considered. The interviews were conducted online using MS Teams and took an average of 30 min. With the participants' permission, the audio of the interviews was recorded and transcribed for analysis purposes. We ensured the participants' confidentiality and anonymity by removing all identifying information during transcription. Participants were assigned pseudonyms, and any contextual details that could potentially reveal their identity were anonymised. Data were stored securely on a password-protected server accessible only to the research team, in accordance with the institution's ethics approval protocols.</p> <p>Due to the exploratory nature of the study, five interviewees were selected to conduct expert interviews. For a qualitative exploratory study, a small sample size is generally acceptable due to the inductive and emergent nature of the research. [<reflink idref="bib27" id="ref88">27</reflink>], based on his review of extensive literature, mentioned that a minimum of five interviews for a qualitative study is adequate. This must be considered in conjunction with the nature and scope of the study, quality of data, and the amount of useful information obtained from each participant ([<reflink idref="bib54" id="ref89">54</reflink>]). Argued by other researchers ([<reflink idref="bib47" id="ref90">47</reflink>]), qualitative research does not aim to generalise the results to a large population; instead, it focuses on understanding the perspectives and experiences of a specific group ([<reflink idref="bib8" id="ref91">8</reflink>]). Additionally, no further insights or issues emerged after five interviews, as the data began to reveal recurring themes: ethical and integrity issues, the need for training and education of research students, and the limited knowledge of research supervisors regarding GenAI, as they are still in the preliminary stages of exploring its use. As argued by [<reflink idref="bib29" id="ref92">29</reflink>], saturation is a critical indicator in qualitative research that a sample is sufficient to understand a phenomenon. Researchers ([<reflink idref="bib58" id="ref93">58</reflink>]) have also argued that saturation is a significant aspect of qualitative research, which makes the process of data collection valid and robust.</p> <hd id="AN0188422899-12">Interview Questions</hd> <p>As the interviews were semi-structured, we were flexible with the questions, so we began with a set of guiding sample questions. In some cases, some leading questions were asked to clarify the situation and get a deeper exploration. Sample questions are as follows.</p> <p></p> <ulist> <item> What is the degree level and area of research that you are supervising?</item> <p></p> <item> How familiar are you with the use of generative artificial intelligence (GenAI) in research?</item> <p></p> <item> Have you used any such tool yourself? If yes, please name the tool.</item> <p></p> <item> Was it for quantitative or qualitative analysis? Or what specific types of data were analysed using the GenAI tool/s?</item> <p></p> <item> How user-friendly is the GenAI tool for research students? Were the students being provided with any special training to use the tool?</item> <p></p> <item> What key benefits GenAI bring to the data analysis process?</item> <p></p> <item> How do you think GenAI improves the efficiency of data analysis for research purposes as compared to traditional methods?</item> <p></p> <item> What are the major challenges you have encountered or foresee in integrating GenAI into research for data analysis?</item> <p></p> <item> How can the accuracy and reliability of analysed data be ensured while using GenAI tools?</item> <p></p> <item> How do you think research students can ensure ethical use of GenAI, especially in areas like data privacy, bias, and intellectual property?</item> <p></p> <item> What are the ethical implications of using this GenAI tool for data analysis, and how can they be addressed?</item> <p></p> <item> What skills or knowledge should future researcher students develop to effectively leverage GenAI for purposes of data analysis in their research?</item> </ulist> <hd id="AN0188422899-13">Data Analysis</hd> <p>After data collection, the content analysis method was used to analyse the qualitative data using NVivo software. Many researchers have used the content analysis method to analyse qualitative data ([<reflink idref="bib13" id="ref94">13</reflink>]; [<reflink idref="bib42" id="ref95">42</reflink>]). As mentioned by [<reflink idref="bib65" id="ref96">65</reflink>], the data analysis strategy consisted of the following three steps: arranging the data, becoming familiar with the data, and coding and interpreting the data. As suggested by [<reflink idref="bib13" id="ref97">13</reflink>], the research adhered to four criteria to ensure the trustworthiness of the qualitative data, that is, credibility, confirmability, transferability, and dependability.</p> <p>This study deployed an inductive coding approach, where themes emerged from the data itself rather than being guided by a predetermined framework. The transcripts were read multiple times to allow the codes to emerge from the raw data. The codes were then categorised and formed the basis for the themes ([<reflink idref="bib48" id="ref98">48</reflink>]). During the process of content analysis, NVivo software was engaged to facilitate the coding process, codes hierarchies, themes development, and comparing codes across different interviews. All three authors independently reviewed the transcripts and codes, consistently comparing the newly analysed data to the previously analysed data. Discussions happened to review any discrepancies in the interpretations which ultimately helped in refining the codes. This approach aligned with [<reflink idref="bib11" id="ref99">11</reflink>] qualitative data analysis process to maintain trustworthiness.</p> <p>The credibility of the data in this study was ensured by justifying data limitations in the five interviews. An interview consent form, given to research supervisors, also adds to research credibility ([<reflink idref="bib2" id="ref100">2</reflink>]). In line with the most qualitative studies, the thick description strategy was employed to maintain transferability ([<reflink idref="bib59" id="ref101">59</reflink>]), as exemplified by the inclusion of the complete interview questionnaire above in this article. The findings of this study will be made available to research supervisors upon publication, ensuring their transferability. The confirmability of the research was ensured by an unbiased interpretation of the data, free from personal judgements and assumptions ([<reflink idref="bib69" id="ref102">69</reflink>]). Dependability of the study was ensured by documenting the data collection and analysis process ([<reflink idref="bib2" id="ref103">2</reflink>]).</p> <hd id="AN0188422899-14">Findings</hd> <p>The purpose of this study was to understand the perspective of research supervisors on the potential role that GenAI can take in the data analysis process as adopted by business and management research students. Interviews were conducted with research supervisors to collect data. Based on the analysis of the collected data, four key themes are identified. The themes are supported by raw quotes from the interviews and are discussed below. Table 1 summarises the identified themes alongside representative quotes to improve clarity and transparency.</p> <p>Table 1. Themes, Codes, and Example Quotations.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="left" /><col align="left" /></colgroup><thead><tr><th align="left">Themes</th><th align="left">Codes</th><th align="left">Quotes</th></tr></thead><tbody><tr><td>Theme 1: Research supervisors' perspective on the adoption of GenAI</td><td><list list-type="Bullet"><list-item><p>Grammar assistance</p></list-item><list-item><p>Literature review</p></list-item><list-item><p>Survey questionnaire</p></list-item><list-item><p>Lack of familiarity</p></list-item><list-item><p>Using traditional tools</p></list-item><list-item><p>Exploring AI tools</p></list-item><list-item><p>Early-stage adoption</p></list-item><list-item><p>Awareness</p></list-item></list></td><td><list list-type="Bullet"><list-item><p>"I haven't spoken specifically with students about the particular tools that they're using ...." (Participant 3)</p></list-item><list-item><p>"Not actually doing the actual analysis ... much more around the grammar ... and basic structure of a literature review." (Participant 1)</p></list-item><list-item><p>"We use traditional machine learning models as they are more useful and user friendly." (Participant 2)</p></list-item><list-item><p>"Still finding traditional tools very useful for quantitative studies." (Participant 4)</p></list-item><list-item><p>"... developing a survey ... what are the sorts of questions that we could be asking." (Participant 5)</p></list-item><list-item><p>"Still exploring myself, still learning ... but it's very useful ...." (Participant 2)</p></list-item></list></td></tr><tr><td>Theme 2: Influence of GenAI on the ethics and integrity of the research findings</td><td><list list-type="Bullet"><list-item><p>Ethical approach needed</p></list-item><list-item><p>Challenging</p></list-item><list-item><p>Data hallucination</p></list-item><list-item><p>Content fabrication</p></list-item><list-item><p>Ethical risk</p></list-item><list-item><p>Lack of policies</p></list-item></list></td><td><list list-type="Bullet"><list-item><p>"(GenAI) ... being used to fabricate material ... Where does that line exist?" (Participant 5)</p></list-item><list-item><p>"Where is the AI getting this information? It's hallucinating ...." (Participant 2)</p></list-item><list-item><p>"Use of GenAI ... not matured yet ... students are not confident." (Participant 3)</p></list-item><list-item><p>"Speed of matter production and access to data ... learning curve is very fast." (Participant 3)</p></list-item></list></td></tr><tr><td>Theme 3: Mitigating the ethical aspect of GenAI</td><td><list list-type="Bullet"><list-item><p>Transparency in using GenAI</p></list-item><list-item><p>Responsible use</p></list-item><list-item><p>Ethical use</p></list-item><list-item><p>Awareness</p></list-item><list-item><p>Training</p></list-item><list-item><p>Authorities' responsibilities</p></list-item></list></td><td><list list-type="Bullet"><list-item><p>"If people understand the scope ... just like with cars ... they're very useful." (Participant 4)</p></list-item><list-item><p>"We need a policy ... international, national ... or even organisation level." (Participant 5)</p></list-item><list-item><p>"Students ... have a responsibility ... be transparent ...." (Participant 3)</p></list-item><list-item><p>"Researchers must be transparent ... disclose methodologies ...." (Participant 5)</p></list-item><list-item><p>"... this is what I did ... this is how I used it ...." (Participant 2)</p></list-item></list></td></tr><tr><td>Theme 4: Balancing GenAI integration and human critical thinking in research</td><td><list list-type="Bullet"><list-item><p>Role of humans</p></list-item><list-item><p>Critical skills development training</p></list-item><list-item><p>GenAI integration in curriculum</p></list-item><list-item><p>Risk related to over-reliance on GenAI</p></list-item></list></td><td><list list-type="Bullet"><list-item><p>"GenAI ... generate interpretations ... concern about overstepping the researcher's role." (Participant 5)</p></list-item><list-item><p>"AI ... very helpful ... but what is appropriate use?" (Participant 1)</p></list-item><list-item><p>"Mechanical vs creative side ... raises concerns." (Participant 4)</p></list-item><list-item><p>"Critical thinking skills ... still need to be training for." (Participant 1)</p></list-item><list-item><p>"Interpretation skills ... regardless of sophistication ...." (Participant 3)</p></list-item><list-item><p>"Researchers must continue developing skills ...." (Participant 5)</p></list-item></list></td></tr></tbody></table> </ephtml> </p> <p>1 GenAI: generative artificial intelligence.</p> <hd id="AN0188422899-15">Theme 1: Research Supervisors' Perspective on the Adoption of GenAI</hd> <p>The analysis revealed that the research supervisors acknowledged the importance of GenAI as a research assistant in accelerating the research process, particularly in areas such as data analysis. However, some participants believed there is limited familiarity with the use of GenAI tools in the research process by research students, particularly for data analysis purposes. In general, due to a lack of discussion and close insights, there is some unfamiliarity regarding the use of GenAI for data analysis purposes. However, the majority of the research supervisors remain optimistic about the future use of GenAI for academic research purposes, recognising its potential and benefits.</p> <p>As indicated in the following statements, two of our participating research supervisors have a limited understanding of how their research students have been using AI, apart from its use in presentations and grammar checking.</p> <p>... I haven't spoken specifically with students about the particular tools that they're using. So, I don't know the specific names, but I think at least up to the moment, it's primarily around assistance with things like grammar, spelling, sentence structure, those sorts of things. Some assistance, or at least initial assistance with initial presentation of results, but not so much, at least to my understanding. (Informed participant 3)</p> <p>Not actually doing the actual analysis. You know, maybe (I'm) missing some things that my student is doing, but at least from my understanding it's much more around the grammar, the sentence structure, the editing sort of role ... Similarly with the literature review, there's been some initial gathering of papers and so on and using AI tools to help with the basic structure of a literature review. (Added participant 1)</p> <p>Some research supervisors expressed concern that these emerging technologies are changing too fast, and they are still exploring the relevant use of these tools. Others, however, affirmed that traditional methods of the research process, including data analysis, are more relevant and valuable to research due to ethical implications associated with GenAI tools and their limited knowledge of how to use these tools.</p> <p>As emphasised by our participant 2, "I'm not sure how much it (GenAI tools) is to do with the research as such, because we use the traditional machine learning model as they are more useful and user friendly."</p> <p>Similarly, participant 4 confirms that traditional data analysis tools such as SPSS are more useful for quantitative studies, "... so they're still finding the traditional tools very useful. For quantitative studies. And also, it's very easy to compare results. There are new dimensions that can be deployed."</p> <p>The data further revealed that integration of GenAI in the research process has helped in developing survey questions, as evidenced by the statement of participant 5.</p> <p>So, I think the benefits are (widespread), if we look across the research process as a whole. From helping to identify what research tools have been used. So if we say for example, getting some assistance with developing a survey, what sorts of questions we could be asking (in) the survey?</p> <p>Although the concerns were expressed regarding ethical and technical aspects of the use of GenAI, research supervisors remain optimistic about integrating GenAI into the research process for data analysis as they keep exploring these emerging technologies. As indicated by participant 2, "I'm still exploring myself, still learning and many of the students are still learning as well, but it's (GenAI) very useful and in future it's going to become much more useful."</p> <hd id="AN0188422899-16">Theme 2: Influence of GenAI on the Ethics and Integrity of the Research Findings</hd> <p>Research supervisors revealed that engaging GenAI in the research process poses significant risk to ethics and integrity—the two fundamental pillars of any research project.</p> <p>As stated by participant 5,</p> <p>(GenAI) ... in some cases, (is) being used to fabricate material. So, I think it's one of the challenges of GenAI is it offers a wide range of potential and actual applications, some which I think are appropriate, and others which I would have concerns about. So, I think one of the issues that we're facing in management of business research is, is there a line that can be drawn? Where does that line exist? Who decides what that line is? And so on.</p> <p>Similarly, participant 2 raised concern on reliability of information provided by GenAI tools:</p> <p>Where is the AI getting this information? It's (GenAI) hallucinating. Is it? You know how? How reliable is this information? So, I think those would be the main two challenges and just say, keeping track of the different technologies that are out there. And I'm struggling to keep up with it like I was delighted to find out about.</p> <p>It's pertinent to mention here that a few research participants stated that supervisors and research students are still learning how to integrate GenAI tools in the research, for data analysis, which is evident from the information provided by the research supervisors as follows: "The use of GenAI for data analysis is not matured yet. Students using it are sometimes not confident that (if) I'm doing wrong or I'm doing right" (participant 3).</p> <p>As of today, there are two dimensions, I think where the benefits are very obvious. One is the speed of matter production and access to this data and also simplification of the data to the user level because the learning curve is becoming very fast. The second one I think is very important. (Affirmed participant 3)</p> <p>These findings align with the existing research which also highlight the risk associated with ethics and academic integrity as expressed by interviewees, who are concerned that there is no line drawn between ethical and unethical use of GenAI due to lack of clear guidelines and policies.</p> <hd id="AN0188422899-17">Theme 3: Mitigating the Ethical Aspect of GenAI</hd> <p>Considering the risk posed by GenAI tools to the ethics and integrity of research, research supervisors emphasised the importance of creating awareness and educating the research students on the ethical use of emerging technologies. They stressed the need to ensure that research students are fully aware of the complete scope and potential of the tools they are using for data analysis.</p> <p>As proposed by participant 4:</p> <p>It may feel like it increases the risks, but I believe that the risk can be mitigated comfortably because it comes down to how we use it and it's just like with cars, if people don't know how to drive, cars can be very dangerous. Otherwise, they're very useful. So, I believe that the tools are used in the right way, if people understand the scope.</p> <p>One of the research supervisors argued that a policy is needed at the government level to guide the ethical use of GenAI as different organisations are following their own standards.</p> <p>Universities, government, private companies, cannot agree how it should be used. Whether they should allow or disallow. We need to have a policy either at the international level, national government or state level, or even at organisation level. I believe the ethical issues cannot be solved otherwise without a defined set of rules. (Participant 5)</p> <p>Another research supervisor suggested that proper training offered to research students focusing on methods of using GenAI tools for data analysis is needed to mitigate the ethical challenges. "... the training should not be focused on the results but should be focused on the methods" (participant 2).</p> <p>In contrast, participant 3 feels that responsibility of ethical use for data analysis lies with research students:</p> <p>I think they (students) do have a responsibility in terms of ensuring the truthfulness of the information used. I think they should be transparent as well, how they've used it. Like if you use it for an analysis, you have to be clear about which engine you went to.</p> <p>The research supervisors also emphasise on maintaining transparency, believing that risk posed by GenAI can be mitigated through transparent use.</p> <p>Researchers must be transparent about AI usage in their research process, just as they would disclose methodologies in a methods chapter Ethical concerns revolve around ensuring researchers maintain control over interpretation rather than relying entirely on AI. The importance of maintaining academic integrity and ensuring proper attribution of AI's role in research. (Participant 5)</p> <p>Another research supervisor also emphasised the importance of researchers maintaining transparency,</p> <p>I think my main thought in terms (of) how to actually manage that is that people need to be transparent, you know and say for this part of this is what I did as an individual for this part of the research. Tool or this particular technology and this is how I used it and so on and so forth. So just as we need to do now, you know when we say for example, for writing up a methods chapter. (Participant 2)</p> <p>Referring to an example of analogy to driving a car, one of the research supervisors argued that responsibility lies with researchers to understand GenAI's capabilities and limitations to use it ethically. As participant 5 mentioned, "AI is not inherently risky, but misuse can lead to problems. The key is ensuring researchers understand AI's scope and limitations to use it effectively."</p> <hd id="AN0188422899-18">Theme 4: Balancing GenAI Integration and Human Critical Thinking in Research</hd> <p>Even when the GenAI is integrated ethically and responsibly in research, concerns remain. The key concern raised by research supervisors was that over-reliance on GenAI for data analysis and result interpretation in research may undermine the critical role of human involvement in ensuring accuracy and data facts. Research supervisors are concerned that human participation is crucial in qualitative research, where emotions and tone are crucial for conveying the story. In the words of participant 5,</p> <p>GenAI can enhance existing tools like SPSS and NVivo, offering more advanced analysis capabilities. AI can generate interpretations of survey or qualitative data, but there is concern about whether this oversteps the researcher's role. Uncertainty exists about how much AI should contribute to data interpretation without compromising the researcher's critical thinking.</p> <p>Similar concern was raised by participant 1, "Transcription, just managing and presenting data. AI can be very, very helpful for. I guess, doubts about what is the appropriate use of AI in that space."</p> <p>Research supervisors further argued that, although AI can assist researchers in data analysis, there is a need for continuous training in critical thinking, interpretation, and analysis skills, alongside technological advancements. AI can help, but researchers should be trained to question, refine, and critically engage with AI-generated outputs—the role of undergraduate, master's, and PhD programmes in strengthening these skills.</p> <p>So, I think the benefits are, if we look across the research process as a whole. From helping to identify what research tools have been used. So, if we say for example, getting some assistance with developing a survey, what are the sorts of questions that we could be asking (in) the survey? (Participant 5)</p> <p>The above argument was supported by another research supervisor:</p> <p>... I think for me one of the challenges is the mechanical sort of aspects as opposed to the more interpretive creative side of the research process which the technology can, at least to some extent, help with. But for me at least that raises concerns around what? What is the role of the human as the researcher in that context? (Participant 4)</p> <p>Highlighting the significance of proper training in using emerging technologies in research, research supervisors argued that GenAI training modules should be integrated into the academic curriculum of master's and PhD programmes, which would help research students to develop the necessary skills they require to conduct research, including data analysis and result interpretation.</p> <p>... for me the critical thinking skills are really what we still need to be providing training for. And I think that's where, for example, both say an undergraduate training. And certainly, in master's by research and PhD training, I think a critical part of that still needs to be—Enabling candidates to develop their critical thinking. (Emphasised participant 1)</p> <p>"Interpretation skills what it is, whatever the sophistication of the technology and the data is, we still need to go to what?" participant 3 argued. Supporting these statements, participant 5 claimed that it's critical for researchers to continue developing their skills in the fast-paced AI era, "At the end of the day, you know the tools can provide us with information. But it's critical that researchers have continued to be developing skills and be trained around skills, interpretation using GenAI."</p> <p>Overall, our fourth theme emphasises the need to maintain a balance between leveraging GenAI for its benefits and ensuring that human critical thinking remains central to the research process, which can be achieved by developing required skills among research students through proper training.</p> <hd id="AN0188422899-19">Discussion</hd> <p>In this study, we aimed to explore the perspective of research supervisors on the potential role GenAI can play in the data analysis process when adopted by management and business students pursuing research studies. The study found that GenAI holds significant potential as a research tool in the future; however, ethical- and integrity-related concerns must be addressed, and comprehensive training is essential for research students to learn the efficient and effective use of these emerging technologies in research for data analysis. Our findings provide compelling arguments to ensure transparency and ethical responsibilities to mitigate the risk associated with the misuse of GenAI, such as data manipulation and integrity issues.</p> <p>We explored the perspective of research supervisors on the use of GenAI for data analysis by research students, analysing interview data from five research supervisors. Our findings highlight that despite the potential benefits of GenAI for data analysis in academic research, the technology is not as widely applied specially in business and management disciplines. The results of this study align with [<reflink idref="bib7" id="ref104">7</reflink>], who reported that there is a dearth of understanding about the diverse use of GenAI and its perception in academic research. The findings of this study also indicate that use of GenAI for data analysis is still at early stage, as these tools are currently mainly used for literature review, which aligns with [<reflink idref="bib43" id="ref105">43</reflink>] findings who argued that GenAI such as ChatGPT has enormous potential in research including data analysis which is dormant until now.</p> <p>Another important finding of this study highlighted concerns about ethics and academic integrity, emphasising the need to adopt a transparent approach when using GenAI for data analysis purposes. [<reflink idref="bib61" id="ref106">61</reflink>] posited that engaging GenAI in research poses a high risk of unintentional academic integrity violations due to the pressure on researchers to increase productivity and research output. The interviewees are also concerned about the risk that there is no clear distinction between the ethical and unethical use of GenAI due to a lack of clear guidelines and policies. This highlights the need for proper training modules to be developed for both research students and supervisors, which helps to develop the required skills of using GenAI tools in research. This is also highlighted in our collected data. There is consensus among supervisors that awareness of the scope and potential of GenAI tools for data analysis is necessary, along with proper training and policies to ensure their ethical use. [<reflink idref="bib22" id="ref107">22</reflink>] argued that engaging ChatGPT in research practice could potentially lead to new dimensions in PhD supervision under the guidance of supervisors. Hence, the availability of training and support may enable supervisors to guide their students effectively.</p> <p>Another important finding of this study is the development of critical thinking skills in researchers who are required to use GenAI effectively in the research process. The interviewees agreed that even with the ethical use of GenAI in data analysis, there is a need for critical thinking from researchers. GenAI has the potential to produce mechanically correct outputs, but without human intervention, there is a risk of overlooking human emotions, contextual factors, and other subjective considerations in the process. This human intervention is essential for providing help in mitigating the phenomenon of AI hallucinations ([<reflink idref="bib24" id="ref108">24</reflink>]; [<reflink idref="bib41" id="ref109">41</reflink>]; [<reflink idref="bib63" id="ref110">63</reflink>]). Humans are uniquely capable of evaluating information within its broader context, and over-reliance on GenAI risks creating an environment where researchers miss opportunities to develop and exercise critical thinking skills.</p> <hd id="AN0188422899-20">Limitations</hd> <p>The study is limited in disciplinary breadth, which necessarily impacts the transferability of the findings. The key limitation of this study is based on responses from a specific subset of participants which solely focused on research supervisors supervising business and management research students. This disciplinary focus limits the study's capacity to account for more complex, multifaceted influences on the phenomena studied. This disciplinary limitation can be addressed in the future by conducting comparative studies across disciplines or institutional contexts, which may offer a more holistic and transferable understanding. For example, examining the perspective of supervisors who are supervising research students from other fields, such as education, engineering, or healthcare, could uncover unique challenges and opportunities inherent to each domain.</p> <p>Second, the key limitation lies in the mismatch between the intended focus and the nature of the data collected. The purpose of this study was to explore the role of GenAI in the data analysis process for academic research degrees from the perspective of research supervisors, focusing on its adoption in research studies programmes. However, in practice, most supervisors interviewed had limited knowledge of, or engagement with, AI to use for data analysis purposes. As a result, many of the responses centred on broader perceptions of GenAI use in academic contexts rather than the intended, more targeted focus. This limitation revealed a significant gap in supervisory awareness, and future researchers should focus on this perspective.</p> <p>Third, the focus of this study is limited to Australia only, hence, it is expected that future studies will investigate the GenAI's use in the higher education sector for research purposes in other countries as well, while considering the country-specific factors, for example, technology adoption rate, economic conditions, political factors, and so on.</p> <p>Fourth, our study did not explore the institutional policies and cultural context of universities. Some universities may have different policies in using GenAI in academic activities, which may impact researchers' engagement. Therefore, findings cannot be generalised based on Australian universities.</p> <p>Fifth, the field of GenAI is evolving with tools and their applications undergoing significant changes at a fast pace. With time, as new tools emerge and existing ones get updated, the patterns of use and perceptions of research integrity may shift accordingly. Thus, the findings of the present study represent a result of the current moment in time and context and may not be generalisable to other settings or future developments.</p> <p>Lastly, this study used snowball sampling as the primary method of participant recruitment. While this approach was beneficial in reaching a targeted population that may have been difficult to access through random sampling methods, it inherently introduces potential biases. It is a non-probabilistic sampling method that limits the generalisability of the findings, as the sample may not accurately represent the broader population. Furthermore, this method is vulnerable to selection bias because the initial pool of participants consists of like-minded individuals, as they may share similar characteristics and experiences, and lack diversity. We recommend that future studies could benefit from using mixed sampling strategies or combining snowball sampling with more systematic methods to enhance the representativeness and robustness of findings.</p> <hd id="AN0188422899-21">Conclusion and Implications</hd> <p>In conclusion, the findings of this study helped to understand the perspective of supervisors regarding the use of GenAI for data analysis in research. The interviews reveal a notable lack of familiarity with the application of GenAI in data analysis among academic researchers. Although some supervisors are aware of their research students using GenAI for grammar checking and presentation purposes, a significant gap remains in understanding the potential of GenAI for data analysis due to the lack of discussion and close insights. The influence of GenAI on research ethics and integrity remains a significant concern among supervisors. The reliability and transparency of GenAI-generated data are also pervasive, while concerns have been raised about a lack of confidence among research students in using these emerging technologies. Notably, the role of human critical thinking research was highlighted as a significant factor to ensure accuracy when integrating GenAI for data analysis in research.</p> <p>The findings and discussion of our research lead to multifaceted implications for better integrating GenAI into the research process and mitigating the ethical challenges associated with its use in research. At both the individual and institutional levels, it is essential to raise awareness and provide education to research students about the potential benefits of incorporating GenAI into the research process, particularly in data analysis. Educational institutions should develop and provide training to research students regarding a proper understanding and responsible use of GenAI. The training schedule should also include training sessions for research supervisors on how to support their students better while utilising the benefits of GenAI. Focusing on the full scope and potential of GenAI in the research process may help to make these trainings more effective.</p> <p>Another critical element to focus on is the ethical use of GenAI tools. Individuals (including research supervisors and individual students) must put in efforts to educate themselves on the ethical concerns related to the use of GenAI in the research process and how GenAI can be incorporated ethically in research for better productivity and efficiency. There is a need for structured guidelines or policy at the institutional, national, and/or international levels to standardise the ethical use of GenAI.</p> <p>This study recommends addressing ethical and integrity concerns related to the use of Gen AI in research. Institutions should adopt or adapt existing ethical frameworks, such as the Australian Code for the Responsible Conduct of Research or the UNESCO recommendation on the Ethics of AI usage. Specific guidelines may include clearly defining acceptable uses of GenAI in research tasks, ensuring proper attribution of AI-generated content, and integrating AI literacy and ethics training into research development programmes. Establishing review mechanisms such as, AI-use declarations or supervisor oversight for AI-assisted outputs can further safeguard academic integrity and promote responsible innovation.</p> <p>Moreover, to strike a balance between integrating AI and human intelligence skills, teachers and research supervisors need to design research projects (developing research questions, methods, and draft proposals) where research scholars can develop research skills with the help of GenAI, followed by human assistance to review critically. Additionally, organise workshops to train students in challenging AI-generated content and evaluating its quality, reliability, and originality.</p> <p>Lastly, the findings of this study provide important implications for institutional policy and professional development. Institutions emphasise the importance of structured training programmes that enhance researchers' ability to engage with GenAI tools critically. This includes fostering ethical awareness, improving digital literacy, and developing skills to evaluate AI-generated content. Furthermore, institutions should establish clear guidelines that govern the responsible use of GenAI in research contexts, thereby ensuring both academic integrity and innovation. These policies can provide consistent expectations across disciplines and support supervisors in navigating the evolving technological landscape. Ultimately, such measures will help embed responsible AI use into the research culture, benefiting individuals and institutions alike.</p> <ref id="AN0188422899-22"> <title> References </title> <blist> <bibl id="bib1" idref="ref15" type="bt">1</bibl> <bibtext> Adıgüzel S., Nibat G. Ç., Yıldırım M. (2022). Analyzing of researches on gifted in science education in Turkey from 2018 to 2020. 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Computers and Education: Artificial Intelligence, 2, 100025. https://doi.org/10.1016/j.caeai.2021.100025</bibtext> </blist> </ref> <ref id="AN0188422899-23"> <title> Footnotes </title> <blist> <bibtext> Amandeep Sehmi https://orcid.org/0000-0002-1584-6055 Isra Sarfraz https://orcid.org/0000-0003-3803-4276 Muzammil Hussain https://orcid.org/0000-0001-8188-2014</bibtext> </blist> <blist> <bibtext> The study was approved by Swinburne Human Research Ethics Committee (Ref: 20248286-19998).</bibtext> </blist> <blist> <bibtext> The authors received no financial support for the research, authorship, and/or publication of this article.</bibtext> </blist> <blist> <bibtext> The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.</bibtext> </blist> </ref> <aug> <p>By Amandeep Sehmi; Isra Sarfraz and Muzammil Hussain</p> <p>Reported by Author; Author; Author</p> <p></p> <p>Dr Amandeep Sehmi holds a PhD in business with specialisations in marketing and entrepreneurship. With a decade of industry experience in marketing and market research, Dr Sehmi brings practical insights to academia. Currently, Dr Sehmi serves as the Assistant Dean and Program Director for the Management Undergraduate programme at the Canterbury Institute of Management. Her research interests include marketing, entrepreneurship, and innovation.</p> <p>Dr Isra Sarfraz holds a PhD in management with research specialisation in graduate employability. She has over 7 years of experience working in the higher education sector in Australia as a lecturer and is currently employed at the Swinburne University of Technology and Canterbury Institute of Management. She teaches undergraduate and postgraduate units in the fields of management, business sustainability, and innovation. She has published in the fields of employability and leadership and is currently researching the use of AI in education and its potential to facilitate learning and teaching processes.</p> <p>Dr Muzammil Hussain holds a PhD in management with a specialisation in leadership and organisational behaviour in the healthcare sector. He is a lecturer and manager curriculum at the Swinburne University of Technology, Melbourne. He is also teaching at the Canterbury Institute of Management. He has corporate experience in various sectors, including healthcare, education, and media. His teaching and research interests include leadership, HRM, organisational behaviour, innovation, and performance management.</p> </aug> <nolink nlid="nl1" bibid="bib72" firstref="ref1"></nolink> <nolink nlid="nl2" bibid="bib16" firstref="ref2"></nolink> <nolink nlid="nl3" bibid="bib34" firstref="ref3"></nolink> <nolink nlid="nl4" bibid="bib77" firstref="ref6"></nolink> <nolink nlid="nl5" bibid="bib21" firstref="ref7"></nolink> <nolink nlid="nl6" bibid="bib30" firstref="ref8"></nolink> <nolink nlid="nl7" bibid="bib68" firstref="ref9"></nolink> <nolink nlid="nl8" bibid="bib67" firstref="ref10"></nolink> <nolink nlid="nl9" bibid="bib50" firstref="ref11"></nolink> <nolink nlid="nl10" bibid="bib31" firstref="ref13"></nolink> <nolink nlid="nl11" bibid="bib10" firstref="ref16"></nolink> <nolink nlid="nl12" bibid="bib19" firstref="ref17"></nolink> <nolink nlid="nl13" bibid="bib71" firstref="ref18"></nolink> <nolink nlid="nl14" bibid="bib73" firstref="ref19"></nolink> <nolink nlid="nl15" bibid="bib18" firstref="ref20"></nolink> <nolink nlid="nl16" bibid="bib56" firstref="ref21"></nolink> <nolink nlid="nl17" bibid="bib20" firstref="ref23"></nolink> <nolink nlid="nl18" bibid="bib45" firstref="ref24"></nolink> <nolink nlid="nl19" bibid="bib57" firstref="ref25"></nolink> <nolink nlid="nl20" bibid="bib12" firstref="ref29"></nolink> <nolink nlid="nl21" bibid="bib61" firstref="ref30"></nolink> <nolink nlid="nl22" bibid="bib36" firstref="ref31"></nolink> <nolink nlid="nl23" bibid="bib51" firstref="ref32"></nolink> <nolink nlid="nl24" bibid="bib35" firstref="ref33"></nolink> <nolink nlid="nl25" bibid="bib39" firstref="ref34"></nolink> <nolink nlid="nl26" bibid="bib28" firstref="ref38"></nolink> <nolink nlid="nl27" bibid="bib70" firstref="ref39"></nolink> <nolink nlid="nl28" bibid="bib75" firstref="ref40"></nolink> <nolink nlid="nl29" bibid="bib33" firstref="ref43"></nolink> <nolink nlid="nl30" bibid="bib76" firstref="ref44"></nolink> <nolink nlid="nl31" bibid="bib38" firstref="ref45"></nolink> <nolink nlid="nl32" bibid="bib66" firstref="ref46"></nolink> <nolink nlid="nl33" bibid="bib14" firstref="ref47"></nolink> <nolink nlid="nl34" bibid="bib25" firstref="ref49"></nolink> <nolink nlid="nl35" bibid="bib17" firstref="ref50"></nolink> <nolink nlid="nl36" bibid="bib62" firstref="ref51"></nolink> <nolink nlid="nl37" bibid="bib64" firstref="ref54"></nolink> <nolink nlid="nl38" bibid="bib26" firstref="ref55"></nolink> <nolink nlid="nl39" bibid="bib49" firstref="ref57"></nolink> <nolink nlid="nl40" bibid="bib52" firstref="ref58"></nolink> <nolink nlid="nl41" bibid="bib41" firstref="ref59"></nolink> <nolink nlid="nl42" bibid="bib63" firstref="ref60"></nolink> <nolink nlid="nl43" bibid="bib24" firstref="ref61"></nolink> <nolink nlid="nl44" bibid="bib40" firstref="ref64"></nolink> <nolink nlid="nl45" bibid="bib37" firstref="ref66"></nolink> <nolink nlid="nl46" bibid="bib53" firstref="ref68"></nolink> <nolink nlid="nl47" bibid="bib22" firstref="ref70"></nolink> <nolink nlid="nl48" bibid="bib23" firstref="ref71"></nolink> <nolink nlid="nl49" bibid="bib15" firstref="ref72"></nolink> <nolink nlid="nl50" bibid="bib44" firstref="ref74"></nolink> <nolink nlid="nl51" bibid="bib32" firstref="ref75"></nolink> <nolink nlid="nl52" bibid="bib46" firstref="ref82"></nolink> <nolink nlid="nl53" bibid="bib74" firstref="ref85"></nolink> <nolink nlid="nl54" bibid="bib55" firstref="ref86"></nolink> <nolink nlid="nl55" bibid="bib60" firstref="ref87"></nolink> <nolink nlid="nl56" bibid="bib27" firstref="ref88"></nolink> <nolink nlid="nl57" bibid="bib54" firstref="ref89"></nolink> <nolink nlid="nl58" bibid="bib47" firstref="ref90"></nolink> <nolink nlid="nl59" bibid="bib29" firstref="ref92"></nolink> <nolink nlid="nl60" bibid="bib58" firstref="ref93"></nolink> <nolink nlid="nl61" bibid="bib13" firstref="ref94"></nolink> <nolink nlid="nl62" bibid="bib42" firstref="ref95"></nolink> <nolink nlid="nl63" bibid="bib65" firstref="ref96"></nolink> <nolink nlid="nl64" bibid="bib48" firstref="ref98"></nolink> <nolink nlid="nl65" bibid="bib11" firstref="ref99"></nolink> <nolink nlid="nl66" bibid="bib59" firstref="ref101"></nolink> <nolink nlid="nl67" bibid="bib69" firstref="ref102"></nolink> <nolink nlid="nl68" bibid="bib43" firstref="ref105"></nolink>
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An: EJ1485752
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PubType: Academic Journal
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: Generative Artificial Intelligence's Integration for Data Analysis in Conducting Academic Research: Understanding the Perspective of Research Supervisors
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Amandeep+Sehmi%22">Amandeep Sehmi</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-1584-6055">0000-0002-1584-6055</externalLink>)<br /><searchLink fieldCode="AR" term="%22Isra+Sarfraz%22">Isra Sarfraz</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-3803-4276">0000-0003-3803-4276</externalLink>)<br /><searchLink fieldCode="AR" term="%22Muzammil+Hussain%22">Muzammil Hussain</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-8188-2014">0000-0001-8188-2014</externalLink>)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Journal+of+Advanced+Academics%22"><i>Journal of Advanced Academics</i></searchLink>. 2025 36(4):788-815.
– Name: Avail
  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
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 28
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2025
– 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="%22Technology+Uses+in+Education%22">Technology Uses in Education</searchLink><br /><searchLink fieldCode="DE" term="%22Technology+Integration%22">Technology Integration</searchLink><br /><searchLink fieldCode="DE" term="%22Data+Analysis%22">Data Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Research%22">Educational Research</searchLink><br /><searchLink fieldCode="DE" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="DE" term="%22Ethics%22">Ethics</searchLink><br /><searchLink fieldCode="DE" term="%22Supervisors%22">Supervisors</searchLink><br /><searchLink fieldCode="DE" term="%22Research+Methodology%22">Research Methodology</searchLink><br /><searchLink fieldCode="DE" term="%22Graduate+Students%22">Graduate Students</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Research%22">Student Research</searchLink><br /><searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink>
– Name: Subject
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Australia%22">Australia</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1177/1932202X251365312
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 1932-202X<br />2162-9536
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This special issue article explores the role of generative artificial intelligence (GenAI) in the data analysis phase of academic research degrees, focusing on its adoption by research students in master's and doctor of philosophy programmes in the business and management disciplines, as viewed through the lens of research supervisors. A qualitative research methodology was adopted, involving semi-structured interviews with research supervisors. The findings revealed that while current familiarity with the use of GenAI tools for data analysis is limited among research supervisors, there is a growing recognition of their potential value and anticipated future acceptance in academic research. The findings of this study recommend the integration of GenAI training modules into research degrees. Furthermore, this study serves as a guide for future research studies exploring the role of GenAI in the data analysis processes of academic research. This study proposes guidelines to raise awareness and educate research students on the ethical use of GenAI, aiming to maintain integrity and enabling them to understand the scope and potential of emerging technologies for data analysis. Emphasising the ethical integration of GenAI, the enhancement of critical thinking, and the development of clear institutional policies are identified as key strategies to support the responsible use of GenAI in research and education.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2025
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1485752
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1485752
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1177/1932202X251365312
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 28
        StartPage: 788
    Subjects:
      – SubjectFull: Artificial Intelligence
        Type: general
      – SubjectFull: Technology Uses in Education
        Type: general
      – SubjectFull: Technology Integration
        Type: general
      – SubjectFull: Data Analysis
        Type: general
      – SubjectFull: Educational Research
        Type: general
      – SubjectFull: Higher Education
        Type: general
      – SubjectFull: Ethics
        Type: general
      – SubjectFull: Supervisors
        Type: general
      – SubjectFull: Research Methodology
        Type: general
      – SubjectFull: Graduate Students
        Type: general
      – SubjectFull: Student Research
        Type: general
      – SubjectFull: Foreign Countries
        Type: general
      – SubjectFull: Australia
        Type: general
    Titles:
      – TitleFull: Generative Artificial Intelligence's Integration for Data Analysis in Conducting Academic Research: Understanding the Perspective of Research Supervisors
        Type: main
  BibRelationships:
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      – PersonEntity:
          Name:
            NameFull: Amandeep Sehmi
      – PersonEntity:
          Name:
            NameFull: Isra Sarfraz
      – PersonEntity:
          Name:
            NameFull: Muzammil Hussain
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          Dates:
            – D: 01
              M: 11
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 1932-202X
            – Type: issn-electronic
              Value: 2162-9536
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            – Type: volume
              Value: 36
            – Type: issue
              Value: 4
          Titles:
            – TitleFull: Journal of Advanced Academics
              Type: main
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