View in EDS HTML Full Text PDF Full Text

Doctoral Students' Reflections on Generative Artificial Intelligence (GenAI) Use in the Literature Review Process

Saved in:
Bibliographic Details
Title: Doctoral Students' Reflections on Generative Artificial Intelligence (GenAI) Use in the Literature Review Process
Language: English
Authors: Swapna Kumar (ORCID 0000-0003-1151-7593), Ariel Gunn (ORCID 0009-0003-9098-927X)
Source: Innovations in Education and Teaching International. 2025 62(4):1395-1408.
Availability: Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
Peer Reviewed: Y
Page Count: 14
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Doctoral Students, Student Attitudes, Artificial Intelligence, Technology Uses in Education, Doctoral Dissertations, Literature Reviews, Information Literacy, Research Skills, Accuracy, Usability, Privacy, Ethics, Technology Integration
DOI: 10.1080/14703297.2024.2427049
ISSN: 1470-3297
1470-3300
Abstract: Amidst discussions about the use of Generative AI (GenAI) for academic research, their potential for doctoral research or literature reviews is an area beginning to be explored. This qualitative study explores doctoral students' perceptions of the value of GenAI tools in the literature review process. The analysis of 26 participants' reflections on their exploration of three GenAI technologies revealed benefits and challenges for the literature review process. Participants highlighted the potential of these tools as complementary to traditional database searches presuming users possess prior information literacy and research skills. They also perceived several challenges such as inaccuracies, usability and privacy and emphasised the importance and indispensability of a human review. This study highlights the need for guidance and strategies for doctoral students on the appropriate and ethical integration of GenAI in literature reviews.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1476857
Database: ERIC
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
    Url: https://content.ebscohost.com/cds/retrieve?content=AQICAHj0k_4E0hTGH8RJwT4gCJyBsGNe_WN95AvKlDbXJGqwxwF1KTydXxLjokWDMckZMfbTAAAA4jCB3wYJKoZIhvcNAQcGoIHRMIHOAgEAMIHIBgkqhkiG9w0BBwEwHgYJYIZIAWUDBAEuMBEEDFTGKDjh2Hi8WJ1H_gIBEICBmrvQtHJV2tJfZNDXA-65A8ztw3CZU1dAXRs40YHzJ5GUP2lg8sDa0shgP8tnWnVZYWennDZDXhkWHGv1LQaRltdwTBO0p_7uDyrS75NSzAiB0DAEfuidMi2iug2RuSX_Ym3qzVJm8yJZUqlhRbokfsqhlbhSJRRW-suyCaYoJuVVqBY4A5XBARxKn6CaVMbnnGd-MCWmyVOippM=
Text:
  Availability: 1
  Value: <anid>AN0186641518;hzj01aug.25;2025Jul17.02:49;v2.2.500</anid> <title id="AN0186641518-1">Doctoral students' reflections on generative artificial intelligence (GenAI) use in the literature review process </title> <p>Amidst discussions about the use of Generative AI (GenAI) for academic research, their potential for doctoral research or literature reviews is an area beginning to be explored. This qualitative study explores doctoral students' perceptions of the value of GenAI tools in the literature review process. The analysis of 26 participants' reflections on their exploration of three GenAI technologies revealed benefits and challenges for the literature review process. Participants highlighted the potential of these tools as complementary to traditional database searches presuming users possess prior information literacy and research skills. They also perceived several challenges such as inaccuracies, usability and privacy and emphasised the importance and indispensability of a human review. This study highlights the need for guidance and strategies for doctoral students on the appropriate and ethical integration of GenAI in literature reviews.</p> <p>Keywords: Generative AI; literature review; doctoral education; dissertation; research</p> <hd id="AN0186641518-2">Introduction</hd> <p>The significance of the literature review in doctoral dissertations and the need for doctoral researchers to understand literature related to their research topic has been highlighted in prior research (Boote & Beile, [<reflink idref="bib3" id="ref1">3</reflink>]). Doctoral students become familiar with prior research, including underlying theories, debates, methodological approaches, and salient authors in their discipline, when they review literature in their research area. The process of writing a literature review – selecting a topic, searching for the literature, identifying relevant literature, critiquing, and synthesising literature (Machi & McEvoy, [<reflink idref="bib13" id="ref2">13</reflink>]) – helps doctoral students develop their research ideas, make connections, contextualise trends and issues, immerse in academic writing, ground their own research, and begin to structure their arguments as they conceptualise their own dissertation research (Green & Bowser, [<reflink idref="bib8" id="ref3">8</reflink>]; Guerin & Aitchison, [<reflink idref="bib9" id="ref4">9</reflink>]). Wisker ([<reflink idref="bib21" id="ref5">21</reflink>]) argues that the literature review is that part of the dissertation where doctoral authors 'engage with theory and theoretical perspectives underlying their research, situating their own contribution to knowledge in established and ongoing dialogues in the field' (p. 64).</p> <p>Given the critical role of the literature review in doctoral dissertations, doctoral programmes often include seminars dedicated to developing information literacy skills, which may include the writing of an annotated bibliography, the development of a literature review matrix, the identification of studies that are most relevant to students' work, the critiquing of literature, and the creation of conceptual frameworks based on the literature (Kumar & Antonenko, [<reflink idref="bib11" id="ref6">11</reflink>]; Neumerski, [<reflink idref="bib14" id="ref7">14</reflink>]; Tolman et al., [<reflink idref="bib19" id="ref8">19</reflink>]). In addition to information literacy skills, familiarity with bibliographic software and citation technologies to manage resources and cite appropriately are foundational for doctoral students. These annotation software and social annotation tools have been found to be useful in helping students critically read, think about literature, co-construct knowledge, and manage annotations (Bjorn et al., [<reflink idref="bib2" id="ref9">2</reflink>]; Kalir & Garcia, [<reflink idref="bib10" id="ref10">10</reflink>]). More recently, the advent of generative AI (GenAI) technologies has provided new opportunities within the literature review process. For example, generative AI technologies for research such as ResearchRabbit.ai, LitMaps.com, or Perplexity.ai enable students to search, filter academic literature, and make connections in a few minutes.</p> <p>Given the key role that information literacy as well as literature review skills play in the formation of researchers, doctoral programmes are challenged with integrating these technologies appropriately in their literature review seminars and within their curriculum. In this article, we explore how GenAI can be integrated and used in the literature review process by doctoral students and their perceptions of the value of these technologies in the process. Our findings provide insight for doctoral curricula and for those engaged in graduate education – doctoral supervisors, doctoral students, supervisors and faculty developers. The term 'dissertation', commonly used in the United States for the culminating research product in PhD and EdD programmes, is used in this paper in place of doctoral 'thesis', the term commonly used in other countries.</p> <hd id="AN0186641518-3">GenAI use in doctoral research</hd> <p>Early literature on the use of GenAI tools for doctoral research addresses both the possibilities and challenges of these technologies. Technologies that help with grammar and spelling (e.g. Grammarly, Quillbot) have been described as useful in the writing process and for writing instruction, especially for non-native speakers, and for specific tasks such as brainstorming or outlining (Barrett & Pack, [<reflink idref="bib1" id="ref11">1</reflink>]; Fan, [<reflink idref="bib5" id="ref12">5</reflink>]; Godwin-Jones, [<reflink idref="bib7" id="ref13">7</reflink>]; Yeo, [<reflink idref="bib22" id="ref14">22</reflink>]). Studies have also concluded that doctoral students' attitudes to ChatGPT use for writing, perceived usefulness and perceived ease of use influenced their intention to use it in their writing (Zou & Huang, [<reflink idref="bib23" id="ref15">23</reflink>], [<reflink idref="bib24" id="ref16">24</reflink>]). Second-language doctoral students also perceived ChatGPT to be a helpful 'virtual tutor and partner', facilitating all aspects of writing, from brainstorming to revising and improving efficiency (Zou & Huang, [<reflink idref="bib23" id="ref17">23</reflink>], p. 8). Students' perceived drawbacks of using ChatGPT included concerns about weakened writing and critical thinking ability; poor quality of writing, both in terms of voice and accuracy; and academic integrity violations. As such, they hoped to receive guidance on 'the ethical, effective and responsible use of ChatGPT' (Zou & Huang, [<reflink idref="bib23" id="ref18">23</reflink>], p. 15).</p> <p>GenAI technologies can serve as a helpful writing assistant at all stages of writing the dissertation, including drafting, editing, and proofreading, as well as 'present dissertation committees with the challenge of how to distinguish between content generated by the doctoral candidate and content generated by AI' (Storey, [<reflink idref="bib18" id="ref19">18</reflink>], p. 2). Recent publications on GenAI use in literature reviews focus on the ways in which large language models (LLMs) such as ChatGPT can be used for literature searches and writing, discuss the accompanying challenges including the poor quality of products, and suggest that such technologies should not be used (Schmidt & Meir, [<reflink idref="bib16" id="ref20">16</reflink>]; Schryen et al., [<reflink idref="bib17" id="ref21">17</reflink>]; van Dis et al., [<reflink idref="bib20" id="ref22">20</reflink>]). The potential for falsified information, lack of credibility, and bias in research conducted with ChatGPT has been discussed by van Dis et al. ([<reflink idref="bib20" id="ref23">20</reflink>]). Schmidt and Meir ([<reflink idref="bib16" id="ref24">16</reflink>]) highlight the various problems with scholarship that is based on GenAI and call for policies and the need to acknowledge the ways in which such technologies were used in publications. These authors emphasise the inability of GenAI to critically synthesise research from the unique perspective of a scholar.</p> <p>The usefulness of GenAI in literature searches is also accompanied by benefits and challenges. Nicholson et al. ([<reflink idref="bib15" id="ref25">15</reflink>]) describe the workings of 'scite.ai' and its usefulness in the literature search process, stating that it can find literature and also provide a 'better understanding how a given paper fits into the broader context of the scientific literature' (p. 894). While acknowledging the benefits of searching for literature using GenAI, Schmidt and Meir ([<reflink idref="bib16" id="ref26">16</reflink>]) cautioned that literature behind a paywall might not be accessed, and that the sources of the results were unclear, pointing to the need for human validation of search results. The need for critical thinking and careful checking by researchers due to non-existent references and false or misleading information in GenAI search results is also reiterated by Schryen et al. ([<reflink idref="bib17" id="ref27">17</reflink>]). They call for a 'nuanced collaboration between GenAI and researchers' where researchers should 'recognize their unique strengths, such as intuition, nuances discerning, and deep critical thinking, as well as GenAI's relative advantages' (p. 4028).</p> <p>Our approach to GenAI use in our doctoral programme was similar in that we wanted doctoral students to benefit from the advantages of GenAI for literature searches but also wanted to emphasise their role in this process and encourage reflective and critical use of GenAI technologies.</p> <hd id="AN0186641518-4">Context for the study</hd> <p>This study was conducted in an online doctoral programme in Educational Technology that enrols professionals across disciplines and diverse educational contexts who conduct research on educational technology innovations. The programme is offered as a cohort model, enrolling a group of 20–25 students every two years. Students complete 5-chapter dissertations that include a literature review where they critically synthesise prior research related to their problem of practice to frame the study and support the design of interventions (Dawson & Kumar, [<reflink idref="bib4" id="ref28">4</reflink>]). Given the interdisciplinary nature of educational technology, students draw upon literature from various domains, for example, their disciplines as well as educational technology, and create a conceptual framework that informs their dissertation. Educational Technology-focused research instruction in the programme is embedded across the curriculum. For instance, information literacy instruction, assignments involving literature searches and the use of bibliographic software (e.g. Zotero) are introduced in the first semester, as part of an initial doctoral seminar. At the end of the first year, students take an online seminar that is focused on the writing of a literature review.</p> <hd id="AN0186641518-5">Literature review seminar</hd> <p>During the online literature review seminar, students search, select, and critically analyse prior research about a research question, culminating in a 25-page literature review and conceptual framework. The process is scaffolded in steps, with students sharing their progress and giving and receiving peer and faculty feedback at regular intervals. Since professional students often return to academia after a hiatus, information literacy skills, facility with academic databases, and academic writing are emphasised during this seminar.</p> <p>An analysis of 69 initial dissertations from the doctoral programme indicated that all the dissertations included a robust literature review that addressed the problem being studied and supported the design of any curricular interventions (Kumar et al., [<reflink idref="bib12" id="ref29">12</reflink>]). We thus concluded that the seminar was providing students with foundational skills that were helpful to their writing of a dissertation literature review. However, given the increased use of GenAI technologies and the discourse around ChatGPT in 2023, we faced a dilemma: to what extent should we integrate GenAI into the literature review seminar?</p> <hd id="AN0186641518-6">Building literature review skills and integrating GenAI technologies</hd> <p>Given our discipline of Educational Technology, it was safe to assume that our students, professionals leading technology integration in their contexts, were already experimenting with these technologies and wrestling with GenAI integration in their professional contexts. The instructor of the online seminar explored existing GenAI technologies for research at the time (e.g. researchrabbit.ai) and experimented with ChatGPT for literature review outlines and for the synthesis of research articles to understand how students might be able to use these technologies in the literature review process. After some reflection on the affordance of these technologies at that time, she decided that for this cohort of doctoral students, it would be wiser not to integrate these technologies in this seminar offering. In a pre-seminar online synchronous session, she emphasised the importance of the literature review as well as the skills acquired during the seminar – information literacy, reading, literature selection, critical analysis, and writing – to the dissertation and to students' development as scholars. Additionally, within the online seminar, she acknowledged the existence and value of these technologies, and explained to the students why they would not use them that semester.</p> <p>These decisions were intentional, because the cohort would take another online course during a subsequent semester with the same instructor that included a focus on scholars' use of social media and emerging technologies, including the use of GenAI technologies for research. This paper focuses on the online module in this course about the use of GenAI technologies for the initial steps of the literature review process.</p> <p>The online module included an overview video from the instructor about a) the purposeful design of this process – where students first acquired the skills to write a literature review without the use of GenAI during their literature seminar but would now experiment with these technologies for literature searches, and b) the importance of prompts and how they needed to be refined to successfully work with GenAI. The module included readings about prompt engineering and the use of GenAI for teaching and learning. For their assignment for the week, students were provided with three GenAI resources, but also given the choice of trying other resources that offered the same features. They were asked to approach this activity as though they had not yet conducted a literature review on their research question in the summer seminar, and to use these technologies to search for prior research and seminal authors that could be used to write their literature review. They were then asked to share their findings and reflect on how these resources might help doctoral students/researchers with literature reviews (or not), what challenges or limitations they perceived, and how these technologies could be integrated into their literature search and review process. An alternative assignment option was offered if any students preferred not to engage with these technologies for any reason (e.g. privacy). All the students chose to complete the assignment as assigned.</p> <hd id="AN0186641518-7">Methodology</hd> <p>A qualitative approach was adopted to explore the following research questions:</p> <p></p> <ulist> <item> In what ways can generative AI be used in the literature review process by doctoral students?</item> <p></p> <item> What are doctoral student perceptions of the value of generative AI to their dissertation literature review?</item> </ulist> <p>After the seminar had ended and institutional review board (IRB) permission was obtained, student submissions (<emph>N</emph> = 26) to the GenAI literature review assignment were downloaded. Although various headings were used and the flow of the submission varied by student, all student submissions followed the assignment instructions and included:</p> <p></p> <ulist> <item> Details of the GenAI technologies that they chose to use and the results of their interactions with the technologies. Additionally, some students explained the functionalities of the technologies.</item> <p></p> <item> Reflections on the results and how these technologies could contribute to their research and literature review process.</item> <p></p> <item> Reflections on how these technologies could help doctoral students in the research process and what limitations they perceived.</item> </ulist> <p>The 26 student submissions were copied into one document and any time stamps, student names, and identifying information were removed to completely deidentify the data. Each student was assigned an identifier number. The constant comparative method (Glaser, [<reflink idref="bib6" id="ref30">6</reflink>]) was used to analyse this data. The data were grouped into three categories according to the sections in each submission, namely, reports of interactions with the GenAI technologies; reflections on the results; and reflections on how these technologies could be useful (or not) to doctoral students or researchers. Three separate documents were created, each containing data for all 26 participants.</p> <p>The coding process was led by a researcher who had not been associated with the course. She first carefully reviewed the data within each category (document), writing memos and on several occasions, noted and moved data that belonged within another category to the relevant document. She then coded data within each category, comparing student responses within that category. The second researcher reviewed the initial round of coding and engaged in another iteration of coding across student responses within the categories. Finally, the researchers met to discuss the codes for each category, refined the codes further, discussed the codes across the categories, and finalised the themes (Table 1).</p> <p>Table 1. Themes and codes.</p> <p> <ephtml> <table><thead><tr><td>Themes</td><td>Codes</td></tr></thead><tbody><tr><td>Perceived benefits to doctoral students</td><td>Facilitates discovery and connections</td></tr><tr><td /><td>Increases efficiency</td></tr><tr><td /><td>Makes the process less intimidating</td></tr><tr><td>Perceived limitations of GenAI</td><td>Quality of literature outputs and summaries</td></tr><tr><td /><td>Usability and features</td></tr><tr><td /><td>Privacy concerns</td></tr><tr><td>Reflections on doctoral students' use of GenAI for literature reviews</td><td>A supplement to library databases, not a primary research tool</td></tr><tr><td /><td>Research skills and a 'human' review necessary</td></tr></tbody></table> </ephtml> </p> <p>Given their discipline of Educational Technology, both the researchers involved in this study continue to explore the appropriate and ethical use of generative AI technologies in teaching and learning. However, no generative AI technologies were used for the literature review, data analysis, or the writing process during this study and the preparation of this manuscript.</p> <hd id="AN0186641518-8">Results</hd> <p>The results detail the ways in which GenAI tools can be used during various parts of the literature review process and report students' perceived benefits and limitations of these technologies for their dissertation literature review.</p> <hd id="AN0186641518-9">GenAI technologies used</hd> <p>Students were provided with the options of exploring Litmaps (<ulink href="http://www.litmaps.com">http://www.litmaps.com</ulink>), Scite (https://www.scite.ai), and Scholarcy (<ulink href="http://www.scholarcy.com">http://www.scholarcy.com</ulink>) or other GenAI tools of their choice. Most students opted to explore the provided tools and some additionally explored Elicit (https://www.elicit.com), Listening (https://www.listening.io), Perplexity (https://www.perplexity.ai), and Research Rabbit (https://<ulink href="http://www.researchrabbit.ai">www.researchrabbit.ai</ulink>) (Table 2).</p> <p>Table 2. GenAI technologies used by students.</p> <p> <ephtml> <table><thead><tr><td>Technology</td><td>Number of students</td></tr></thead><tbody><tr><td>LitMaps</td><td>22</td></tr><tr><td>Scholarcy</td><td>14</td></tr><tr><td>Scite</td><td>9</td></tr><tr><td>ResearchRabbit</td><td>4</td></tr><tr><td>Elicit</td><td>2</td></tr><tr><td>Perplexity.ai</td><td>1</td></tr><tr><td>listening.io.</td><td>1</td></tr></tbody></table> </ephtml> </p> <p>Students found all the technologies easy to use. Several students also briefly described the functionalities of the three suggested tools with which they experimented, to provide context for their reflections.</p> <p>Students explained that <emph>Litmaps</emph> finds 'recurring authors and themes' and connections to other related research articles, displays information 'as seed maps', shows 'the scholarly (and temporal) connections between citations', can categorise according to topics and facilitates a snowball search. They appreciated 'having the ability to decide how you want to organise the literature in a personalised visually pleasing way by dates, most citations, and names' (Student2) and exploring relationships between authors, papers, and concepts or topics through visual maps to easily 'understand the important themes in a field' (Student8). Student11 found it 'serves as a convenient starting point for new projects but also offers an efficient way to explore the literature landscape ... researchers can quickly identify related works and delve deeper into their research topics'.</p> <p>In their exploration of <emph>Scholarcy</emph>, which creates exportable flashcards with key information from the literature, several students found the summary features, intext highlighting, bulleted sticking points, built-in citation links, and comparative analysis feature useful and described it as a timesaving tool. Some experimented with the summary feature by comparing the summary or organisation to their own or attempting to summarise key concepts of multiple research papers. Student23 and Student24 were excited about the options to toggle the length of the summary word count, specify how the summary should be written, export flashcards of interest to MS Excel and specify whether tables and figures should be extracted.</p> <p>Following their exploration of <emph>Scite</emph>, students found that it provided details on metrics and impact factor, and focused on articles that were most cited in the academic community. Student16 described the year filter that 'shows you a visual representation of which time periods have the most results, which makes it very easy to understand the range of your search results'. Other students commented on the literature mapping feature, the short summaries of topics with cited sources and the possibility to find more information about included citations. Student19 found it useful for 'evaluating the scholarly impact of a specific work'.</p> <hd id="AN0186641518-10">Perceived benefits to doctoral students</hd> <p>Students perceived the GenAI technologies they explored as having three major benefits for doctoral students: facilitating discovery and connections, increasing efficiency, and making the process less intimidating.</p> <hd id="AN0186641518-11">Facilitates discovery and connections</hd> <p>Doctoral students found that GenAI can help them discover seminal works, make connections between researchers and articles, conduct snowball searches, and on occasion, determine key themes and gaps in the literature. For instance, Student3 stated, 'I felt that the research process was organic, and visually I was able to identify certain authors and topics from my own research already'. Student8 wrote, 'it was empowering and fascinating to witness the interconnectedness of various research papers, authors, and concepts within my field of study, allowing me to identify key themes and trends at a glance'. Additionally, some students found GenAI useful to see details such as citations from a paper, research contradicting that paper, abstracts and links, but also the bigger picture about the research topic. Student17 explained, 'I could quickly see related citations used in that paper. I could also see supporting statements, any retractions or withdrawals, or corrections. I liked this because this told me quickly if a reference should not be used or if other authors were publishing contradicting findings'. This enabled them to 'understand the scholarly discourse surrounding a paper by evaluating whether citing works support, contradict, or mention specific claims' according to Student6. Similarly, Student19 stated that it was valuable 'knowing whether an article has garnered support, faced contradiction, or been referenced in subsequent research'. Students speculated that GenAI might make it possible for them to identify additional literature or sources through connections and citations that did not emerge in a traditional search.</p> <hd id="AN0186641518-12">Increases efficiency</hd> <p>Students stated that the GenAI tools they explored could streamline the literature review process and save them time by providing an immediate overview of relevant articles and seminal work, summarising individual articles and connections, and helping them identify where to begin their search or reading. Student2 stated, 'One of the biggest complaints is lack of time, so having a way to prioritise how and what to read will help it feel more feasible'. Being able to see how often an article had been cited helped Student3 'assess its impact and relevance'. Student18 believed that GenAI could help a researcher find seminal works quickly, while Student5 stated that this 'allows you to see which articles have the biggest impact'. Other students discussed how this could help them focus their search instead of trying various avenues, and that two of the technologies also integrated with citation managers, like library databases.</p> <hd id="AN0186641518-13">Makes the process less intimidating</hd> <p>Students found that GenAI technologies were generally easy to use to get started with a literature search, with a 'clean' interface and 'helpful documentation'. Student8 stated that in their literature review the previous semester they 'struggled to find a starting point'. Others mentioned that it could help doctoral students 'get unstuck', if they did not know where to start. The quick introduction to the topic, an overview to the topic, and the ability to locate key terms were advantages mentioned by students that made the process less overwhelming than starting with search terms in databases, although they acknowledged limitations to beginning a search with GenAI technologies (detailed later in this paper). Student22 wrote that GenAI tools were 'particularly good at breaking down and presenting complex information in an accessible manner', while Student12 stated that 'looking for relevant articles can be daunting and with the vast amount of information available it can be hard to wade through'.</p> <hd id="AN0186641518-14">Perceived limitations of GenAI</hd> <p>Although participants were excited about the potential of GenAI and one even initially termed it a 'game changer', all the doctoral students pointed out limitations such as the quality of the literature outputs and summaries, usability, and privacy concerns as they experimented with GenAI for literature searches.</p> <hd id="AN0186641518-15">Quality of literature outputs and summaries</hd> <p>Students found that the output from GenAI was not always accurate – GenAI often returned articles that were not connected or relevant to their topic, keywords, and prompts. For instance, Student15 stated, 'The results were very vague, and in some cases, inaccurate' while Student14 wrote, 'It returned articles on an unrelated topic that was not included in the search'. The summaries and synthesis of articles were also sometimes inaccurate and more importantly, did not include key details that doctoral students need for literature reviews. Student8 found that 'they [automated summaries] sometimes oversimplified complex research findings, omitting important nuances and context'. Summaries where the methods section was inaccurate and skewed towards the search terms, and the fact that multiple searches resulted in different outputs, made students question the reliability of GenAI results. Students also reported that the snowball search did not work when probed further, links pointed to unrelated articles, citations were inaccurate, and that the 'references were eclectic and no central authors were directly cited' (Student13). They thus concluded that they could not rely on GenAI for their literature searches.</p> <hd id="AN0186641518-16">Usability and features</hd> <p>Previously, doctoral students in this study had learned to filter their searches within library databases – for example, by date range, context, peer review, and using Boolean operators. Several students stated that filtering within GenAI tools was difficult, challenging, or limiting despite several attempts and prompts, and did not provide the flexibility or ease of library database search filters. Student9 stated, 'I felt I had a better comprehension of my parameters when searching through databases'. Students further elaborated why these filters were important for doctoral researchers writing a literature review. Student3 wrote, 'Often in a literature review, we have to share our research method, including inclusion and exclusion criteria', and Students10 talked about the importance of documenting the search process. Five students emphasised that determining whether the resources are peer-reviewed or not, an option within library databases, was a critical missing filter.</p> <p>Additional challenges reported by all the students were that many features of these technologies were behind a paywall or entailed a paid/premium version, a limited trial, a limited number of articles, or the need for a credit card to proceed. Two students also reported technical issues when exporting results or connecting the GenAI with their bibliographic software, while others stated that it was easy to feel overwhelmed or lost in a 'rabbit hole' while searching.</p> <hd id="AN0186641518-17">Privacy concerns</hd> <p>Some students questioned how their searches, information, or the articles that they uploaded would be used. Student17 reflected,</p> <p>I am skeptical of how my information is used by either program. Both systems had lengthy agreement terms, and I am not sure how my search terms and use will be utilized by the company. I assume ... that they are using all consumer searches to improve the AI functionality, but this still makes me feel uneasy from a privacy perspective.</p> <hd id="AN0186641518-18">Reflections on doctoral students' use of GenAI for literature reviews</hd> <p>Based on the current capabilities of the GenAI technologies reviewed, doctoral students concluded that they could be used to complement traditional searches in library databases, but that research skills and a 'human' review were still essential for the doctoral literature review process.</p> <hd id="AN0186641518-19">A supplement to library databases, not a primary research tool</hd> <p>Students reflected that these technologies had several benefits such as finding related authors and related areas of interest, visualising the literature, and seeing connections between articles. They used words such as 'enhance', 'complement', 'supplement', and 'add' to explain that these technologies were 'another possible avenue to identify articles to be used for a literature review', an addition to their 'research toolkit' and one of the strategies that would complement other search approaches. They emphasised that GenAI should not 'be relied upon as a substantial part of the process' or be used 'as a primary research tool'. The following two student statements synthesise many other comments that were made:</p> <p>These tools would also be a good place to check your work. You could see if you were missing any major relevant works/authors and compare the authors you found in your search among library databases to the databases these tools have access to.</p> <p>(Student18)</p> <p>I can see myself using this tool during dissertation time after I have conducted initial search cycles ... I think these are useful supplemental resources but should not replace the tried-and-true process. Rather, they should be used to fill gaps, make connections, and perhaps help with efficiency.</p> <p>(Student10)</p> <p>Two students reflected that GenAI could also be useful to get an overview of key themes to determine what to read first. They asserted that it was easy for novice researchers to feel overwhelmed, especially when there was a large body of research about a topic. Overall, 17 (65%) students stated that they would use at least one of the GenAI tools they had explored in the future in addition to traditional searches with library databases. The remaining nine (35%) said they did not plan to use it. Their reasons focused on limitations, the unreliability of the technologies, preferences for library databases, and questions about how to document and cite the use of the tools. These students concluded that the current limitations of the GenAI tools meant that they weren't useful 'at the moment'.</p> <hd id="AN0186641518-20">Prior research skills and a 'human' review are necessary</hd> <p>Although many students thought GenAI tools could be useful, they emphasised the importance of 'foundational research knowledge skills' to be able to use GenAI technologies in the literature review process, with some acknowledging that these previously acquired skills had enabled them to evaluate the capabilities of the GenAI technologies. Student19 highlighted the importance of 'exercising scholarly judgement when relying on the generated information', and Student5 explained that 'while such tools can save some time ... , people must still ultimately make decisions about the results'. Many students emphasised the importance of reading the articles rather than relying on summaries, to ensure that the summaries were accurate and didn't exclude important information that the researcher might find pertinent. This was especially true of research specific to professional practice; as Student24 explained, 'there is no replacement for actually reading and interpreting the findings and making connections yourself; AI will not be able to make those inferences and connections with your unique problem of practice'.</p> <p>Students emphasised the human in the loop review and the inability of GenAI to replace human expertise in literature analysis, with Student1 concluding that 'we must continue to use our judgement and expertise alongside AI tools to ensure the most comprehensive and accurate literature reviews possible'. Student25 wondered if the tools would actually save time in the long run because the summaries required checking for 'mistakes made by AI'. Other students concluded that GenAI tools would be useful for specific tasks such as locating additional articles or skimming articles, but that they would read and analyse articles themselves.</p> <hd id="AN0186641518-21">Discussion</hd> <p>A review of the prior literature revealed scant research on the use of GenAI technologies in doctoral research or literature reviews, with the few studies found focusing on their use for writing. This study focused on the potential of three GenAI technologies for the initial stages of the literature review process – searching and identifying relevant literature – not on the writing process. The results indicate that these technologies can help novice researchers identify connections between studies, authors, and topics; more easily acquire a chronological perspective or overview of the field and identify seminal works; and find the process less intimidating to begin. They can discover other terminology used for their research topics and other contexts in which their topics have been studied, and benefit from a visual overview of existing research. Although GenAI technologies might appear to increase efficiency (Storey, [<reflink idref="bib18" id="ref31">18</reflink>]), the inaccuracy and unreliability of results (van Dis et al., [<reflink idref="bib20" id="ref32">20</reflink>]) remains the biggest challenge. This was highlighted by almost all the participants, along with the need for a subscription or payment after a certain number of searches or for advanced features. Equally important, concerns about data privacy, copyright and bias expressed in prior literature (Schmidt & Meir, [<reflink idref="bib16" id="ref33">16</reflink>]; Schryen et al., [<reflink idref="bib17" id="ref34">17</reflink>]) remain a challenge for doctoral researchers. Having previously learned to provide evidence for claims or cite prior research appropriately, participants in this study expressed their inability to use the results without verification and careful checking through searches within library databases.</p> <p>This study demonstrates that GenAI technologies in their current state can be used by novice researchers to complement traditional searches in library databases, and that learning to use these technologies appropriately would supplement their 'research toolkit'. However, information literacy and research skills remain crucial to evaluate and appropriately use the output of these resources in dissertations. Doctoral curricula, workshops, and seminars have to continue to teach the skills essential for reviewing and critiquing research because a human review remains indispensable in the literature review process even when using GenAI technologies. Participants in this study stressed the importance of these skills to identify, select, and critique prior research that was relevant to their doctoral work, especially in professional programmes where students conduct research in their practice, and critical selection and evaluation of prior research specific to context, policy, learners, and other contextual factors is crucial.</p> <p>This study included the experiences of a small sample of professional doctoral students in an online doctoral programme, which may not be transferable to all traditional doctoral contexts. As doctoral students in Educational Technology and professionals in various educational contexts engaged in technology integration, they may be more positively inclined towards integrating new technologies than other students, while possessing significant experience and expertise with respect to usability, technology-based privacy and security, and copyright concerns. Future research could explore the use of GenAI tools by traditional doctoral students across disciplines and in actual literature reviews for dissertations or research. It would also be important to identify at which stages of the literature review process these technologies are most beneficial and what support researchers need to use them ethically and effectively.</p> <p>Additionally, participants explored the free versions of a small number of GenAI technologies for a simulated-graded activity with parameters that limited their focus; had participants implemented with GenAI tools for their actual literature reviews they might have explored and discovered other uses. The use of other technologies or paid versions of the GenAI technologies may have resulted in different experiences. Experienced researchers might also use and perceive these GenAI technologies differently.</p> <p>All researchers bring a unique purpose and expertise that informs their research questions as well as the critical lens through which they critique and synthesise the literature. Overreliance on GenAI tools would hinder the critical analysis required to understand the nuances of research studies and conduct a rigorous literature review. For example, GenAI summaries can be useful to understand what an article is about but can also be false or inaccurate. It is only through reading articles for themselves that students can begin to engage in the critical discourse of their discipline. The easy access to GenAI technologies for research makes it imperative that doctoral programmes, supervisors, and institutions educate novice researchers to use them wisely, emphasise the importance of the human review, and teach the skills needed to critically and appropriately assess their output.</p> <hd id="AN0186641518-22">Conclusion</hd> <p>The question for institutions and programmes in the long run is no longer <emph>whether</emph> to help doctoral students use GenAI in the literature review process, but <emph>how</emph> and <emph>in which parts of the process</emph> they can be used ethically. As such, doctoral programmes, workshops, and seminars must continue to teach the skills essential for reviewing and critiquing research in addition to showing doctoral students how to integrate these technologies effectively. Furthermore, guidelines for the sharing of original research or data analysis within these technologies as well as policies for attribution and copyright are crucial for all researchers, not only those learning how to research. As GenAI technologies evolve rapidly and questions of attribution and privacy abound, institutions can provide guidance to doctoral and experienced researchers by vetting technologies, providing guidelines and policies, and educating them on their ethical use.</p> <hd id="AN0186641518-23">Disclosure statement</hd> <p>No potential conflict of interest was reported by the author(s).</p> <ref id="AN0186641518-24"> <title> References </title> <blist> <bibl id="bib1" idref="ref11" type="bt">1</bibl> <bibtext> Barrett, A., & Pack, A. (2023). Not quite eye to AI: Student and teacher perspectives on the use of generative artificial intelligence in the writing process. International Journal of Educational Technology in Higher Education, 20 (1), 59. https://doi.org/10.1186/s41239-023-00427-0</bibtext> </blist> <blist> <bibl id="bib2" idref="ref9" type="bt">2</bibl> <bibtext> Bjorn, G. A., Quaynor, L., & Burgasser, A. J. (2022). Reading research for writing: Co-constructing core skills using primary literature. Impacting Education: Journal on Transforming Professional Practice, 7 (1), 47 – 58. https://doi.org/10.5195/ie.2022.237</bibtext> </blist> <blist> <bibl id="bib3" idref="ref1" type="bt">3</bibl> <bibtext> Boote, D. N., & Beile, P. (2005). Scholars before researchers: On the centrality of the dissertation literature review in research preparation. Educational Researcher, 34 (6), 3 – 15. https://doi.org/10.3102/0013189X034006003</bibtext> </blist> <blist> <bibl id="bib4" idref="ref28" type="bt">4</bibl> <bibtext> Dawson, K., & Kumar, S. (2014). An analysis of professional practice Ed.D. dissertations in educational technology. Technology Trends, 58 (4), 62 – 72. https://doi.org/10.1007/s11528-014-0770-5</bibtext> </blist> <blist> <bibl id="bib5" idref="ref12" type="bt">5</bibl> <bibtext> Fan, N. (2023). Exploring the effects of automated written corrective feedback on EFL students' writing quality: A mixed-methods study. SAGE Open, 13 (2). https://doi.org/10.1177/21582440231181296</bibtext> </blist> <blist> <bibl id="bib6" idref="ref30" type="bt">6</bibl> <bibtext> Glaser, B. G. (1965). The constant comparative method of qualitative analysis. Social Problems, 12 (4), 436 – 445. https://doi.org/10.2307/798843</bibtext> </blist> <blist> <bibl id="bib7" idref="ref13" type="bt">7</bibl> <bibtext> Godwin-Jones, R. (2022). Partnering with AI: Intelligent writing assistance and instructed language learning. Language Learning & Technology, 26 (2), 5 – 24.</bibtext> </blist> <blist> <bibl id="bib8" idref="ref3" type="bt">8</bibl> <bibtext> Green, R., & Bowser, M. (2006). Observations from the field: Sharing a literature review rubric. Journal of Library Administration, 45 (1–2), 185 – 202. https://doi.org/10.1300/J111v45n01_10</bibtext> </blist> <blist> <bibl id="bib9" idref="ref4" type="bt">9</bibl> <bibtext> Guerin, C., & Aitchison, C. (2021). Doctoral writing and remote supervision: What the literature tells us. Innovations in Education and Teaching International, 58 (6), 624 – 634. https://doi.org/10.1080/14703297.2021.1991429</bibtext> </blist> <blist> <bibtext> Kalir, R. H., & Garcia, A. (2021). Annotation. MIT Press.</bibtext> </blist> <blist> <bibtext> Kumar, S., & Antonenko, P. (2014). Connecting practice, theory and method: Supporting professional doctoral students in developing conceptual frameworks. Technology Trends, 58 (4), 54 – 61. https://doi.org/10.1007/s11528-014-0769-y</bibtext> </blist> <blist> <bibtext> Kumar, S., Dawson, K., Pollard, R., & Jeter, G. (2022). Analyzing theories, conceptual frameworks, and research methods in EdD dissertations. Technology Trends, 66 (4), 721 – 728. https://doi.org/10.1007/s11528-022-00739-4</bibtext> </blist> <blist> <bibtext> Machi, L. A., & McEvoy, B. T. (2022). The literature review: Six steps to success (4th ed.). Sage Publications.</bibtext> </blist> <blist> <bibtext> Neumerski, C. M. (2023). The tension between rigor and relevance: Redesigning EdD applied research methods coursework within an R1 institution. Impacting Education: Journal on Transforming Professional Practice, 8 (2), 36 – 39. https://doi.org/10.5195/ie.2023.361</bibtext> </blist> <blist> <bibtext> Nicholson, J. M., Mordaunt, M., Lopez, P., Uppala, A., Rosati, D., Rodrigues, N. P., Grabitz, P., & Rife, S. C. (2021). Scite: A smart citation index that displays the context of citations and classifies their intent using deep learning. Quantitative Science Studies, 2 (3), 882 – 898. https://doi.org/10.1162/qss_a_00146</bibtext> </blist> <blist> <bibtext> Schmidt, P. G., & Meir, A. J. (2023). Using generative AI for literature searches and scholarly writing: Is the integrity of the scientific discourse in jeopardy? Notices of the American Mathematical Society, 71 (1), 1 – 104. https://doi.org/10.1090/noti2838</bibtext> </blist> <blist> <bibtext> Schryen, G., Marrone, M., & Yang, J. (2024). Adopting generative AI for literature reviews: An epistemological perspective. HICSS 2024: Proceedings of the 57th Hawaii International Conference on System Science, Hawaii, USA. https://hdl.handle.net/10125/106870</bibtext> </blist> <blist> <bibtext> Storey, V. A. (2023). AI technology and academic writing: Knowing and mastering the "craft skills". International Journal of Adult Education and Technology (IJAET), 14 (1), 1 – 15. https://doi.org/10.4018/IJAET.325795</bibtext> </blist> <blist> <bibtext> Tolman, S., Calhoun, D. W., McBrayer, J. S., Patel, N., & Cain, E. J. (2023). Inquiry as practice: The pathway to redesigning an educational leadership doctoral research seminar series. Impacting Education: Journal on Transforming Professional Practice, 8 (2), 40 – 46. https://doi.org/10.5195/ie.2023.329</bibtext> </blist> <blist> <bibtext> van Dis, E. A. M., Bollen, J., Zuidema, W., van Rooij, R., & Bockting, C. L. (2023). ChatGPT: Five priorities for research. Nature, 614 (7947), 224 – 226. https://doi.org/10.1038/d41586-023-00288-7</bibtext> </blist> <blist> <bibtext> Wisker, G. (2015). Developing doctoral authors: Engaging with theoretical perspectives through the literature review. Innovations in Education and Teaching International, 52 (1), 64 – 74. https://doi.org/10.1080/14703297.2014.981841</bibtext> </blist> <blist> <bibtext> Yeo, M. A. (2023). Academic integrity in the age of artificial intelligence (AI) authoring apps. TESOL Journal, 14 (3), e716. https://doi.org/10.1002/tesj.716</bibtext> </blist> <blist> <bibtext> Zou, M., & Huang, L. (2023a). The impact of ChatGPT on L2 writing and expected responses: Voice from doctoral students. Education and Information Technologies, 29 (11), 13201 – 13219. https://doi.org/10.1007/s10639-023-12397-x</bibtext> </blist> <blist> <bibtext> Zou, M., & Huang, L. (2023b). To use or not to use? Understanding doctoral students' acceptance of ChatGPT in writing through technology acceptance model. Frontiers in Psychology, 14, 1259531. https://doi.org/10.3389/fpsyg.2023.1259531</bibtext> </blist> </ref> <aug> <p>By Swapna Kumar and Ariel Gunn</p> <p>Reported by Author; Author</p> <p></p> <p>Swapna Kumar is a Clinical Professor of Educational Technology at the University of Florida, USA. Her research focus is quality in online education, specifically online pedagogy, online programmes, online supervision/mentoring, and recently, the integration of Generative AI in teaching and learning.</p> <p>Ariel Gunn is an instructional designer at the University of Florida. Her research interests include equitable and authentic assessment, and the roles instructional designers play to support effective use of established and emerging technologies.</p> </aug> <nolink nlid="nl1" bibid="bib13" firstref="ref2"></nolink> <nolink nlid="nl2" bibid="bib21" firstref="ref5"></nolink> <nolink nlid="nl3" bibid="bib11" firstref="ref6"></nolink> <nolink nlid="nl4" bibid="bib14" firstref="ref7"></nolink> <nolink nlid="nl5" bibid="bib19" firstref="ref8"></nolink> <nolink nlid="nl6" bibid="bib10" firstref="ref10"></nolink> <nolink nlid="nl7" bibid="bib22" firstref="ref14"></nolink> <nolink nlid="nl8" bibid="bib23" firstref="ref15"></nolink> <nolink nlid="nl9" bibid="bib24" firstref="ref16"></nolink> <nolink nlid="nl10" bibid="bib18" firstref="ref19"></nolink> <nolink nlid="nl11" bibid="bib16" firstref="ref20"></nolink> <nolink nlid="nl12" bibid="bib17" firstref="ref21"></nolink> <nolink nlid="nl13" bibid="bib20" firstref="ref22"></nolink> <nolink nlid="nl14" bibid="bib15" firstref="ref25"></nolink> <nolink nlid="nl15" bibid="bib12" firstref="ref29"></nolink>
Header DbId: eric
DbLabel: ERIC
An: EJ1476857
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Doctoral Students' Reflections on Generative Artificial Intelligence (GenAI) Use in the Literature Review Process
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Swapna+Kumar%22">Swapna Kumar</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-1151-7593">0000-0003-1151-7593</externalLink>)<br /><searchLink fieldCode="AR" term="%22Ariel+Gunn%22">Ariel Gunn</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0003-9098-927X">0009-0003-9098-927X</externalLink>)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Innovations+in+Education+and+Teaching+International%22"><i>Innovations in Education and Teaching International</i></searchLink>. 2025 62(4):1395-1408.
– Name: Avail
  Label: Availability
  Group: Avail
  Data: Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 14
– 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="%22Doctoral+Students%22">Doctoral Students</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Attitudes%22">Student Attitudes</searchLink><br /><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="%22Doctoral+Dissertations%22">Doctoral Dissertations</searchLink><br /><searchLink fieldCode="DE" term="%22Literature+Reviews%22">Literature Reviews</searchLink><br /><searchLink fieldCode="DE" term="%22Information+Literacy%22">Information Literacy</searchLink><br /><searchLink fieldCode="DE" term="%22Research+Skills%22">Research Skills</searchLink><br /><searchLink fieldCode="DE" term="%22Accuracy%22">Accuracy</searchLink><br /><searchLink fieldCode="DE" term="%22Usability%22">Usability</searchLink><br /><searchLink fieldCode="DE" term="%22Privacy%22">Privacy</searchLink><br /><searchLink fieldCode="DE" term="%22Ethics%22">Ethics</searchLink><br /><searchLink fieldCode="DE" term="%22Technology+Integration%22">Technology Integration</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1080/14703297.2024.2427049
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 1470-3297<br />1470-3300
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Amidst discussions about the use of Generative AI (GenAI) for academic research, their potential for doctoral research or literature reviews is an area beginning to be explored. This qualitative study explores doctoral students' perceptions of the value of GenAI tools in the literature review process. The analysis of 26 participants' reflections on their exploration of three GenAI technologies revealed benefits and challenges for the literature review process. Participants highlighted the potential of these tools as complementary to traditional database searches presuming users possess prior information literacy and research skills. They also perceived several challenges such as inaccuracies, usability and privacy and emphasised the importance and indispensability of a human review. This study highlights the need for guidance and strategies for doctoral students on the appropriate and ethical integration of GenAI in literature reviews.
– 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: EJ1476857
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1476857
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1080/14703297.2024.2427049
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 14
        StartPage: 1395
    Subjects:
      – SubjectFull: Doctoral Students
        Type: general
      – SubjectFull: Student Attitudes
        Type: general
      – SubjectFull: Artificial Intelligence
        Type: general
      – SubjectFull: Technology Uses in Education
        Type: general
      – SubjectFull: Doctoral Dissertations
        Type: general
      – SubjectFull: Literature Reviews
        Type: general
      – SubjectFull: Information Literacy
        Type: general
      – SubjectFull: Research Skills
        Type: general
      – SubjectFull: Accuracy
        Type: general
      – SubjectFull: Usability
        Type: general
      – SubjectFull: Privacy
        Type: general
      – SubjectFull: Ethics
        Type: general
      – SubjectFull: Technology Integration
        Type: general
    Titles:
      – TitleFull: Doctoral Students' Reflections on Generative Artificial Intelligence (GenAI) Use in the Literature Review Process
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Swapna Kumar
      – PersonEntity:
          Name:
            NameFull: Ariel Gunn
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 1470-3297
            – Type: issn-electronic
              Value: 1470-3300
          Numbering:
            – Type: volume
              Value: 62
            – Type: issue
              Value: 4
          Titles:
            – TitleFull: Innovations in Education and Teaching International
              Type: main
ResultId 1