The DOI matches its Crossref record; 54 of the 55 references it cites were found in a registry or an index; 3 of the 23 sources its claims cite were checked for support.
GenAI et al.: Cocreation, Authorship, Ownership, Academic Ethics and Integrity in a Time of Generative AI
Open accessAbstract
This paper investigates the complex interplay between generative artificial intelligence (AI) and human intellect in academic writing and publishing. It examines the ‘organic versus synthetic’ paradox, emphasizing the implications of using generative AI tools in educational and academic integrity contexts. The paper critiques the prevalent ‘publish or perish’ culture in academia, highlighting the need for systemic reevaluation due to generative AI’s emerging role in academic writing and reporting. It delves into the legal and ethical challenges of authorship and ownership, especially in relation to copyright laws and AI-generated content. The paper discusses generative AI’s diverse roles and advocates for transparent reporting to uphold academic integrity. Additionally, it calls for a broader examination of generative AI tools and stresses the need for new mechanisms to identify generative AI use and ensure adherence to academic integrity and ethics. The implications of generative AI are also explored, suggesting the need for innovative AI-inclusive strategies in academia. The paper concludes by emphasizing the significance of generative AI in various information-processing domains, highlighting the urgency to adapt and transform academic practices in an era of rapid generative AI-driven change.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This position paper examines generative AI’s impact on academic writing, authorship, ownership, and integrity, arguing that AI should not be credited as a co-author and that human authors remain responsible. It proposes the aiTARAS framework for transparent, multilayered disclosure of generative AI use.
Key findings
- Argues generative AI should not be credited as a co-author because authorship requires specific criteria; Table 1 cites ICMJE’s four criteria: substantial contributions, drafting/reviewing, final approval, and accountability. It notes some have credited AI as co-author, while others treat it as a ghost contributor.
- Highlights legal and ownership grey areas: under the 1976 US Copyright Law requiring human authorship, AI-generated works are ineligible for registration and lack copyright protection, potentially entering the public domain immediately (citing Schroeder, 2023); only humans can bear legal responsibility.
- Identifies academic integrity risks: generative AI can enhance efficiency but also enable misconduct and plagiarism; academic integrity is defined by six fundamental values—honesty, trust, fairness, respect, responsibility, and courage. It states there is no effective working solution to unattributed sourcing from unpublished online materials and that banning generative AI is not viable.
- Proposes the aiTARAS framework for disclosure, with categories such as direct contribution, general assistance, specific sections, idea development, editing/reviewing, translation/localization, data analysis, data visualization, and code/algorithms, plus a final human approval statement; primary responsibility rests with the human author.
Methods
- Design: critical/position paper offering a conceptual examination of generative AI in academic writing and publishing, including cocreation, authorship, ownership, ethics, integrity, and reporting.
- Data: no datasets were generated or analysed; the Data availability statement says data sharing is not applicable. The analysis draws on cited literature and policy documents, including ICMJE, COPE, and CSE.
- Main analysis: argumentative synthesis and proposal of the aiTARAS framework with sample disclosure statements; no empirical testing or systematic sampling is described.
Limitations
- Stated: analyses of many papers focus predominantly on ChatGPT; relying on a single example for generalization can mislead, and a wider array of generative AI tools/examples is needed. Also stated: detection/similarity tools are not 100% accurate and often produce false positives, so new mechanisms are needed to identify AI use and violations.
- Stated: discussions often assume English is the native language of all researchers; researchers using generative AI for translation, proofreading, or editing face dilemmas about originality versus AI-produced content.
- Evident from the text: no empirical data, sample, or systematic review is presented; the aiTARAS framework is proposed but not tested or evaluated.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
3 of 23 cited sources checked · 0 supported · 0 flagged · accuracy not yet measured
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Therefore, there is a growing consensus on the necessity to verify human contribution, originality, and authenticity in academic outputs (Lee, 2023;Perera Molligoda Arachchige & Stomeo, 2023).”
§ Integrity in a Time of Generative AI > "A lack of transparency results in distrust and a deep sense of insecurity." -Dalai Lama
What the sources say
- Partly supportedcites Lee, 2023 sourceExperimental — accuracy not yet measured
The Conclusion (page not provided) says the authenticity of AI-generated text must be verified, and the Introduction reports journal policies requiring disclosure of AI use and accountability for authorship. The copyright-law section discusses human authorship, but the paper does not establish a growing consensus that human contribution and originality must also be verified across academic outputs. (LLM, conf=0.84)
- Partly supportedcites Perera Molligoda Arachchige & Stomeo, 2023 sourceExperimental — accuracy not yet measured
In the “Dear Editor” section (page unavailable), the authors call for acknowledging ChatGPT use, preserving human expertise, and upholding academic integrity and transparency. That supports concern about human contribution, but the paper does not establish a growing consensus or specifically call for verification of originality and authenticity in academic outputs. (LLM, conf=0.84)
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Though generative AI use can technically be detected (Herbold et al., 2023), the focus is on encouraging authors to be transparent about the extent of its use (Tang, 2023b).”
§ Integrity in a Time of Generative AI > "A lack of transparency results in distrust and a deep sense of insecurity." -Dalai Lama
What the sources say
- Partly supportedcites Herbold et al., 2023 sourceExperimental — accuracy not yet measured
The Discussion (page unavailable) identifies measurable linguistic differences between ChatGPT and human-written essays, and Related work reports that some human raters could identify AI-generated answers. These passages support the possibility of distinguishing some AI-generated text, but the paper does not establish a technical method for detecting generative-AI use in authors’ writing generally. (LLM, conf=0.78)
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Partly supported
In the paper
“Currently, there appears to be no effective working solution to this issue (Dien, 2023).”
§ Integrity in a Time of Generative AI > "A lack of transparency results in distrust and a deep sense of insecurity." -Dalai Lama
What the sources say
- Not checked — no full text of the sourcecites Dien, 2023 source
Load-bearingStrength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“However, the fact that generative AI learns and produces responses based on pre-existing work introduces serious concerns about the ownership of such content, creating a grey area (Bakla, 2023;Eke, 2023).”
§ Integrity in a Time of Generative AI > OWNERSHIP AND AUTHORSHIP: GEN AI IS THE NEW KID ON THE BLOCK WHO OWNS WHAT?
What the sources say
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesNone of the sources cited for this claim have been checked yet.
In the paper
“One of the primary concerns with generative AI is its ability and the degree to source ideas from unpublished online materials without proper attribution (Dien, 2023;Weissman, 2023).”
§ Integrity in a Time of Generative AI > "A lack of transparency results in distrust and a deep sense of insecurity." -Dalai Lama
What the sources say
- Not checked — no full text of the sourcecites Dien, 2023 source
- Not matchedcites Weissman, 2023
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn the paper
“Another notable problem is the unedited use of AI-generated outputs as if they were the original work of the researcher (Bhatia & Kulkarni, 2023).”
§ Integrity in a Time of Generative AI > "A lack of transparency results in distrust and a deep sense of insecurity." -Dalai Lama
What the sources say
- Not checked — no full text of the sourcecites Bhatia & Kulkarni, 2023 source
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
- Probably not a substantial claim. 8
- It may state two separate claims as one. 4
In the paper
“Generative AI, when expertly trained, exemplifies a potent tool capable of learning, unlearning, and relearning which makes it a continually adapting to the evolving entity (Bozkurt, 2023b).”
§ Integrity in a Time of Generative AI > Abstract
What the sources say
- Not matchedcites Bozkurt, 2023b
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“While generative AI has the potential to enhance effectiveness and efficiency in academic endeavors, it also presents opportunities for misuse in the form of academic misconduct and plagiarism (Bin-Nashwan et al., 2023;Currie, 2023).”
§ Integrity in a Time of Generative AI > CAN GENERATIVE AI BE CREDITED AS A CO-AUTHOR?
What the sources say
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Generative AI is considered a disruptive technology in many ways, but more importantly because of its ability to utilise the most sophisticated technology ever invented by humans, namely language (Bozkurt, 2023b;Harari, 2023).”
§ Integrity in a Time of Generative AI > Abstract
What the sources say
- Not matchedcites Bozkurt, 2023b
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites Harari, 2023
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
- Not matched
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“Given the near-impossibility of avoiding the use of generative AI tools in education, owing to their ease of use and usefulness (Davis, 1989), banning these technologies is not considered as a viable solution (Eke, 2023;Kirwan, 2023).”
§ Integrity in a Time of Generative AI > "A lack of transparency results in distrust and a deep sense of insecurity." -Dalai Lama
What the sources say
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“However, the fact that generative AI learns and produces responses based on pre-existing work introduces serious concerns about the ownership of such content, creating a grey area (Bakla, 2023;Eke, 2023).”
§ Integrity in a Time of Generative AI > OWNERSHIP AND AUTHORSHIP: GEN AI IS THE NEW KID ON THE BLOCK WHO OWNS WHAT?
What the sources say
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“This unique characteristic of generative AI, being non-human, suggests a potential for ownership-free educational content (Bozkurt, 2023c).”
§ Integrity in a Time of Generative AI > OWNERSHIP AND AUTHORSHIP: GEN AI IS THE NEW KID ON THE BLOCK WHO OWNS WHAT?
What the sources say
- Not matchedcites Bozkurt, 2023c
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
In the paper
“However, considering that academic writing is fundamentally a creative process aimed at producing original and authentic work (Bozkurt, 2023d), this creates a critical juncture.”
§ Integrity in a Time of Generative AI > "A lack of transparency results in distrust and a deep sense of insecurity." -Dalai Lama
What the sources say
- Not matchedcites Bozkurt, 2023d
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“With the ability of generative AI to produce sophisticated and seemingly authentic work, traditional forms of assessment, such as written essays or reports, may no longer be sufficient to gauge a student's true understanding or original thinking.”
§ Integrity in a Time of Generative AI > FINAL REMARKS
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
- Rests on an assumption: Many academic outputs, such as essays, are used for assessment and evaluation.
None of the sources cited for this claim have been checked yet.
In the paper
“Another perspective posits that, as only humans can bear legal responsibility for their creations, they should be the sole entities recognized as authors (Nature, 2023;Stokel-Walker, 2023).”
§ Integrity in a Time of Generative AI > OWNERSHIP AND AUTHORSHIP: GEN AI IS THE NEW KID ON THE BLOCK WHO OWNS WHAT?
What the sources say
- Not matchedcites Nature, 2023
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked — no full text of the sourcecites Stokel-Walker, 2023 source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✗ Asserted: fails✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The authors may not put it forward as true. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In some cases, merely reporting the use of generative AI is inadequate.”
§ Integrity in a Time of Generative AI > "Ancora Imparo" Michelangelo
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Relying on a single example for generalization can be misleading and potentially misguide us.”
§ Integrity in a Time of Generative AI > FINAL REMARKS
Strength:plain· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper relies on this without support.
Generative AI systems are designed to comprehend and replicate human language.
“These generative AI systems, developed to analyze complex patterns and structures in human language, are primarily designed to comprehend and replicate it.”
The paper relies on this without support.
The analyses of many papers examined predominantly focus on one generative AI tool, ChatGPT.
“It is also crucial to acknowledge that the analyses of many papers examined predominantly focus on one generative AI tool, ChatGPT.”
The authors state this as an assumption.
The frequent assumption in discussions on academic ethics that English is the native language of all researchers.
“An additional point of consideration is the frequent assumption in discussions on academic ethics that English is the native language of all researchers.”
The paper relies on this without support.
Researchers proficient in various native languages often utilize generative AI for translation, proofreading, or editing.
“It is important to remember that researchers proficient in various native languages often utilize generative AI for translation, proofreading, or editing.”
The paper relies on this without support.
Many academic outputs, such as essays, are used for assessment and evaluation.
“Lastly, many academic outputs, such as essays, are used for assessment and evaluation.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Among university teachers, academic integrity and cheating are major concerns (Bozkurt, 2024;Johnston et al., 2024;Lee et al., 2024;Newell et al., 2024;Overono & Ditta, 2025;Perkins et al., 2024;Xia et al., 2024;Yusuf et al., 2024).”
From Toward Partnership Ethics for Generative Artificial Intelligence in Higher Education (2026) · cites this paper as Bozkurt, 2024
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“As in other empirical contexts (e.g., Bozkurt, 2024;Johnston et al., 2024;Lee et al., 2024;Newell et al., 2024;Yusuf et al., 2024), academic integrity was a focus, especially for teachers.”
From Toward Partnership Ethics for Generative Artificial Intelligence in Higher Education (2026) · cites this paper as Bozkurt, 2024
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Research participants' concerns included and extended beyond academic integrity, the topic that dominates much academic research on genAI in higher education (Bozkurt, 2024;Johnston et al., 2024;Lee et al., 2024;Newell et al., 2024;Overono & Ditta, 2025;Perkins et al., 2024;Xia et al., 2024;Yusuf et al., 2024).”
From Toward Partnership Ethics for Generative Artificial Intelligence in Higher Education (2026) · cites this paper as Bozkurt, 2024
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“This pattern is consistent with broader arguments that authorship in the age of generative AI cannot be settled by detecting tool use alone, but depends on who can take accountability for the content (Bozkurt, 2024).”
From From production to verification: generative AI, doctoral formation, and the leadership of digital education (2026) · cites this paper as Bozkurt, 2024
Citations
Cites
Perceived usefulness, perceived ease of use, and user acceptance of information technology
Davis, F. D. (1989) · MIS Quarterly · doi:10.2307/249008 · Cited by 68,483
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., et al. (2023) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2023.102642 · Cited by 4,584
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Chatting and cheating: Ensuring academic integrity in the era of ChatGPT
Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2024) · Innovations in Education and Teaching International · doi:10.1080/14703297.2023.2190148 · Cited by 3,087
Chatting and Cheating. Ensuring academic integrity in the era of ChatGPT
Cotton, D., Cotton, P., & Shipway, J. R. (2023) · doi:10.35542/osf.io/mrz8h · Cited by 3,079
ChatGPT is fun, but not an author
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ChatGPT listed as author on research papers: many scientists disapprove
Stokel-Walker, C. (2023) · Nature · doi:10.1038/d41586-023-00107-z · Cited by 868
Tools such as ChatGPT threaten transparent science; here are our ground rules for their use
(2023) · Nature · doi:10.1038/d41586-023-00191-1 · Cited by 605
Flagged first, then by citations
Research works printed but not linked: 3
- CSE. (2022). Recommendations for promoting integrity in scientific journal publications. Council of Science Editors. www.councilscienceeditors.org/recommendations-for-promoting-integrityin-Scientific-journal-publications
- ICMJE. (2022). Recommendations. International Committee of Medical Journal Editors. www.icmje.org/icmje-recommendations.pdf
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Cited by
Sorted by citations
Bozkurt, A., Xiao, J., Farrow, R., Bai, J. Y. H., Nerantzi, C., Moore, S. L., Dron, J., Stracke, C. M., Singh, L., Crompton, H., et al. (2024) · Open Praxis · doi:10.55982/openpraxis.16.4.777 · Cited by 320
Self-citationBalalle, H., & Pannilage, S. (2025) · Social Sciences & Humanities Open · doi:10.1016/j.ssaho.2025.101299 · Cited by 137
Bozkurt, A. (2024) · Open Praxis · doi:10.55982/openpraxis.16.3.739 · Cited by 105
Self-citationMaphoto, K. B., Sevnarayan, K., Mohale, N. E., Suliman, Z., Ntsopi, T. J., & Mokoena, D. (2024) · Open Praxis · doi:10.55982/openpraxis.16.2.649 · Cited by 129
Tell Me Your Prompts and I Will Make Them True: The Alchemy of Prompt Engineering and Generative AI
Bozkurt, A. (2024) · Open Praxis · doi:10.55982/openpraxis.16.2.661 · Cited by 87
Self-citationSymeou, L., Louca, L. Τ., Kavadella, A., Mackay, J., Danidou, Y., & Raffay, V. I. (2025) · European Journal Of Dental Education · doi:10.1111/eje.13069 · Cited by 87
“Adding to this, while ChatGPT has a clear policy that the copyright of generated materials belongs to the user, it is not clear that this is the case in all other current and future GenAI models [51, 52].”
Integrating generative AI in university teaching and learning: A model for balanced guidelines
Cacho, R. (2024) · Online Learning · doi:10.24059/olj.v28i3.4508 · Cited by 59
“He then delves into the multifaceted roles of generative AI and promotes the necessity of open disclosure to ensure academic integrity (Bozkurt, 2024).”
BackgroundMapping Tomorrow’s Teaching and Learning Spaces: A Systematic Review on GenAI in Higher Education
Tillmanns, T., Filho, A. S., Rudra, S., Weber, P., Dawitz, J., Wiersma, E., Dudenaite, D., & REYNOLDS, S. L. (2025) · Trends in Higher Education · doi:10.3390/higheredu4010002 · Cited by 88
“Bozkurt [55] adds that mastering the art of GenAI prompting and nuanced editing has become a new cornerstone for intellectual expression, highlighting that while AI can aid creativity, human agency and expertise remain central.”
Tejeda, A. S., Oller, J. C. D., Escandell, J. M. B., Gómez-Díaz, R., & Rodríguez, A. G. (2026) · Frontiers in Education · doi:10.3389/feduc.2025.1711718 · Cited by 111
“…need for digital literacy activities and the practice of guidelines for formulating accurate prompts (Ortega-Rodríguez and Pericacho-Gómez, 2025), in fact, the design of instructions for AI ( prompting) is going to become one of the essential competencies (Eager and Brunton, 2023; Bozkurt, 2024).”
InfluentialXiao, J., Bozkurt, A., Nichols, M., Pazurek, A., Stracke, C. M., Bai, J. Y. H., Farrow, R., Mulligan, D., Nerantzi, C., Sharma, R. C., et al. (2025) · TechTrends · doi:10.1007/s11528-025-01060-6 · Cited by 62
“Questions such as these need to be addressed but remain mostly unsolved (Bozkurt, 2024a; Floridi, 2024).”
InfluentialSelf-citationSwindell, A., Greeley, L., Farag, A., & Verdone, B. (2024) · Online Learning · doi:10.24059/olj.v28i2.4438 · Cited by 45
“…researchers of online, blended, and in-person learning are still coming to grips with how to employ current AI technologies in the learning experience, let alone understanding the potential consequences that future and unknown developments in AI will produce (Bozkurt, 2024; Şenocak et al., 2024).”
BackgroundArar, K. H., Özen, H., Polat, G., & Turan, S. (2025) · Smart Learning Environments · doi:10.1186/s40561-025-00403-3 · Cited by 53
“The study especially highlights the effort toward reinforcement of academic integrity via AI plagiarism detection (Bin-Nashwan et al., 2023) and article authorship directives (Bozkurt, 2024).”
Potential Societal Biases of ChatGPT in Higher Education: A Scoping Review
Li, M., Enkhtur, A., Yamamoto, B. A., Cheng, F., & Chen, L. (2025) · Open Praxis · doi:10.55982/openpraxis.17.1.750 · Cited by 57
Exploring the Ethical Implications of Using Generative AI Tools in Higher Education
Đerić, E., Frank, D., & Vuković, D. (2025) · Informatics · doi:10.3390/informatics12020036 · Cited by 53
Pratiwi, H., Suherman, Hasruddin, & Ridha, M. (2025) · European Journal of Education · doi:10.1111/ejed.70083 · Cited by 31
Inventing with Machines: Generative AI and the Evolving Landscape of IS Research
Gopal, R. D., Li, J., Riemer, K., Sarker, S., Singh, P. V., Susarla, A., Bichler, M., & Thatcher, J. B. (2025) · Information systems research · doi:10.1287/isre.2025.editorial.v36.n4 · Cited by 35
“When AI systems blur the lines of intellectual authorship (Bozkurt 2024), propagate synthetic or misattributed evidence, introduce subtle but systematic biases (Xu et al. 2024), or gradually erode core scholarly practices through over-reliance, they threaten the very integrity they promise to…”
Exploring the Effectiveness of AI Course Assistants on the Student Learning Experience
Hanshaw, G., Vance, J., & Brewer, C. (2024) · Open Praxis · doi:10.55982/openpraxis.16.4.719 · Cited by 27
Applications of Artificial Intelligence in Psychiatry and Psychology Education: Scoping Review
Prégent, J., Chung, V.-H.-A., Adib, I. E., Désilets, M., & Hudon, A. (2025) · JMIR Medical Education · doi:10.2196/75238 · Cited by 40
Generative AI Governance Model in Educational Research
de Pinho, I. G., Costa, A. P., & Pinho, C. S. B. (2025) · Frontiers in Education · doi:10.3389/feduc.2025.1594343 · Cited by 33
Güneş, A., & Kaban, A. L. (2025) · Higher Education Quarterly · doi:10.1111/hequ.70057 · Cited by 25
Behind the Scenes: A Critical Perspective on GenAI and Open Educational Practices
Aksoy, D. A., & Kurşun, E. (2024) · Open Praxis · doi:10.55982/openpraxis.16.3.674 · Cited by 30
Sabbaghan, S., & Eaton, S. E. (2025) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-024-00454-6 · Cited by 46
“…importance of human judgment and critical thinking in navigating the use of these tools underscore the need for robust ethical frameworks and institutional policies to guide the responsible integration of GenAI in academic research (Alier et al., 2024; Bozkurt, 2024; Fanni et al., 2023; Kim, 2023).”
InfluentialKangwa, D., Msafiri, M. M., & Fute, A. (2025) · Journal of Computer Assisted Learning · doi:10.1111/jcal.70109 · Cited by 22
Suliman, Z., Mohale, N. E., Maphoto, K. B., & Sevnarayan, K. (2024) · Discover Education · doi:10.1007/s44217-024-00289-2 · Cited by 28
Getto, G., Kelley, S., & Vance, B. (2025) · Journal of Technical Writing and Communication · doi:10.1177/00472816251332208 · Cited by 19
Addressing Academic Dishonesty in Higher Education: A Systematic Review of Generative AI’s Impact
Sharma, R. C., & Panja, S. K. (2025) · Open Praxis · doi:10.55982/openpraxis.17.2.820 · Cited by 30
Onto-epistemological Understandings of Generative Artificial Intelligence in Education
Creely, E., & Janssen, K. (2025) · International Journal of Changes in Education · doi:10.47852/bonviewijce52024380 · Cited by 23
Yavuz, M., Balat, Ş., & Kayalı, B. (2025) · Open Praxis · doi:10.55982/openpraxis.17.2.811 · Cited by 21
Simsek, E., & Yılmaz, T. K. (2025) · Open Praxis · doi:10.55982/openpraxis.17.1.692 · Cited by 34
Lanfranchi, G., Cioli, A., Amanti, A., & Marinelli, L. (2025) · European Journal of Innovation Management · doi:10.1108/ejim-03-2024-0317 · Cited by 23
A Systematic-Narrative Review of Online Proctoring Systems and a Case for Open Standards
Heinrich, E. (2025) · Open Praxis · doi:10.55982/openpraxis.17.3.836 · Cited by 14
Slimi, Z. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1769680 · Cited by 24
Renewable Assignments: Comparing Faculty and Student Perceptions
Lazzara, J., Bloom, M., & Clinton‐Lisell, V. (2024) · Open Praxis · doi:10.55982/openpraxis.16.4.706 · Cited by 13
Generative AI-Based Tutoring for Enhancing Learning Engagement and Achievement
Belawati, T., & Prasetyo, D. (2025) · Open Praxis · doi:10.55982/openpraxis.17.2.902 · Cited by 19
Appliances of Generative AI-Powered Language Tools in Academic Writing: A Scoping Review
Raitskaya, L., & Тихонова, Е. В. (2024) · Journal of language and Education · doi:10.17323/jle.2024.24181 · Cited by 23
Liang, H., & Mao, X. (2025) · International Journal of Educational Development · doi:10.1016/j.ijedudev.2025.103434 · Cited by 12
Lazăr, A. M., Repanovici, A., Popa, D., Ionaș, D. G., & Dobrescu, A. I. (2024) · Education Sciences · doi:10.3390/educsci14111239 · Cited by 11
Sok, S., & Heng, K. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4806030 · Cited by 14
Jiang, N., & Meng, N. (2025) · SAGE Open · doi:10.1177/21582440251396497 · Cited by 12
“Taken together, these gaps highlight the need for an integrative framework that considers dispositional (digital mindset), cognitive (sensemaking), affective (trust), and contextual (institutional support) factors (Bozkurt, 2024; Dwivedi et al., 2023).”
Bridging the Gap: ChatGPT’s Role in Enhancing STEM Education
Luan, L., Lin, X. Y., & Dai, Y. (2025) · Open Praxis · doi:10.55982/openpraxis.17.1.685 · Cited by 18
Rampelt, F., Ruppert, R., Schleiß, J., Mah, D.-K., Bata, K., & Egloffstein, M. (2025) · Open Praxis · doi:10.55982/openpraxis.17.1.766 · Cited by 17
AI as a Research Partner: Advocating for Co-Authorship in Academic Publications
Dempere, J. M., Ramasamy, L. K., & Harris, J. (2025) · The Artificial Intelligence Business Review · doi:10.64044/j8enmv71 · Cited by 8
Balkır, N. B., & TOPKAYA, E. (2025) · Journal of Academic Ethics · doi:10.1007/s10805-025-09632-0 · Cited by 7
“The implications extend to scholarly publications, where verifying genuine authorship has become increasingly complex (Bozkurt, 2024).”
Bozkurt, A. (2025) · Open Praxis · doi:10.55982/openpraxis.17.2.792 · Cited by 7
Self-citationBozkurt, A. (2025) · Open Praxis · doi:10.55982/openpraxis.17.4.1058 · Cited by 7
Self-citationEducational integrity in GenAI-augmented assessment: making judgement visible
Sharma, S. (2026) · International Journal for Educational Integrity · doi:10.1007/s40979-026-00216-y · Cited by 13
Bozkurt, A., Uçar, H., & Koçdar, S. (2025) · Open Praxis · doi:10.55982/openpraxis.17.1.896 · Cited by 7
Self-citationSoko, J. J. (2025) · Journal of the Kenya National Commission for UNESCO · doi:10.62049/jkncu.v5i1.426 · Cited by 6
van Wyk, M. M. (2025) · Open Praxis · doi:10.55982/openpraxis.17.1.711 · Cited by 8
An Experimental Study of the Effect of AI Course Assistants on Student Grade Outcomes
Hanshaw, G., Wicker, E., & Denlinger, K. (2025) · Open Praxis · doi:10.55982/openpraxis.17.2.772 · Cited by 6
Responses to the Initial Hype: ChatGPT Supporting Teaching, Learning, and Scholarship?
Cefa, B., Macgilchrist, F., ElGamal, H., Bai, J. Y. H., Zawacki‐Richter, O., & Loglo, F. S. (2025) · Open Praxis · doi:10.55982/openpraxis.17.2.872 · Cited by 10
Bozkurt, A. (2025) · Open Praxis · doi:10.55982/openpraxis.17.3.794 · Cited by 7
Self-citationBozkurt, A., Crompton, H., & Kurban, C. F. (2026) · Open Praxis · doi:10.55982/openpraxis.18.1.1145 · Cited by 9
Self-citationMishra, P., & Bozkurt, A. (2026) · Open Praxis · doi:10.55982/openpraxis.18.2.1093 · Cited by 6
Self-citationThe Effect of Feedback on Informative Text Writing: AI or Teacher?
Baz, M. A., & Hasırcı, S. (2025) · Open Praxis · doi:10.55982/openpraxis.17.3.871 · Cited by 12
Bachtiar, B., & Sylvia, S. (2026) · Open Praxis · doi:10.55982/openpraxis.18.1.925 · Cited by 6
Ngo, T. N., & Fians, G. (2026) · Journal of English for Academic Purposes · doi:10.1016/j.jeap.2026.101680 · Cited by 6
The Role of Open Education and Faculty Gender in Shaping Student Perceptions
Clinton‐Lisell, V., & Kelly, A. E. (2024) · Open Praxis · doi:10.55982/openpraxis.16.4.733 · Cited by 6
Chatto, M. J., & Logronio, F. V. (2025) · Journal of Education and Learning Reviews · doi:10.60027/jelr.2025.2061 · Cited by 11
Maslenkova, N. A., & Nikitina, A. (2025) · Semiotic studies · doi:10.18287/2782-2966-2025-5-2-122-133 · Cited by 3
Ética de la IA generativa en la formación legal universitaria
Salcedo, E. D. R. R., Hurtado, D. J. M., Sáez, Y. T. M., Vaca, K. A. L., Alvarado, M. A. M., Rodríguez, M. C. E., Lagua, A. D. G., Chiguano, M. A. C., & Paredes, B. H. Z. (2025) · Prohominum · doi:10.47606/acven/ph0375 · Cited by 4
Stenbom, S., Garrison, D. R., & Bozkurt, A. (2026) · Open Praxis · doi:10.55982/openpraxis.18.1.1042 · Cited by 4
Self-citationDoctoral students’ interdisciplinary research proposal development supported by generative AI
Naganuma, S., Minematsu, T., Matsueda, K., & Oshima, J. (2026) · Education and Information Technologies · doi:10.1007/s10639-026-13952-y · Cited by 4
“While these debates are still ongoing, because our students used GenAI for ideation, as Bozkurt (2024) and Nguyen (2025) point out, to main - tain academic integrity and ethics, transparent reporting such as “The development of ideas in this paper involved sessions assisted by GenAI” (Bozkurt,…”
Artificial Intelligence in Education and Training
Shashkova, N., Chetyrbok, P. V., Lukyanova, Y. Y., Kazak, A., & Sidin, M. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-78776-8_36 · Cited by 4
Karakitsou, C., Karayianni, I., Leftheriotis, K., Logotheti, A., & Avgerinou, M. D. (2025) · Journal of Visual Literacy · doi:10.1080/1051144x.2025.2538256 · Cited by 3
“Issues of academic integrity, such as plagiarism and cheating, privacy concerns, and biases in AI-generated content pose ethical dilemmas (Avgerinou et al., 2023; Batista et al., 2024; Bozkurt et al., 2024).”
Virtual Exchange to Develop Global Citizenship and Promote Sustainable Development Goal 3.4
van Vuuren, W. J., Heymans, Y., Pool, J., & Strosnider, C. L. (2025) · Open Praxis · doi:10.55982/openpraxis.17.2.776 · Cited by 4
Bates, T., & Bozkurt, A. (2025) · Open Praxis · doi:10.55982/openpraxis.17.3.968 · Cited by 3
Self-citationReclaiming authorship in the age of generative AI: From panic to possibility
Askari, M. (2025) · AI Magazine · doi:10.1002/aaai.70022 · Cited by 3
Meishar‐Tal, H. (2025) · Ethics & Behavior · doi:10.1080/10508422.2025.2562589 · Cited by 5
“The tools with the highest relative usage are ChatGPT 3.5, ChatGPT 4, and Claude.”
InfluentialDreams, Distortions, and Disruptions
ARTSIN, M., & Bozkurt, A. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-1195-1.ch001 · Cited by 3
Self-citationA Posthumanist Inquiry into AI-assisted Academic Writing Practices
Karthika, V. K. (2026) · Higher Education for the Future · doi:10.1177/23476311251381967 · Cited by 4
“While AI may support cognitive development, it also prompts debate around the authenticity of students’ written work and the changing nature of authorship in an increasingly digital academic environment (Bozkurt, 2024).”
Yağcı, Ş. Ç., Orhan, A., Yıldız, T. A., & Bozkurt, A. (2026) · Interactive Learning Environments · doi:10.1080/10494820.2026.2617482 · Cited by 5
“As emphasized by Bozkurt (2024), the true educational value of generative artificial intelligence (GenAI) emerges not merely from its technical capacities but from the degree to which it is guided by ethical, human-centered, and pedagogically meaningful principles.”
Self-citationDennison, D. V., Ahtisham, B., Chourasia, K., Arora, N., Singh, R., Kizilcec, R. F., Nambi, A., Ganu, T., & Vashistha, A. (2026) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3788074 · Cited by 30
Human-centered generative AI in education: ethical challenges and equity-driven solutions
Abu, S., Uddın, M. M., Moon, J., Awoyemi, I. D., Baah-Mintah, R., & Ghooreian, A. (2026) · Ethics & Behavior · doi:10.1080/10508422.2026.2685014 · Cited by 4
Training Needs Analysis for Digital Assessment in Higher Education
Sava, S., Grosseck, G., & Maliţa, L. (2024) · Lecture notes in educational technology · doi:10.1007/978-981-97-6136-4_1 · Cited by 4
Trend and Issues in Educational Video Research: A Bibliometric Mapping Analysis
Polat, H., Kayaduman, H., & Battal, A. (2025) · Open Praxis · doi:10.55982/openpraxis.17.1.721 · Cited by 3
Beyond Tools: Navigating Pedagogical Innovation in Postdigital Classrooms
Manna, M., & Eradze, M. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-95627-0_6 · Cited by 3
Rethinking Teacher Education in the ChatGPT Era through a Currere Perspective
Akbaş, O. (2025) · Journal of Teacher Education and Lifelong Learning · doi:10.51535/tell.1697199 · Cited by 3
Leininger, P., Weber, C. J., & Rothe, S. (2025) · Proceedings of the 2025 ACM International Conference on Interactive Media Experiences · doi:10.1145/3706370.3727853 · Cited by 3
“Emerging questions include the intellectual ownership of AI-generated environments, transparency in generative processes, concerns about authenticity and cultural representation in AI-generated visuals, and the cultural implications of automated content production [2, 3, 6, 12, 31, 41].”
Anthuvan, T., Nagendra, L., Lakhani, O., Prabhuram, S., Rizvi, A. H., Maheshwari, K., & Kalra, S. (2025) · doi:10.2139/ssrn.5394918 · Cited by 2
Wu, E. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1890769 · Cited by 2
Effects of Generative AI on Engagement and Performance Within Asynchronous Online Courses
Flores, F., Norman, K., & Hanshaw, G. (2026) · Open Praxis · doi:10.55982/openpraxis.18.2.980 · Cited by 2
“According to the National Center for Educational Statistics (2023), in Fall 2021 roughly 9.4 million students, or 61 percent of all undergraduate students, were enrolled in at least one distance education course.”
InfluentialAlpat, M. F., & Görgülü, E. (2024) · Open Praxis · doi:10.55982/openpraxis.16.3.704 · Cited by 2
A Case on Remote Supervised Exam Practices During the Pandemic
Güneş, E. P. U., Gedik, N., Işıkoğlu, M. A., et al. (2024) · Open Praxis · doi:10.55982/openpraxis.16.4.691 · Cited by 2
Anthony, A., & Sharma, S. (2025) · Journal of Educational Technology Systems · doi:10.1177/00472395251356520 · Cited by 4
Democratizing Game Modding with GenAI: A Case Study of StarCharM, a Stardew Valley Character Maker
Miralvand, H. Z., Nikghalb, M. R., Darandeh, M., Khan, A., Arawjo, I., & Cheng, J. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3748612 · Cited by 3
Arhin, V., Ampofo, S. Y., Kaedabi‐Donkor, R., Nyagorme, P., & Laryea, J. E. (2025) · Open Praxis · doi:10.55982/openpraxis.17.4.984 · Cited by 4
Yang, M., Luo, T., Herman, K., Li, B., Chappell, S., Glaser, N. J., & Jiang, S. (2025) · Journal of Computing in Higher Education · doi:10.1007/s12528-025-09476-y · Cited by 3
“Ethical concerns, as well as data privacy and ownership, arise, particularly as AI’s role in an individual’s work blurs traditional boundaries of ownership (Bozkurt, 2024 ; Bozkurt et al., 2021 ; Draxler et al., 2024 ).”
Harrison, M. (2025) · Open Praxis · doi:10.55982/openpraxis.17.4.909 · Cited by 2
Uçan, S., & Uçan, A. D. (2026) · Journal of Academic Ethics · doi:10.1007/s10805-026-09739-y · Cited by 5
“The need to “uphold academic integrity” despite GenAI’s temptations has been a common refrain (Bozkurt, 2024 , p. 4).”
Elibol, S., & Bozkurt, A. (2025) · American Journal of Distance Education · doi:10.1080/08923647.2025.2510736 · Cited by 2
Self-citationInitial Validation of the IMPACT Model: Technological Appropriation of ChatGPT by University Faculty
Pereira, L. M., Basantes-Andrade, A., Naranjo-Toro, M., & Guia-Pereira, M. (2025) · Education Sciences · doi:10.3390/educsci15111520 · Cited by 5
“Ethical considerations have increasingly been recognized as determinants of user trust and acceptance, encompassing issues such as academic integrity, algorithmic bias, and data privacy (Bozkurt, 2024; Fowler, 2023).”
Towards an experience in AI-driven development for programming applied to multimedia using Tabnine
Llerena-Izquierdo, J., Vargas, I. V. F., Gorozabel, J. A. P., & Arce, A. E. V. (2024) · IEEE URUCON · doi:10.1109/urucon63440.2024.10850207 · Cited by 1
“Even with generative AI tools, it is crucial to consider inherent factors such as the reliability of suggestions and possible risks, such as patterns of unethical behavior by students [24].”
Students as Co-Authors: Achievement Emotions, Beliefs About Writing, and OER Publishing Decisions
Kelly, A. E., Avila, B. N., & Schell, A. C. (2025) · Open Praxis · doi:10.55982/openpraxis.17.1.745 · Cited by 1
Barrera, M. A. O., Álvarez, L., & Maciel, J. P. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-2685-6.ch004 · Cited by 1
Simple Checklists to Verify the Accuracy of AI-Generated Research Summaries
Veletsianos, G. (2025) · TechTrends · doi:10.1007/s11528-025-01127-4 · Cited by 1
Students’ Perception of the Integration of GenAI in Academic Paper Assignment Preparation
Zdravkova, K., & Ilijoski, B. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-01429-0_15 · Cited by 1
AI in Academia: An In-Depth Analysis of the Impact of AI on Undergraduate Studies
Alawamleh, M., Alatout, R., & Awamleh, W. (2025) · Journal of Academic Ethics · doi:10.1007/s10805-025-09685-1 · Cited by 2
“These concerns are further supported by Bozkurt (2024 ), who emphasizes the ‘organic versus synthetic’ paradox in academic writing, and calls for transparent reporting to uphold academic integrity in the era of generative AI.”
Towards a Flipped Learning Ecosystem: A Generative Artificial Intelligence-Enabled Framework
ElGamal, H. (2025) · European Journal of Open Distance and E-Learning · doi:10.65043/eurodl.151 · Cited by 1
Exploring Equity, Diversity, and Inclusion in Blended Learning: Trends and Insights
Durak, G., Çankaya, S., Öncü, S., & Rienties, B. (2025) · Open Praxis · doi:10.55982/openpraxis.17.4.931 · Cited by 2
Metadata
- Authors
- Bozkurt, A.
- Affiliations
- Anadolu University (TR)
- Corresponding author
- Bozkurt, Aras
- ORCID
- Bozkurt, Aras
- Venue
- Open Praxis
- Published
- 2024
- Volume
- 16
- Issue
- 1
- Pages
- 1-10
- ISSN
- 1369-9997 (ISSN-L)
- 2304-070X
- OpenAlex
- W4391488437
- Semantic Scholar
- 39d26fd00ba7b6bd7a642a10a0287234157cb3e7
- Cited by
- 215
- References
- 43
- Citations by year
- 2026: 106 · 2025: 89 · 2024: 20
- FWCI
- 89.51
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- Open Praxis (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Anadolu University (SBA-2023-1852)
- Topics
- Social Sciences › Education › Academic integrity and plagiarism
- Social Sciences › Education › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Keywords
- generative AI, academic integrity, authorship, intellectual property, copyright, AI-generated content, publish or perish, academic writing, AI detection, academic ethics, human-AI collaboration
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-29, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-29 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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