Exploring the Factors That Promote a Balance Between Academic Integrity and the Effective Use of GenAI Tools in Higher Education: A Systematic Review
Abstract
Background This study explored the factors that influence the balance between academic integrity and the effective use of GenAI tools in higher education. It focused on the role of institutional guidelines in enhancing the responsible use of GenAI technologies to enhance academic integrity. Objectives The study was theoretically grounded in the Technology Acceptance Model and the Theory of Planned Behaviour to investigate the factors that promote academic integrity in using GenAI tools (RQ1), their impact and institutional strategies to effectively mitigate ethical risks (RQ2) and the model practices to support the ethical and effective use in higher education (RQ3). Methods The PRISMA framework was used to systematically review and thematically synthesise the results of 213 peer‐reviewed articles published between January 2021 and May 2025. Results Finding indicates that academic support, defined by structured training, technical scaffolding, and perceived usefulness, is critical to enabling ethical GenAI use. Additionally, student self‐regulation, as influenced by behavioural control and goal setting, was associated with greater academic integrity in GenAI‐mediated learning. Whereas institutional policies varied widely, those with transparent, adaptive and discipline‐responsive governance frameworks more effectively mitigated academic misconduct. Indeed, the model practices included GenAI ethics committees, interactive GenAI literacy modules, and the developer‐educator coll…
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Students report concerns regarding the accuracy and reliability of GenAI-generated content.
“Students also report concerns regarding the accuracy and reliability of GenAI-generated content (Kangwa et al., 2025).”
From Beyond unstructured use: scaffolded integration of generative AI in assessment for learning (2026) · cites this paper as Kangwa et al., 2025
Most institutions still rely heavily on AI outputs for high-stakes decisions without sufficient human review, appeals processes, or faculty training.
“Nevertheless, Kangwa et al. (2025) observe that most institutions still rely heavily on AI outputs for high-stakes decisions without sufficient human review, appeals processes, or faculty training.”
From Harms and Ethical Risks of AI Detection Tools in Education: False Positives, Bias, Surveillance, and Student Rights (2026) · cites this paper as Kangwa et al. (2025)
Recent systematic review evidence indicates that the responsible adoption of GenAI in higher education depends on a combination of academic support, student self-regulation, and clear institutional governance.
“Recent systematic review evidence indicates that the responsible adoption of GenAI in higher education depends on a combination of academic support, student self-regulation, and clear institutional governance.”
From Engineering student perceptions of generative AI use in learning trust judgment and information seeking (2026) · cites this paper as [14]
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The Influence of AI ChatGPT on Improving Teachers' Creative Thinking
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Kangwa, D., Msafiri, M. M., Charles, K., & Fute, A. Z. (2026) · Sustainable Development · doi:10.1002/sd.71178 · Cited by 3 · see citing works
Self-citationYang, L., & Yang, J. (2026) · International Journal of Multilingualism · doi:10.1080/14790718.2026.2729839 · Cited by 3 · see citing works
Mu, S., Wang, B., Xiong, J., Xie, Y., & Hu, W. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1876096 · Cited by 1 · see citing works
Charles, K., Lucy, A. C., Irene, E. C., & Fobi, S. (2026) · Educational Point · doi:10.71176/edup/19172 · Cited by 1 · see citing works
Beyond unstructured use: scaffolded integration of generative AI in assessment for learning
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GİRAY, L., CANER, H. N., & AMOOZEGAR, A. (2026) · Journal of Narrative and Language Studies · doi:10.59045/nalans.2026.109
Artificial intelligence in physiology teaching and learning
Favero, T. G. (2026) · Current Opinion in Physiology · doi:10.1016/j.cophys.2026.100951
Ethical, Pedagogical, and Research Implications of AI Use in Education and Academic Research
Hương, L. P. H., & Dao, T. T. (2026) · Studies in computational intelligence · doi:10.1007/978-3-032-18392-7_11
Yuan, Z.-H. (2026) · Ethics & Behavior · doi:10.1080/10508422.2026.2659602
Who assesses morality, and how? A systematic review of research gaps in Akidah Akhlak assessment
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Lamsiyah, S. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-29760-0_44
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Tan, C.-Y., Adams, D., Ng, D., & Gao, L. (2026) · Educational Research and Evaluation · doi:10.1080/13803611.2026.2698571
Brătianu, C., & Bejinaru, R. (2026) · Proceedings of the ... International Conference on Business Excellence · doi:10.2478/picbe-2026-0305
Gao, Y., Liu, Y., & Wang, X. (2026) · Acta Psychologica · doi:10.1016/j.actpsy.2026.107605
Zhong, Z., Hou, J., Duan, P., & Qin, H. (2026) · Journal of Professional Capital and Community · doi:10.1108/jpcc-02-2026-0057
Le, N. T. T. (2026) · International Journal of Learning Teaching and Educational Research · doi:10.26803/ijlter.25.8.13
EFL Students' Ethical Reasoning and Academic Integrity in the Age of Generative AI
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Policy Design for AI-Enabled Higher Institutions
Oyenuga, M. O. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-4050-8.ch006
Hafizin, Maisyaroh, Timan, A., Triwiyanto, T., & Ahyar, M. (2026) · Multidisciplinary Science Journal · doi:10.31893/multiscience.2027184
- Authors
- Kangwa, D., Msafiri, M. M., & Fute, A.
- Affiliations
- Zhejiang Normal University (CN)
- Yulin University (CN)
- Corresponding author
- Kangwa, Daniel
- Venue
- Journal of Computer Assisted Learning
- Publisher
- Wiley
- Published
- 2025-08-31
- Volume
- 41
- Issue
- 5
- ISSN
- 0266-4909 (ISSN-L)
- 1365-2729
- OpenAlex
- W4413868768
- Cited by
- 22
- References
- 375
- Citations by year
- 2026: 22
- FWCI
- 3.41
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Computer Assisted Learning (publishedVersion)
- Topics
- Social Sciences › Safety Research › Academic integrity and plagiarism
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- generative AI, academic integrity, higher education, institutional guidelines, academic misconduct, student self-regulation, academic support, AI literacy, AI ethics, technology acceptance model, theory of planned behavior, responsible AI use, algorithmic transparency, faculty development
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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.
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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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