ChatGPT in Higher Education: A Review of Its Impact on Student Research
Open accessAbstract
This systematic literature review examines how ChatGPT influences student research in higher education. Drawing on the TPACK, Connectivism, and Community of Inquiry frameworks, the study explores how generative AI affects research skills, academic outcomes, and learner attitudes. Using PRISMA guidelines, 12 peer-reviewed empirical studies published in 2023 were selected from Scopus, Web of Science, ERIC, and Dimensions databases. Thematic analysis revealed that ChatGPT supports the development of research capabilities such as idea generation, sourcing, synthesis, and writing fluency. It also improves perceived academic confidence and motivation. However, ethical concerns including plagiarism risks, over-reliance, and hallucinated content were noted in several studies. Technical reliability, transparency, and the absence of clear pedagogical integration remain major limitations. The review concludes that ChatGPT can be a valuable tool in research-based learning if embedded within structured, ethical, and pedagogically guided frameworks. Practical recommendations and research directions are offered to guide future use and policy. Keywords: ChatGPT, Higher Education, Research, PRISMA, Academic Integrity.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
De Silva et al. (2025) reported benefits in idea generation, sourcing, synthesis, and writing, alongside hallucinated content and over-reliance.
“De Silva et al. (2025) likewise reported benefits in idea generation, sourcing, synthesis, and writing, alongside hallucinated content and over-reliance.”
From From Assistance to Substitution: Generative AI and Undergraduate Research Skills (2026) · cites this paper as De Silva et al., 2025
De Silva et al. (2025) identified hallucinated content, over-reliance, and academic integrity as recurring concerns.
“De Silva et al. (2025) identified hallucinated content, over-reliance, and academic integrity as recurring concerns.”
From From Assistance to Substitution: Generative AI and Undergraduate Research Skills (2026) · cites this paper as De Silva et al., 2025
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
No references could be resolved yet.
Research works printed but not linked: 13
- Bhuiyan, M. A., Rahman, M. K., Basile, V., Ping, H., & Bari, A. M. (2025). Adoption of ChatGPT for students' learning effectiveness. The International Journal of Management Education, 23(3), 101255. https://doi.org/10.1016/j.ijme.20 25.101255 · DOI
- Balalle, H., & Pannilage, S. (2025). Reassessing academic integrity in the age of AI: A systematic literature review on AI and academic integrity. Social Sciences & Humanities Open, 11, 101299. https://doi.org/10.1016/j.ssaho.2 025.101299 · DOI · Paper page
- Bittle, K., & El-Gayar, O. (2025). Generative AI and academic integrity in higher education: A systematic review and research agenda. Information, 16(4), 296. https://doi.org/10.3390/info1604 0296 · DOI · Paper page
- Blahopoulou, J., & Ortiz-Bonnin, S. (2025). Student perceptions of ChatGPT: Benefits, costs, and attitudinal differences between users and non-users toward AI integration in higher education. Education and Information Technologies, 1-24. https://doi.org/10.1007/s10639- 025-13575-9 · DOI · Paper page
- Kasneci, E., Sessler, K., Kessler, T., et al. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2 023.102274 · DOI · Paper page
- Kotsis, K. T. (2025). The role of ChatGPT in academic writing: Pedagogical and ethical dimensions. EthAIca: Journal of Ethics, AI and Critical Analysis, 4, 420. https://doi.org/10.56294/ai20254 · DOI
- Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, 108(6), 1017-1054.
- Patel, R., Bajaj, P., Kumar, A., Kumari, A., Rai, V., & Kumar, S. (2023, December). ChatGPT in the classroom: A comprehensive review of the impact of ChatGPT on modern education. In 2023 11th International Conference on Intelligent Systems and Embedded Design (ISED) (pp. 1-6). IEEE. https://doi.org/10.1109/ISED593 82.2023.10444568 · DOI
- Shuhaiber, A., Kuhail, M. A., & Salman, S. (2025). ChatGPT in higher education: A student's perspective. Computers in Human Behavior Reports, 17, 100565. https://doi.org/10.1016/j.chbr.20 · DOI
- Downes, S. (2008). Connectivism and connective knowledge. International Journal of Instructional Technology and Distance Learning, 5(10), 3-10. https://doi.org/10.12691/educati on-2-2-7 · DOI
- Yang, B., & Su, J. (2023). ChatGPT and the illusion of competence: An exploratory study in higher education. International Journal of Educational Technology in Higher Education, 20(1), 1-15. https://doi.org/10.1186/s41239- 023-00410-5 · DOI
- Zawacki-Richter, O., Marín, V., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education. International Journal of Educational Technology in Higher Education, 16(1), 1-27. https://doi.org/10.1186/s41239- 019-0171-0 · DOI · Paper page
- Zhai, X. (2023). ChatGPT for next generation science learning. XRDS: Crossroads, The ACM Magazine for Students, 29(3), 42-46. https://doi.org/10.1145/3589649 . . · DOI · Paper page
Cited by
Sorted by citations
Susena, Y. S., Arianto, F., Khotimah, K., Andrianto, R. E., & Syuhada’, M. (2026) · Reading Psychology · doi:10.1080/02702711.2026.2644973 · Cited by 1 · see citing works
From Assistance to Substitution: Generative AI and Undergraduate Research Skills
Rami Al Jundi, M., Abdulkareem Alali, B., Alothman, R., Almansour, A., & AL Zyadi, E. (2026) · Research Journal of Maaref University of Applied Sciences · doi:10.66422/rjmu.id.80
“De Silva et al. (2025) likewise reported benefits in idea generation, sourcing, synthesis, and writing, alongside hallucinated content and over-reliance.”
- Authors
- De Silva, G. H. B. A., Sandanayake, T. C., Firdhous, M. F. M., & Senarathne, C. D.
- Affiliations
- University of Moratuwa (LK)
- University of Kelaniya (LK)
- Venue
- Journal of Business and Technology
- Publisher
- Sri Lanka Journals Online
- Published
- 2025-10-31
- Pages
- 129-138
- ISSN
- 2550-2530 (ISSN-L)
- 2738-2028
- OpenAlex
- W4415719619
- Cited by
- 2
- References
- 0
- Citations by year
- 2026: 1 · 2025: 1
- FWCI
- 2.08
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Journal of Business and Technology (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Safety Research › Academic integrity and plagiarism
- Keywords
- ChatGPT, generative AI, student research, higher education, research skills, academic integrity, plagiarism, overreliance on AI, hallucinations, academic self-confidence, student motivation, TPACK, connectivism, community of inquiry, research-based learning, AI ethics in education
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.
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 ›
Are you an author, or do you see a problem with this page? Request a review
