The Impact of GenAI Chatbots on Student Learning in Higher Education: A Literature Review
DOI:
https://doi.org/10.46328/ijte.5111Keywords:
Artificial Intelligence (AI), Generative AI, Chatbots, Student learning, Higher EducationAbstract
The integration of generative AI (GenAI) chatbots in Higher Education raises questions about the impact of the new technology on student learning. This systematic literature review synthesizes findings from 49 empirical studies published between 2022 and 2024, focusing on how GenAI chatbots influence key dimensions of student learning, including motivation, engagement, self-efficacy, self-regulation, comprehension, critical thinking, problem-solving, and learning performance. Results indicate that GenAI chatbots can enhance learning by providing personalized support, immediate feedback, and opportunities for self-directed learning. However, concerns persist regarding over-reliance on AI, reduced critical thinking, and academic integrity. The review highlights that guided and pedagogically sound integration of GenAI tools is essential to maximize benefits and mitigate risks. These findings underscore the importance of developing AI literacy and ethical usage guidelines to support meaningful and equitable learning experiences in Higher Education.
References
Abu Khurma, O., Albahti, F., Ali, N., & Bustanji, A. (2024). AI ChatGPT and student engagement: Unraveling dimensions through PRISMA analysis for enhanced learning experiences. Contemporary Educational Technology, 16(2). https://doi.org/10.30935/cedtech/14334
Akcaoğlu, M. Ö., Mor, E., & Külekçi, E. (2023). The mediating role of metacognitive awareness in the relationship between critical thinking and self-regulation. Thinking Skills and Creativity, 47. https://doi.org/10.1016/j.tsc.2022.101187
Araujo, S. M., & Cruz-Correia, R. (2024). Incorporating ChatGPT in Medical Informatics Education: Mixed Methods Study on Student Perceptions and Experiential Integration Proposals. JMIR Medical Education, 10. https://doi.org/10.2196/51151
Barkley, E. F., & Major, C. H. (2020). Student engagement techniques: a handbook for college faculty (2nd ed.). Jossey-Bass.
Beatson, N. J., Berg, D. A. G., & Smith, J. K. (2018). The impact of mastery feedback on undergraduate students’ self-efficacy beliefs. Studies in Educational Evaluation, 59, 58–66. https://doi.org/10.1016/j.stueduc.2018.03.002
Bhullar, P. S., Joshi, M., & Chugh, R. (2024). ChatGPT in higher education - a synthesis of the literature and a future research agenda. Education and Information Technologies. https://doi.org/10.1007/s10639-024-12723-x
Bloom, B. S. (1984). The 2 Sigma Problem: The Search for Methods of Group Instruction as Effective as One-to-One Tutoring. Educational Researcher, 4–16.
Bloom, B. S., Engelhart, M. D., Furst, E. J., Hill, W. H., & Krathwohl, D. R. (1956). Taxonomy of educational objectives: The classification of educational goals. Handbook 1: Cognitive domain (B. S. Bloom, Ed.). David McKay.
Bol, L., & Garner, J. K. (2011). Challenges in supporting self-regulation in distance education environments. Journal of Computing in Higher Education, 23(2–3), 104–123. https://doi.org/10.1007/s12528-011-9046-7
Bowden, J. L. H., Tickle, L., & Naumann, K. (2021). The four pillars of tertiary student engagement and success: a holistic measurement approach. Studies in Higher Education, 46(6), 1207–1224. https://doi.org/10.1080/03075079.2019.1672647
Bravo, F. A., & Cruz-Bohorquez, J. M. (2024). Engineering Education in the Age of AI: Analysis of the Impact of Chatbots on Learning in Engineering. Education Sciences, 14(5). https://doi.org/10.3390/educsci14050484
Bruun, M. H., Krause-Jensen, J., & Hasse, C. (2024). Store sprogmodeller og AI-chatbots på videregående uddannelser. In Pædagogisk Indblik (Vol. 26). https://dpu.au.dk/fileadmin/edu/Paedagogisk_Indblik/AI_paa_videregaaende_uddannelser/26_Store_Sprogmodeller_og_AI-chatbots_paa_videregaaende_uddannelser_-11-12-2024.pdf
Caspersen, J., Smeby, J. C., & Olaf Aamodt, P. (2017). Measuring learning outcomes. European Journal of Education, 52(1), 20–30. https://doi.org/10.1111/ejed.12205
Casuso-Holgado, M. J., Cuesta-Vargas, A. I., Moreno-Morales, N., Labajos-Manzanares, M. T., Barón-López, F. J., & Vega-Cuesta, M. (2013). The association between academic engagement and achievement in health sciences students. BMC Medical Education, 13(33). http://www.biomedcentral.com/1472-6920/13/33
Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: the state of the field. International Journal of Educational Technology in Higher Education, 20(1). https://doi.org/10.1186/s41239-023-00392-8
Dahri, N. A., Yahaya, N., Al-Rahmi, W. M., Vighio, M. S., Alblehai, F., Soomro, R. B., & Shutaleva, A. (2024). Investigating AI-based academic support acceptance and its impact on students’ performance in Malaysian and Pakistani higher education institutions. Education and Information Technologies. https://doi.org/10.1007/s10639-024-12599-x
Davies, M. (2015). A Model of Critical Thinking in Higher Education. In M. B. Paulsen (Ed.), Higher Education: Handbook of Theory and Research (pp. 41–92). Springer International Publishing Switzerland. https://doi.org/10.1007/978-3-319-12835-1_2
Davis, F. (1985). A technology acceptance model for empirically testing new end-user information systems : theory and results [Massachusetts Institute of Technology]. http://hdl.handle.net/1721.1/15192
Deng, R., Jiang, M., Yu, X., Lu, Y., & Liu, S. (2025). Does ChatGPT enhance student learning? A systematic review and meta-analysis of experimental studies. Computers & Education, 227, 105224. https://doi.org/10.1016/j.compedu.2024.105224
Elkhodr, M., Gide, E., Wu, R., & Darwish, O. (2023). ICT students’ perceptions towards ChatGPT: An experimental reflective lab analysis. STEM Education, 3(2), 70–88. https://doi.org/10.3934/steme.2023006
Fahim, M., & Masouleh, N. S. (2012). Critical thinking in higher education: A pedagogical look. Theory and Practice in Language Studies, 2(7), 1370–1375. https://doi.org/10.4304/tpls.2.7.1370-1375
Faisal, E. (2024). Unlock the potential for Saudi Arabian higher education: a systematic review of the benefits of ChatGPT. Frontiers in Education, 9. https://doi.org/10.3389/feduc.2024.1325601
Flake, J. K., Barron, K. E., Hulleman, C., McCoach, B. D., & Welsh, M. E. (2015). Measuring cost: The forgotten component of expectancy-value theory. Contemporary Educational Psychology, 41, 232–244. https://doi.org/10.1016/j.cedpsych.2015.03.002
Gao, Z., Cheah, J.-H., Lim, X.-J., & Luo, X. (2024). Enhancing academic performance of business students using generative AI: An interactive-constructive-active-passive (ICAP) self-determination perspective. International Journal of Management Education, 22(2). https://doi.org/10.1016/j.ijme.2024.100958
Gouia-Zarrad, R., & Gunn, C. (2024). Enhancing students’ learning experience in mathematics class through ChatGPT. International Electronic Journal of Mathematics Education, 19(3). https://doi.org/10.29333/iejme/14614
Guo, Y., & Lee, D. (2023). Leveraging ChatGPT for Enhancing Critical Thinking Skills. Journal of Chemical Education, 100(12), 4876–4883. https://doi.org/10.1021/acs.jchemed.3c00505
Habib, S., Vogel, T., Anli, X., & Thorne, E. (2024). How does generative artificial intelligence impact student creativity? Journal of Creativity, 34(1). https://doi.org/10.1016/j.yjoc.2023.100072
Haindl, P., & Weinberger, G. (2024). Students’ Experiences of Using ChatGPT in an Undergraduate Programming Course. IEEE ACCESS, 12, 43519–43529. https://doi.org/10.1109/ACCESS.2024.3380909
Hamid, H., Zulkifli, K., Naimat, F., Yaacob, N. L. C., & Ng, K. W. (2023). Exploratory study on student perception on the use of chat AI in process-driven problem-based learning. Currents in Pharmacy Teaching and Learning, 15(12), 1017–1025. https://doi.org/10.1016/j.cptl.2023.10.001
Hammoda, B. (2024). ChatGPT for Founding Teams: An Entrepreneurial Pedagogical Innovation. International Journal of Technology in Education, 7(1), 154–173. https://doi.org/https://doi.org/10.46328/ijte.530
Harlim, J., & Belski, I. (2013). Long-term Innovative Problem Solving Skills: Redefining Problem Solving. International Journal of Engineering Education, 29(2), 280–290.
Hmoud, M., Swaity, H., Hamad, N., Karram, O., & Daher, W. (2024). Higher Education Students’ Task Motivation in the Generative Artificial Intelligence Context: The Case of ChatGPT. Information, 15(1). https://doi.org/10.3390/info15010033
Holland, A., & Ciachir, C. (2024). A qualitative study of students’ lived experience and perceptions of using ChatGPT: immediacy, equity and integrity. Interactive Learning Environments, 33(1), 483–494. https://doi.org/10.1080/10494820.2024.2350655
Hsu, M. H. (2023). Mastering medical terminology with ChatGPT and Termbot. Health Education Journal. https://doi.org/10.1177/00178969231197371
Hyde, S. J., Busby, A., & Bonner, R. L. (2024). Tools or Fools: Are We Educating Managers or Creating Tool-Dependent Robots? Journal of Management Education. https://doi.org/10.1177/10525629241230357
Inoferio, H. V., Espartero, M. M., Asiri, M. S., Damin, M. D., & Chavez, J. V. (2024). Coping with math anxiety and lack of confidence through AI-assisted Learning. Environment and Social Psychology, 9(5). https://doi.org/10.54517/esp.v9i5.2228
Jonassen, D. H. (2000). Toward a Design Theory of Problem Solving. Educational Technology Research and Development, 48, 63–85. https://doi.org/https://doi.org/10.1007/BF02300500
Jost, G., Taneski, V., & Karakatic, S. (2024). The Impact of Large Language Models on Programming Education and Student Learning Outcomes. Applied Sciences, 14(10), 4115. https://doi.org/10.3390/app14104115
Karataş, F., Abedi, F. Y., Ozek Gunyel, F., Karadeniz, D., & Kuzgun, Y. (2024). Incorporating AI in foreign language education: An investigation into ChatGPT’s effect on foreign language learners. Education and Information Technologies, 29, 19343–19366. https://doi.org/10.1007/s10639-024-12574-6
Kavadella, A., Da Silva, M. A. D., Kaklamanos, E. G., Stamatopoulos, V., & Giannakopoulos, K. (2024). Evaluation of ChatGPT’s Real-Life Implementation in Undergraduate Dental Education: Mixed Methods Study. JMIR Medical Education, 10(1). https://doi.org/10.2196/51344
Kim, D., Majdara, A., & Olson, W. (2024). A Pilot Study Inquiring into the Impact of ChatGPT on Lab Report Writing in Introductory Engineering Labs. International Journal of Technology in Education, 7(2), 259–289. https://doi.org/10.46328/ijte.691
Kirschner, P. A. (2002). Cognitive load theory: implications of cognitive load theory on the design of learning. Learning and Instruction, 12(1), 1–10. https://doi.org/https://doi.org/10.1016/S0959-4752(01)00014-7
Komba, M. M. (2024). The influence of ChatGPT on digital learning: experience among university students. Global Knowledge, Memory and Communication. https://doi.org/10.1108/GKMC-10-2023-0390
Krathwohl, D. R. (2002). A Revision of Bloom’s Taxonomy: An Overview. Theory Into Practice, 41(4), 212–218. https://doi.org/10.1207/s15430421tip4104_2
Lee, J. S. (2014). The relationship between student engagement and academic performance: Is it a myth or reality? Journal of Educational Research, 107(3), 177–185. https://doi.org/10.1080/00220671.2013.807491
Liang, J., Wang, L. L., Luo, J., Yan, Y. F., & Fan, C. (2023). The relationship between student interaction with generative artificial intelligence and learning achievement: serial mediating roles of self-efficacy and cognitive engagement. Frontiers in Psychology, 14. https://doi.org/10.3389/fpsyg.2023.1285392
Lovett, M. C. ; B. M. W. ; D. M. (2023). How learning works: eight research-based principles for smart teaching (2nd ed.). Jossey-Bass.
Mai, D. T. T., Da, C. Van, & Hanh, N. Van. (2024). The use of ChatGPT in teaching and learning: a systematic review through SWOT analysis approach. Frontiers in Education, 9. https://doi.org/10.3389/feduc.2024.1328769
Marquardson, J. (2024). Embracing Artificial Intelligence to Improve Self-Directed Learning: A Cybersecurity Classroom Study. Information Systems Education Journal, 22(1), 4–13. https://doi.org/https://doi.org/10.62273/WZBY3952
Muthmainnah, Seraj, P. M. I., & Oteir, I. (2022). Playing with AI to Investigate Human-Computer Interaction Technology and Improving Critical Thinking Skills to Pursue 21st Century Age. Education Research International, 2022. https://doi.org/10.1155/2022/6468995
Ou, A. W., Stöhr, C., & Malmström, H. (2024). Academic communication with AI-powered language tools in higher education: From a post-humanist perspective. System, 121. https://doi.org/10.1016/j.system.2024.103225
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372(n71). https://doi.org/10.1136/bmj.n71
Pekruna, R. (2020). Commentary: Self-report is indispensable to assess students’ learning. Frontline Learning Research, 8(3), 185–193. https://doi.org/10.14786/flr.v8i3.637
Prakong, S. (2024). The Role of Critical Thinking in Enhancing Students’ Problem-Solving Abilities in Higher Education. Journal of Education, Humanities, and Social Research, 1(1), 10–16. https://doi.org/10.70088/scx8x622
Qureshi, B. (2023). ChatGPT in Computer Science Curriculum Assessment: An analysis of Its Successes and Shortcomings. ACM International Conference Proceeding Series, 7–13. https://doi.org/10.1145/3613944.3613946
Rajala, J., Hukkanen, J., Hartikainen, M., & Niemelä, P. (2023). “Call me Kiran” ChatGPT as a Tutoring Chatbot in a Computer Science Course. ACM International Conference Proceeding Series, 83–94. https://doi.org/10.1145/3616961.3616974
Rosenzweig, E. Q., Wigfield, A., & Eccles, J. S. (2019). Expectancy-value theory and its relevance for student motivation and learning. In The Cambridge Handbook of Motivation and Learning (pp. 617–644). Cambridge University Press. https://doi.org/10.1017/9781316823279.026
Sánchez-Guerrero, J., Miranda-López, X., Buenaño, H., & Lozada-Miranda, D. (2024). ChatGPT to Motivate Critical Thinking in the Teaching–Learning Process of First Semester Students at the Technical University of Ambato. In A. Rocha, J. M. R. Rodríguez, & C. H. Fajardo-Toro (Eds.), Smart Innovation, Systems and Technologies (Vol. 380, pp. 169–179). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-99-8894-5_15
Shoufan, A. (2023). Exploring Students’ Perceptions of ChatGPT: Thematic Analysis and Follow-Up Survey. IEEE ACCESS, 11, 38805–38818. https://doi.org/10.1109/ACCESS.2023.3268224
Silitonga, L. M., Hawanti, S., Aziez, F., Furqon, M., Zain, D. S. M., Anjarani, S., & Wu, T.-T. (2023). The Impact of AI Chatbot-Based Learning on Students’ Motivation in English Writing Classroom. In H. Y.-M. & T. Rocha (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Vol. 14099 LNCS (pp. 542–549). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-40113-8_53
Singh, H., Tayarani-Najaran, M.-H., & Yaqoob, M. (2023). Exploring Computer Science Students’ Perception of ChatGPT in Higher Education: A Descriptive and Correlation Study. Education Sciences, 13(9). https://doi.org/10.3390/educsci13090924
Soderstrom, N. C., & Bjork, R. A. (2015). Learning Versus Performance: An Integrative Review. Perspectives on Psychological Science, 10(2), 176–199. https://doi.org/10.1177/1745691615569000
Song, C. P., & Song, Y. P. (2023). Enhancing academic writing skills and motivation: assessing the efficacy of ChatGPT in AI-assisted language learning for EFL students. Frontiers in Psychology, 14. https://doi.org/10.3389/fpsyg.2023.1260843
Suskie, L. (2018). Assessing student learning: a common sense guide (3rd ed.). Jossey-Bass.
Swapna, G., & Jin SHIM, K. (2023). Exploring students’ adoption of ChatGPT as a mentor for undergraduate computing projects: PLS-SEM analysis. Proceedings of the 31st International Conference on Computers in Education Conference, Matsue, Shimane, Japan, 2023 December 4-8., 40–45. https://ink.library.smu.edu.sg/sis_research/8503/
Tossell, C. C., Tenhundfeld, N. L., Momen, A., Cooley, K., & Visser, E. J. de. (2024). Student Perceptions of ChatGPT Use in a College Essay Assignment: Implications for Learning, Grading, and Trust in Artificial Intelligence. IEEE Transactions on Learning Technologies, 17, 1069–1081. https://doi.org/10.1109/TLT.2024.3355015
Tu, Y.-F. (2024). Roles and Functionalities of ChatGPT for Students with Different Growth Mindsets: Findings of Drawing Analysis. Educational Technology & Society, 27(1), 198–214. https://doi.org/10.30191/ETS.202401_27(1).TP01
Tu, Y.-F., & Hwang, G.-J. (2023). University students’ conceptions of ChatGPT-supported learning: a drawing and epistemic network analysis. Interactive Learning Environments, 32(10), 6790–6814. https://doi.org/10.1080/10494820.2023.2286370
Van Dinther, M., Dochy, F., & Segers, M. (2011). Factors affecting students’ self-efficacy in higher education. Educational Research Review, 6(2), 95–108. https://doi.org/10.1016/j.edurev.2010.10.003
Wu, R., & Yu, Z. (2024). Do AI chatbots improve students learning outcomes? Evidence from a meta-analysis. British Journal of Educational Technology, 55(1), 10–33. https://doi.org/10.1111/bjet.13334
Xiao, Y., & Zhi, Y. (2023). An Exploratory Study of EFL Learners’ Use of ChatGPT for Language Learning Tasks: Experience and Perceptions. Languages, 8(3). https://doi.org/10.3390/languages8030212
Yang, Y. Y., Luo, J. W., Yang, M. Y., Yang, R. D., & Chen, J. Y. (2024). From surface to deep learning approaches with Generative AI in higher education: an analytical framework of student agency. Studies in Higher Education. https://doi.org/10.1080/03075079.2024.2327003
Zhang, Y. Y., Viriyavejakul, C., & Sumettikoon, P. (2023). Integrating Chatbots in Educational Administration for Improved Language Learning Outcomes. Eurasian Journal of Educational Research, 104, 142–163. https://doi.org/10.14689/ejer.2023.104.009
Zhao, X., Aydeniz, M., & Yuan, F. (2023). Exploring Opportunities and Challenges of AI-incorporated Biomedical Informatics Education: A Qualitative Study. Proceedings 2023 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI). https://doi.org/10.1109/BHI58575.2023.10313394
Zhou, W., & Kim, Y. (2024). Innovative music education: An empirical assessment of ChatGPT-4’s impact on student learning experiences. Education and Information Technologies, 29, 20855–20881. https://doi.org/10.1007/s10639-024-12705-z
Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory into Practice, 41(2), 64–70. https://doi.org/10.1207/s15430421tip4102_2
Zirar, A. (2023). Exploring the Impact of Language Models, Such as ChatGPT, on Student Learning and Assessment. Review of Education, 11(3). https://doi.org/10.1002/rev3.3433
Downloads
Published
Issue
Section
License
Copyright (c) 2026 International Journal of Technology in Education

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Articles may be used for research, teaching, and private study purposes. Authors alone are responsible for the contents of their articles. The journal owns the copyright of the articles. The publisher shall not be liable for any loss, actions, claims, proceedings, demand, or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of the research material.
The author(s) of a manuscript agree that if the manuscript is accepted for publication in the International Journal of Technology in Education (IJTE), the published article will be copyrighted using a Creative Commons “Attribution 4.0 International” license. This license allows others to freely copy, distribute, and display the copyrighted work, and derivative works based upon it, under certain specified conditions.
Authors are responsible for obtaining written permission to include any images or artwork for which they do not hold copyright in their articles, or to adapt any such images or artwork for inclusion in their articles. The copyright holder must be made explicitly aware that the image(s) or artwork will be made freely available online as part of the article under a Creative Commons “Attribution 4.0 International” license.

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
