The Impact of GenAI Chatbots on Student Learning in Higher Education: A Literature Review
Open accessAbstract
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.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
Generative AI chatbots are increasingly used in university learning for explanations, feedback, and writing support.
“Generative artificial intelligence (GenAI) chatbots are increasingly used in university learning for explanations, feedback, and writing support (Badger et al., 2026).”
From Beyond checking: verification quality, reliance calibration, and learning in generative AI-assisted higher education (2026) · cites this paper as Badger et al., 2026
Cites
The publisher hasn’t deposited this paper’s reference list.
Cited by
Sorted by citations
Impacto de chatbots educativos en aulas virtuales universitarias: una revisión sistemática
Alva-Franco, C. I., Sánchez, M. J. S., Estela-Paredes, R. R., Torres, C. Y. C., & Nieto-Rivas, E. A. (2026) · Revista Tribunal · doi:10.59659/revistatribunal.v6i16.516 · Cited by 1 · see citing works
Wei, J., & Shang, Y. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1965371 · Cited by 1 · see citing works
“Generative artificial intelligence (GenAI) chatbots are increasingly used in university learning for explanations, feedback, and writing support (Badger et al., 2026).”
Percepciones, uso y confianza en IA generativa según género en estudiantes universitarios
Rodríguez-Rodríguez, E., Moreno‐Guzmán, M., Fernández, I. O., Hervás-Pérez, J. P., Mateos‐Aparicio, I., García‐Alonso, A., Pérez-Rodríguez, M. L., Armada, R. T. J. D. L. P., Raggio, J., Gómez-Gómez, B., et al. (2026) · GDI Revista de investigación de Género Diseño e Innovación · doi:10.63206/gdi.2026.4.3
Dickerson, P. (2026) · Artificial Intelligence Advances in Education · doi:10.66818/aiaie.v1i2.912
Franco, A. (2026) · Research Square · doi:10.21203/rs.3.rs-10362021/v1
Synergizing Growth Mindset and Generative AI in K-12 Classrooms
Gaffar, M. A., Aulawy, M. Z., Cahyana, W., Handaka, I. B., & Maharani, E. A. (2026) · Journal of Educational Technology Innovation and Applications · doi:10.56741/iistr.jetia.002274
Generative AI and Intercultural Competence in Higher Education: A Scoping Review (2022–2026)
Li, J., Han, J., & Einfalt, J. (2026) · Education Sciences · doi:10.3390/educsci16101691
Pérez‐López, R., González-Vallejo, R., Lucia‐Palacios, L., Melero‐Polo, I., & Trifu, A. (2026) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-026-00629-2
Enhancing Student Learning in Online STEM Labs Through Live Support: The Lab Assist Project
Ismail, D. K. B., Kane, A., & Kear, K. (2026) · Education Sciences · doi:10.3390/educsci16101724
- Authors
- Badger, M., López, N., & Nissen, C. F. R.
- Affiliations
- Copenhagen Business School (DK)
- ORCID
- Badger, Merete
- Venue
- International Journal of Technology in Education
- Publisher
- ISTES Organization
- Published
- 2026-01-01
- Volume
- 9
- Issue
- 1
- Pages
- 43-69
- ISSN
- 2689-2758 (ISSN-L)
- OpenAlex
- W7118066178
- Cited by
- 9
- References
- 0
- Citations by year
- 2026: 9
- FWCI
- 75.93
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- International Journal of Technology in Education (publishedVersion, cc-by-nc-sa)
- CBS Research Portal (Copenhagen Business School) (publishedVersion, cc-by-nc-sa)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- generative AI chatbots, student learning, higher education, student motivation, student engagement, self-efficacy, student self-regulation, critical thinking, academic integrity, overreliance on AI, AI literacy, personalized learning, self-directed learning, learning performance
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
