The Impact of Artificial Intelligence on Students’ Learning Experience
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Consistent student interactions with intelligent GenAI tutoring systems in university settings lead to improved overall academic achievement.
“Crompton and Burke [48] and Kaledio, Robert [49] highlight that consistent student interactions with intelligent GenAI tutoring systems in university settings lead to improved overall academic achievement.”
From Generative AI in Higher Education: Balancing Innovation and Integrity (2025) · cites this paper as [49]
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Generative AI in Higher Education: Balancing Innovation and Integrity
Francis, N. J., Jones, S., & Smith, D. P. (2025) · British Journal of Biomedical Science · doi:10.3389/bjbs.2024.14048 · Cited by 295 · see citing works
Bećirović, S., Polz, E., & Tinkel, I. (2025) · Smart Learning Environments · doi:10.1186/s40561-025-00384-3 · Cited by 133 · see citing works
Du, X., Du, M., Zhou, Z., & Bai, Y. (2025) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-025-00534-0 · Cited by 44 · see citing works
The Use of AI in Improving Student's Critical Thinking Skills
Lawasi, M. C., Rohman, V. A., & Shoreamanis, M. (2024) · Proceedings Series on Social Sciences & Humanities · doi:10.30595/pssh.v18i.1279 · Cited by 29 · see citing works
“One significant advantage of AI in education is its ability to provide immediate and constructive feedback to students by analyzing vast amounts of data to gain insight into students' strengths, weaknesses, and learning preferences (Kaledio et al., 2024).”
Evaluating the Impact of <scp>AI</scp> Tools on Academic Performance and Student Satisfaction
Wu, Z. (2026) · Journal of Computer Assisted Learning · doi:10.1002/jcal.70225 · Cited by 5 · see citing works
- Authors
- Kaledio, P., Robert, A., & Frank, L.
- Corresponding author
- Kaledio, Potter
- Venue
- SSRN Electronic Journal
- Publisher
- Elsevier BV
- Published
- 2024
- ISSN
- 1556-5068 (ISSN-L)
- OpenAlex
- W4392404912
- Cited by
- 82
- References
- 0
- Citations by year
- 2026: 16 · 2025: 39 · 2024: 27
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Locations
- SSRN Electronic Journal (publishedVersion)
- 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
- artificial intelligence in education, student learning experience, personalized learning, adaptive learning, automated grading, intelligent tutoring systems, virtual learning assistants, student engagement, student motivation, data privacy, AI ethics, overreliance on AI, human instructions
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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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