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Artificial Intelligence for Computer Science Education in Higher Education: A Systematic Review of Empirical Research Published in 2003–2023
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GenAI-empowered assessment systems dynamically adapt to individual learners by diagnosing misconceptions and suggesting personalized learning pathways.
“GenAI-empowered assessment systems dynamically adapt to individual learners by utilizing sophisticated algorithms, diagnosing misconceptions, and suggesting personalized learning pathways (Heil et al., 2025 ; Zhu & Zhang, 2025 ).”
From Socratic Mind: Impact of a Novel GenAI-Powered Formative Assessment Tool on Student Learning and Higher-Order Thinking (2026) · cites this paper as Zhu & Zhang, 2025
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A revision of bloom's taxonomy: an overview
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Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-019-0171-0 · Cited by 7,242 · cited 1×
“However, there is a lack of systematic educational perspectives, which has been stressed in other reviews on AI in education (e.g., Chen et al., 2020; Hinojo-Lucena et al., 2019; Kabudi et al., 2021; Tang et al., 2023; Zawacki-Richter et al., 2019; Zhang & Aslan, 2021).”
Wohlin, C. (2014) · Proceedings of the 18th International Conference on Evaluation and Assessment in Software Engineering · doi:10.1145/2601248.2601268 · Cited by 4,234
Huang, A. Y. Q., Lu, O. H. T., & Yang, S. J. H. (2023) · Computers & Education · doi:10.1016/j.compedu.2022.104684 · Cited by 605
Hinojo-Lucena, F.-J., Aznar-Díaz, I., Cáceres-Reche, M.-P., & Romero-Rodríguez, J.-M. (2019) · Education Sciences · doi:10.3390/educsci9010051 · Cited by 400
Yilmaz, R., & Karaoglan Yilmaz, F. G. (2023) · Computers in Human Behavior: Artificial Humans · doi:10.1016/j.chbah.2023.100005 · Cited by 344
Prather, J., Reeves, B. N., Denny, P., Becker, B. A., Leinonen, J., Luxton-Reilly, A., Powell, G., Finnie-Ansley, J., & Santos, E. A. (2024) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3617367 · Cited by 256 · cited 2×
“Prather et al. (2023) found that utilizing GitHub Copilot can boost students’ cognitive coding, metacognition, positive emotions, and learning efficiency.”
"It's Weird That it Knows What I Want": Usability and Interactions with Copilot for Novice Programmers
Prather, J., Reeves, B. N., Denny, P., Becker, B. A., Leinonen, J., Luxton-Reilly, A., Powell, G., Finnie-Ansley, J., & Santos, E. A. (2023) · arXiv · cited 2×
“Prather et al. (2023) found that utilizing GitHub Copilot can boost students’ cognitive coding, metacognition, positive emotions, and learning efficiency.”
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Lee, J., Hung, J.-T., Yilmaz Soylu, M., Popescu, D., Cui, C. Z., Grigoryan, G., Joyner, D. A., & Harmon, S. W. (2026) · Technology, Knowledge and Learning · doi:10.1007/s10758-026-10007-6 · Cited by 23
Metadata
- Authors
- Zhu, M., & Zhang, K.
- Affiliations
- Wayne State University (US)
- Kennesaw State University (US)
- Corresponding author
- Zhu, Meina
- ORCID
- Venue
- Technology, Knowledge and Learning
- Published
- 2025-05-20
- Volume
- 30
- Issue
- 4
- Pages
- 2417-2441
- ISSN
- 2211-1662 (ISSN-L)
- 2211-1670
- OpenAlex
- W4410540339
- Cited by
- 13
- References
- 8
- Citations by year
- 2026: 11 · 2025: 2
- FWCI
- 1.37
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Technology Knowledge and Learning (publishedVersion)
- Topics
- Social Sciences › Education › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Keywords
- artificial intelligence in education, computer science education, higher education, diversity, equity, and inclusion, future research directions
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