The Impact of AI on the Personal and Collaborative Learning Environments in Higher Education
Abstract
Artificial intelligence (AI) has extensively developed, impacting different sectors of society, including higher education, and has attracted the attention of various educational stakeholders, leading to a growing number of research on its integration into education. Hence, this systematic literature review examines the impact of integrating AI tools in higher education on students' personal and collaborative learning environments. Analysis of 148 articles published between 2021 and 2024 indicates that AI Tools improve personalised learning and assessments, communication and engagement, and scaffolding performance and motivation. Additionally, they promote a collaborative learning environment by providing peer‐learning opportunities, enhanced learner‐content interaction and cooperative learning support. Indeed, strategies such as skills development, ethical use, academic integrity and instructional content design. Acknowledged limitations include ethical considerations, particularly privacy and bias, which require ongoing attention. Hence, it is recommended to create a good balance between AI‐mediated and human interaction in learning environments, a key area of future exploration.
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Platforms with learning analytics provide students real-time feedback on mistakes, time spent, and success/failure patterns, thereby enhancing their capacity to reflect on learning.
“Platforms equipped with learning analytics mechanisms provide students with real-time feedback on their mistakes, the time spent on each task, and patterns of success or failure, thereby enhancing their capacity to reflect on their learning processes [57].”
From Artificial Intelligence in Higher Education: A State-of-the-Art Overview of Pedagogical Integrity, Artificial Intelligence Literacy, and Policy Integration (2025) · cites this paper as [57]
Artificial intelligence has proven particularly effective in supporting metacognitive skills such as self-regulation, performance monitoring, and strategic study planning.
“Artificial intelligence has proven particularly effective in supporting the development of metacognitive skills such as self-regulation, performance monitoring, and strategic study planning.”
From Artificial Intelligence in Higher Education: A State-of-the-Art Overview of Pedagogical Integrity, Artificial Intelligence Literacy, and Policy Integration (2025) · cites this paper as [57]
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- Authors
- Msambwa, M. M., Wen, Z., & Daniel, K.
- Affiliations
- Institute of International and Comparative Education, College of Teacher Education Zhejiang Normal University Jinhua City Zhejiang Province People's Republic of China
- Zhejiang Normal University (CN)
- Corresponding author
- Wen, Zhang
- ORCID
- Wen, Zhang
- Venue
- European Journal of Education
- Publisher
- Wiley
- Published
- 2025-01-07
- Volume
- 60
- Issue
- 1
- ISSN
- 0141-8211 (ISSN-L)
- 1465-3435
- OpenAlex
- W4406190419
- Semantic Scholar
- 4e07c3d240d5712306a2aa5d56c4a8dd65a72c11
- Cited by
- 76
- References
- 157
- Citations by year
- 2026: 53 · 2025: 23
- FWCI
- 79.68
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- European Journal of Education (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Keywords
- artificial intelligence in education, higher education, personalized learning, cooperative learning, student engagement, peer learning, academic integrity, AI ethics, data privacy, algorithmic bias, student motivation, human-AI interaction
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 ›
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