Generative AI in Education: A Review of Applications, Impacts, and Future Research Directions
Yang Liu, Wei Cui, Shuang Wu, Wee Kek Tan · Journal of the Association for Information Systems · 2025
AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.
Methodology & findings
Study design
Systematic review of 89 empirical studies on generative AI in education.
Sample
N = 89, 1 group
Main result
The study synthesized findings from 89 empirical studies on GenAI in education, identifying that "existing research remains fragmented, lacking theoretical integration and design synthesis." The review categorized GenAI applications into teaching facilitation and learning facilitation across K–12, higher education, and corporate training contexts, and identified theoretical frameworks including "technology adoption models, learning theories, and human–AI collaboration perspectives" that inform GenAI research in education.
Reports effect sizes.
Research paradigm
Mixed-methods (integrating quantitative and qualitative evidence from 89 empirical studies)
Author conclusions
The authors conclude that "By integrating insights across disciplines, this study provides a basis for guiding the effective use and design of GenAI in educational research and practice." They emphasize the importance of synthesizing findings across theoretical frameworks including "technology adoption models, learning theories, and human–AI collaboration perspectives."
Risk of bias
Not explicitly stated in the abstract
Open questions raised
- Future research directions include: long-term learning impacts, academic integrity and assessment innovation, pedagogical integration, inclusive adoption, contextualized theory development, system design, and institutional governance.
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