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AI evidence extraction

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.

7/10
Relevance
1/4
Quality (LMQS)
E
Evidence
0
Citations
0.00
FWCI

Methodology & findings

Study design

Systematic review of 89 empirical studies on generative AI in education.

Sample

N = 89, 2 groups

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; Not explicitly reported in the abstract. Potential risks include publication bias (only peer-reviewed studies may be included), selection bias in study inclusion criteria, and lack of reporting on heterogeneity across reviewed studies.; Not explicitly stated in the abstract. Potential risks inherent to systematic reviews include publication bias (studies with positive findings more likely to be published), selection bias in study inclusion criteria, and heterogeneity across the 89 studies reviewed.

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.
  • Future research directions identified 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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