Paper Number
ICIS2025-2062
Paper Type
Short
Abstract
The rise of generative AI (GenAI), particularly large language models like ChatGPT, has sparked widespread experimentation across diverse educational contexts. However, existing research remains fragmented, lacking theoretical integration and design synthesis. This study presents a systematic review of 89 empirical studies on GenAI in education. First, we analyze its varied applications across K–12, higher education, and corporate training, categorizing them into teaching facilitation and learning facilitation. Second, we identify the theoretical frameworks that inform this stream of work, including technology adoption models, learning theories, and human–AI collaboration perspectives. Third, we synthesize findings on GenAI’s impact on learning outcomes. Finally, we outline future research directions focused on long-term learning impacts, academic integrity and assessment innovation, pedagogical integration, inclusive adoption, contextualized theory development, system design, and institutional governance. By integrating insights across disciplines, this study provides a basis for guiding the effective use and design of GenAI in educational research and practice.
Recommended Citation
Liu, Yang; Cui, Wei; Wu, Shuang; and Tan, Wee Kek, "Generative AI in Education: A Review of Applications, Impacts, and Future Research Directions" (2025). ICIS 2025 Proceedings. 13.
https://aisel.aisnet.org/icis2025/learn_curricula/learn_curricula/13
Generative AI in Education: A Review of Applications, Impacts, and Future Research Directions
The rise of generative AI (GenAI), particularly large language models like ChatGPT, has sparked widespread experimentation across diverse educational contexts. However, existing research remains fragmented, lacking theoretical integration and design synthesis. This study presents a systematic review of 89 empirical studies on GenAI in education. First, we analyze its varied applications across K–12, higher education, and corporate training, categorizing them into teaching facilitation and learning facilitation. Second, we identify the theoretical frameworks that inform this stream of work, including technology adoption models, learning theories, and human–AI collaboration perspectives. Third, we synthesize findings on GenAI’s impact on learning outcomes. Finally, we outline future research directions focused on long-term learning impacts, academic integrity and assessment innovation, pedagogical integration, inclusive adoption, contextualized theory development, system design, and institutional governance. By integrating insights across disciplines, this study provides a basis for guiding the effective use and design of GenAI in educational research and practice.
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