Paper Type
ERF
Abstract
Driven by the current blend of curiosity, skepticism, and concern regarding the potential of GenAI to replace teachers in the classroom, this short paper seeks to identify the strengths and weaknesses of current agentic teaching systems. To this end, we first conceptualize what constitutes effective teaching by considering the recent shift towards student-centered learning in higher education, as well as Bloom’s revised taxonomy of educational objectives. Then, we review the literature from both educational sciences and information systems to assess GenAI effectiveness in facilitating learning, across Bloom’s educational objectives in higher education. What we found is that GenAI can facilitate learning across all levels of Bloom’s taxonomy, but significant challenges remain. Based on these insights, we propose future research avenues to enable the IS discipline t0 take a leading role in shaping the future of GenAI in education.
Recommended Citation
Manzke, Leonie S.; Conrad, Colin D. Mr.; Marchildon, Philippe; Raisinghani, Mahesh; and Xie, Runjie, "Artificial Intelligence in the Classroom: Can GenAI Teach Effectively?" (2025). AMCIS 2025 Proceedings. 2.
https://aisel.aisnet.org/amcis2025/paperathon/paperathon/2
Artificial Intelligence in the Classroom: Can GenAI Teach Effectively?
Driven by the current blend of curiosity, skepticism, and concern regarding the potential of GenAI to replace teachers in the classroom, this short paper seeks to identify the strengths and weaknesses of current agentic teaching systems. To this end, we first conceptualize what constitutes effective teaching by considering the recent shift towards student-centered learning in higher education, as well as Bloom’s revised taxonomy of educational objectives. Then, we review the literature from both educational sciences and information systems to assess GenAI effectiveness in facilitating learning, across Bloom’s educational objectives in higher education. What we found is that GenAI can facilitate learning across all levels of Bloom’s taxonomy, but significant challenges remain. Based on these insights, we propose future research avenues to enable the IS discipline t0 take a leading role in shaping the future of GenAI in education.
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