Clustering Pre-Service Teachers' Reflections on Generative AI: Linguistic, Semantic, and Emotional Trajectories in Online Discussions

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Abstract

This study investigates pre-service teachers’ reflective responses to generative AI through linguistic analysis of online discussions across four asynchronous courses. Using LIWC and SBERT, four distinct reflection patterns emerged: Critical Analysts, Strategic Thinkers, Personal Reflectors, and Practical Problem-Solvers. The clustering and linguistic analysis revealed different language use in the high-emotion/high-self-reflection and information-focused groups. The former showed a high frequency of using affective and introspective language, while the latter used formal and analytic language. Reflection styles varied by course context and evolved over time, influenced by assignment design. Findings suggest that reflection is shaped by pedagogical context. The study demonstrates the value of computational linguistics in capturing emotional, cognitive, and temporal dimensions of reflections in online course discussions.

Authors

• Jaesung Hur, Florida State University

• Jewoong Moon, University of Alabama

• Gi Woong Choi, Florida State University

• Haesol Bae, University at Albany - SUNY

• Jaesung Park, University at Albany - SUNY