Case-based study reveals pre-service teachers emphasize curriculum alignment and fairness but struggle with accountability in generative AI use, highlighting gaps in teacher training.
Generative artificial intelligence (GenAI) offers opportunities for innovative teaching but also raises ethical concerns. While pre-service teachers are expected to integrate GenAI responsibly, little is known about how they engage in reasoning processes that guide such integration. This study investigates pre-service teachers’ pedagogical and ethical reasoning through case-based scenarios involving GenAI. Pre-service teachers (N = 49) from two Finnish universities reflected on GenAI-related pedagogical ethical cases. Their reflections were analyzed using Shulman’s Pedagogical Reasoning and Action Model and Rest’s Four-Component Model. Findings show that participants’ pedagogical reasoning focused on aligning GenAI with curricula, adapting outputs, and critical evaluation while deeper reflections were less frequent. Ethical reasoning was strongest in fairness cases and motivation-related dimensions, but weaker in accountability cases and policy-related dimensions. This study contributes to understanding pre-service teachers’ pedagogical and ethical reasoning for GenAI integration and informs the development of teacher education programs.
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Dalyanci et al. (2026) studied this question.
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