Unpacking ethics-domain of intelligent-TPACK scale in relation to in-service teachers’ trust and distrust

This multiple case study explores how in-service teachers’ technical knowledge of artificial intelligence (AI) relates to their trust in using AI in educational contexts, and how this trust is shaped by their ethical perceptions. Framed within the Ethics dimension of the Intelligent-TPACK framework (Celik, 2023), this study focuses on four ethical constructs (i.e., transparency, fairness, accountability, and inclusiveness) as indicators of ethical AI and examines how teachers’ trust plays out in relation to these constructs. Data were drawn from written reflections of seven in-service teachers teaching across K-12 levels. These reflections were collected following their participation in a two-week, AI-focused online learning module designed to foster both technical and ethical understanding as part of a graduate-level computer science and instructional technology course. Findings suggest that without solid foundational technical knowledge, teachers often struggle to recognize how human decisions shape AI systems and outcomes. Moreover, the ethical constructs were found to be deeply interconnected and dynamic, with some constructs (e.g., fairness and accountability) often emerging together. In addition, while teachers frequently referred to fairness, inclusiveness, and accountability concerns, other ethical constructs such as transparency and human accountability in decision-making were not addressed nearly as often, emerging as areas that need greater attention in teacher education initiatives. Overall, these insights highlight the ways teachers’ classroom priorities influence how they interpret and engage with ethical considerations in AI, offering important implications for defining and operationalizing the Ethics dimension of Intelligent-TPACK in teacher education.