Designing an AI-integrated role-rotation pedagogical model to support competence-related learning in pre-service educational psychologists

Abstract

The rapid integration of artificial intelligence (AI) into higher education has created new opportunities for supporting professional learning. However, AI-supported learning, case-based instruction, collaborative role rotation, and reflective practice are often examined separately. Limited attention has been given to how systematic role rotation can structure pre-service educational psychologists’ critical engagement with AI-generated information, particularly in gifted education contexts. This study employed a Design-Based Research approach to develop, implement, and refine an AI-integrated role-rotation pedagogical model. The eight-week intervention involved 62 undergraduate pre-service educational psychologists enrolled in a Pedagogy and Psychology program at a Kazakhstani university. The model combined generative AI, case-based professional learning, systematic rotation among four professional roles, and structured reflection. Data were collected through reflective journals, semi-structured interviews, observational field notes, and student-generated learning products and examined using reflexive thematic analysis and data triangulation. The qualitative findings indicated participants’ engagement in cognitive, practical, and reflective dimensions of competence-related professional learning. The four datasets contained instances of analytical reasoning, contextual interpretation, intervention planning, collaborative decision-making, and critical examination of AI-generated outputs. Role rotation distributed problem-framing, analytical, intervention-oriented, and reflective responsibilities and created opportunities for perspective-taking and collaborative accountability. AI functioned primarily as a source of preliminary ideas and cognitive support rather than as a substitute for professional judgment. Technological authority, uneven participation, difficulty adapting to unfamiliar roles, and occasional overreliance on AI-generated recommendations were also evident. The principal contribution of the study lies in conceptualizing systematic role rotation as a pedagogical mechanism for structuring critical human–AI interaction in professional learning. By requiring participants to reconsider professional cases and AI-generated recommendations from different role positions, the model organized opportunities for theoretical verification, contextual adaptation, collaborative interpretation, and reflection. Because the study did not include pre–post competence measures or a comparison group, the findings should be interpreted as context-specific qualitative evidence of engagement rather than as demonstrated developmental gains in professional competence.