Integrating AI into pre-service teacher training for reflective interpretation of fine art: a qualitative study in Kazakhstan

Abstract

Artificial intelligence (AI) is increasingly integrated into education, yet its role in supporting multimodal reflective processes in discipline-specific learning, particularly in fine arts, remains underexplored. This study examines how AI can mediate reflective engagement among pre-service teachers in the interpretation of paintings within a pedagogically structured framework. A qualitative design was employed with 28 pre-service teachers in Kazakhstan. Data were generated through an iterative multimodal reflective process, including individual reflection, AI-mediated interpretation, reflective comparison, and focus group discussions. A four-dimensional framework—descriptive-contextual, formal-compositional, technical-spatial, and individuality and uniqueness—guided reflective engagement, and thematic analysis with triangulation was applied. Findings indicate that the role of AI is dimension-dependent. It most effectively supports reflection on formal-compositional and technical-spatial aspects of paintings by structuring attention to visual elements, but shows clear limitations in enabling culturally grounded and symbolic meaning-making. AI primarily functions as a mediating tool that facilitates structured reflective engagement rather than replacing human interpretive processes. The study contributes by conceptualizing artistic interpretation as a multimodal reflective process and by positioning AI as a mediator within reflective practice. It further proposes a model for integrating AI into fine arts teacher education as a supplementary resource that supports, but does not replace, human-centered meaning-making. Effective AI integration requires alignment between reflective frameworks, technological affordances, and the interpretive nature of disciplinary learning.