A Multimodal Immersive Intervention for Artificial Intelligence Literacy and Metacognitive Self-Regulation in Pre-Service Teacher Education

Abstract

This study examined differences associated with participation in a multimodal immersive instructional sequence designed to support artificial intelligence literacy and metacognitive self-regulation in pre-service teacher education. A quantitatively dominant quasi-experimental pretest–posttest design with an embedded qualitative descriptive component was used. Sixty fifth-year teacher education students from two intact academic sections participated: one section served as the active comparison group (n = 30) and the other received the multimodal intervention (n = 30). The intervention integrated generative AI tasks, immersive virtual reality, haptic interaction, guided reflection, and metacognitive scaffolding across eight sessions. The comparison section addressed the same content and learning objectives through readings, videos, platform activities, and non-immersive simulations. Individual-level ANCOVA yielded statistically significant adjusted differences between sections after controlling for pretest scores. However, treatment condition was completely confounded with section membership, only one section represented each condition, and the analyses could not separate intervention-related differences from section-level influences. Outcomes were measured primarily with study-specific self-report scales supported by expert content review and internal consistency evidence; therefore, the very large standardized differences should be treated as exploratory and potentially influenced by common-method bias, novelty, motivation, and demand characteristics. Reflective journals and brief interviews illustrated critical evaluation of AI outputs, bias identification, planning, monitoring, reflective evaluation, and pedagogical transfer. The findings document the feasibility and promise of an integrated pedagogical sequence, but they do not establish causal effects of immersive VR, haptic interaction, or any individual component.