Artificial Intelligence in Microteaching Lesson Study: Enhancing Pre-Service Teachers’ Confidence and Instructional Quality

Abstract

Background/purpose. The marginalization of art education globally has prompted concerns about the instructional competence of art teachers. This study probed the potential of microteaching lesson study as a remedy, with a novel integration of generative artificial intelligence. Materials/methods. This was a pre-test/post-test controlled study with quantitative data collected from two groups of pre-service visual art teachers. They partook in collaborative lesson planning: one aided by generative chatbots and the other not, both followed by microteaching activities. A comparison group adhered to a standard university curriculum. Results. Both treatment conditions, with or without conversational agent usage, significantly improved overall lesson plan quality, particularly in terms of facilitating art-related discourse. Furthermore, both experimental groups outperformed untreated subjects in overall teaching competence. Specifically, the chatbot-supported condition scored significantly higher in the instruction domain at the post-test. However, post-intervention teaching self-efficacy scores indicated a uniform decline compared to pre-existing levels, without significant intergroup variance. Conclusion. This study provides empirical support for microteaching lesson study as a potent tool to enhance specific teaching skills within visual art education, irrespective of artificial intelligence integration. Furthermore, the findings of this investigation underscore the need for continued research into the effective deployment of technology, such as generative conversational agents, in teacher training programs.