Artificial Intelligence-Integrated Instruction for Promoting Deep Learning: A Mixed methods Case Study of Pre-Service English Teachers

Abstract

The study examines how pre-service teachers construct pedagogical knowledge through AI-mediated interaction, challenges, and their adaptation strategies in lesson planning in a Syllabus Design course. The study was held at a public university in Indonesia, employing an exploratory sequential mixed-method design, quantitative data were collected through questionnaires (n = 69), followed by qualitative data from interviews and classroom observations. The results indicate a moderately positive perception of AI use, with a particular emphasis on the enhancement of time efficiency, the support of instructional design understanding, and stimulation of creativity. Nevertheless, numerous obstacles were identified, such as the inability to generate specified learning objectives, the limited contextual relevance, and the challenges associated with prompt formulation. Critical evaluation, contextual modification, peer collaboration, and iterative prompt refinement were among the adaptive strategies in response to these challenges. It indicates that AI serves as a cognitive partner that facilitates the creation of lesson plans that are more contextualized, differentiated, and meaningful. It contributes to the expanding body of literature on AI in education by offering empirical insights into the collaboration between humans and AI in teacher education and emphasizing the significance of critical AI literacy in the development of pedagogical decision-making.