Turning Free-to-Use Generative AI into Flipped-Interaction Intelligent Tutors: Exploring Student Engagement and Perceptions of Learning

Abstract An engaging and cost-effective digital tool for developing subject-matter knowledge (SMK) could mitigate educational inequities in resource-constrained contexts. This case study explores the effectiveness of free generative artificial intelligence (GAI) platforms, prompted to function as a Flipped-Interaction Intelligent Tutoring System (FIITS), to promote science student-teacher SMK learning. Data were collected across a 10-week intervention from 42 third- and fourth-year natural and physical sciences student teachers at two campuses of a South African university. The students engaged with the FIITS in odd-numbered weeks by pasting a provided prompt into a free GAI platform, and with benchmarking interactive electronic worksheets in even-numbered weeks. Affective, behavioural, and cognitive engagement and perceptions of SMK learning were analysed using post-session questionnaires, end-of-module reflections, and group interviews. Both modalities elicited consistently high and comparable engagement levels, but students reported stronger perceptions of SMK development with the FIITS. They valued its personalisation, relevance, and human-like interactivity, although workload pressures and occasional GAI errors somewhat hindered the FIITS’s effectiveness. These findings suggest that free GAI platforms, prompted to behave as FIITS, may provide equitable, scalable, and pedagogically effective tools for improving SMK in student-teacher education. Recommendations include scaffolding an initial adjustment phase, incorporating varied levels of challenge, and managing novelty and workload to sustain engagement. This study offers early evidence of the pedagogical potential of free flipped-interaction GAI in science teacher education, particularly in contexts where access to costly educational technologies is limited.