Exploring Artificial Intelligence as a Tool for Differentiation and Inquiry in IB Computer Science

Abstract

This mixed-methods study examined the use of artificial intelligence (AI)-supported prompts to enhance the ability of IBDP Computer Science students to work more independently towards the outcomes of a selected programming unit on arrays. Grounded in a problem of practice concerning student dependence on direct teacher explanation while supporting mixed levels of readiness, this bounded single-case study with an embedded intervention used AI-supported prompts designed for inquiry-based and differentiated learning. Quantitative data were collected from 36 students (22 DP1, 14 DP2). Qualitative data were collected through interviews with three teachers, supported by student reflections, classroom
observations, and learning artifacts. Findings indicate that AI-supported prompts improved conceptual understanding, supported a partial shift toward greater autonomy, and enabled more personalized pacing. However, these gains were conditional: teacher mediation remained essential for verification, clarification, and responsible use. The study contributes subject-specific evidence on how AI can function as a tutor-like scaffold in IBDP Computer Science when embedded within structured, differentiated, and teacher-mediated learning environments.