Instructional scaffolding in dialogue-based programming tutoring

Abstract

This study examines how instructional scaffolding is enacted in dialogue-based artificial intelligence (AI) tutoring systems for programming education and evaluates the levels of cognitive demand they support. While AI tutors can guide novice learners through programming tasks, it remains unclear whether they promote meaningful higher-order thinking or primarily support procedural task completion. Using a mixed-methods approach, 1,255 tutor utterances from 36 tutoring sessions were analyzed using a dual-layer coding framework grounded in instructional scaffolding theory and Bloom’s revised taxonomy. Results show that instructional support is concentrated at the understanding and applying levels, with prompting and explaining as dominant strategies. Higher-order cognitive scaffolding (analyzing, evaluating, creating) was rare or absent. Sequential patterns revealed repetitive prompting–explaining cycles with limited scaffold progression. These findings indicate that AI tutoring effectively supports foundational learning but lacks mechanisms for deeper cognitive engagement. This study highlights the need for pedagogically informed AI tutor design and provides actionable insights for educators and system developers to integrate AI tools in ways that promote higher-order thinking and independent problem-solving.