Towards SocratiCode: Designing a Generative AI-Based Programming Tutor for K-12 Students through a 4-Week Participatory Design Study

TL;DR

This study develops and refines a Socratic AI-based programming tutor for K-12 students through participatory design, emphasizing dialogic support over directive explanations to enhance beginner learning experiences.

Contribution

The paper introduces SocratiCode, an adaptive AI tutor that evolves into a Socratic, dialogic system for K-12 programming education based on iterative user feedback.

Findings

Socratic shift improved explanation clarity and engagement.

Adaptive system better aligned with novice learners' needs.

Combining AI with human guidance enhances learning outcomes.

Abstract

Generative AI creates new opportunities for programming education, but many existing systems remain overly directive, producing lengthy explanations and premature solutions that can overwhelm K-12 novices. In this paper, we present a participatory design study of how an adaptive tutorial system, SocratiCode, evolved toward a Socratic tutoring model for beginner programming instruction. Drawing on weekly learner feedback, we iteratively refined the system over a four-week study with two K-12 students learning Python. Across iterations, the system shifted from flexible tutorial generation toward a more dialogic form of support characterized by guided questioning, reflection prompts, misconception checks, incremental hints, and mandatory pauses for learner input. Our preliminary observations suggest that this Socratic shift improved explanation clarity, supported problem-solving engagement, and better aligned instruction with novice learners' needs, especially when combined with human guidance. We argue that generative AI in K-12 programming education may be most effective not as an answer engine, but as a Socratic, adaptive learning companion embedded within a human-guided instructional framework.