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

Key Finding

Socratic questioning, reflection prompts, misconception checks, and mandatory pauses produce better K-12 engagement than directive answer-giving AI tutors.

Synthesis

SocratiCode demonstrates a participatory design evolution from directive AI tutor to Socratic learning companion for K-12 programming. Over four weeks with two Python learners, the system shifted from flexible tutorial generation toward dialogic support: guided questioning instead of answers, reflection prompts, misconception checks, incremental hints, and mandatory pauses requiring learner input. This Socratic shift improved explanation clarity and problem-solving engagement. The findings directly reinforce the Codify Socratic Tutoring Programming approach of discovery-based learning over direct answer generation, but extend it to the K-12 context where Cognitive Load Theory concerns are particularly acute. The emphasis on mandatory pauses and reflection aligns with Metacognition and Self Regulated Learning scaffolding strategies. The authors argue that AI tutoring is most effective as a companion within a human-guided framework, not an answer engine — a principle that resonates with the Human In The Loop AI architecture and the findings from Structured LLM Feedback Programming that less guided feedback may be more effective.

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Citation

preprint, A. (2026). Towards SocratiCode: Designing a Generative AI-Based Programming Tutor for K-12 Students through a 4-Week Participatory Design Study