Indirect and Direct AI Scaffolding for Computational Problem Posing: A Pilot Experience Report

2026-07-10

AI Summary

This study investigates how to effectively support learners in formulating high-quality questions across computational education tasks with varying degrees of openness. To this end, two large language model–based scaffolding systems were designed and pilot-deployed: guided questioning (an indirect scaffold) and worked examples (a direct scaffold), evaluated through a unified framework grounded in Bloom’s taxonomy. The work innovatively compares and integrates these two AI scaffolding modalities, proposing a sequential “reflect-then-exemplify” strategy that balances immediate question quality improvement with deeper cognitive engagement. Findings indicate that direct scaffolding yields more pronounced immediate gains in question quality, whereas indirect scaffolding is more favorably received by students and fosters richer reflection on the process of question design.

Abstract

Problem posing is a valuable learning activity in computing education, encouraging learners to actively construct, refine, and reflect on problems rather than simply solving them. This experience report presents the design and pilot deployment of two LLM-powered scaffolding systems for supporting problem posing across two computational scenarios with different levels of task openness. Both systems assessed student-generated problems using Bloom's Taxonomy-based criteria and applied the same assessment framework, differing only in output modality: one provided guiding questions (Indirect scaffolding), while the other offered worked examples (Direct scaffolding). We conducted a within-subjects, counterbalanced pilot study with 20 graduate students and collected problem-quality ratings, user-experience surveys, and post-session interviews. Our deployment showed that both systems supported problem refinement in complementary ways, each offering distinct benefits. Direct scaffolding produced greater immediate improvements, while interviews showed that participants valued Indirect scaffolding for promoting deeper reflection on their own problem design. Based on these findings, we suggest sequencing the two modalities by beginning with Indirect scaffolding to promote reflection, then shifting to Direct scaffolding when learners become stuck. These lessons offer an initial practical strategy for integrating LLM-based scaffolding into computing classrooms.

Problem

• problem posing
• computing education
• AI scaffolding
• LLM-based support
• Bloom's Taxonomy

Innovation

• AI scaffolding
• problem posing
• large language models
• computing education
• Bloom's Taxonomy