From Code Generation to Code Auditing: Constrained AI Scaffolding, Transfer, and Cognitive Efficiency in Debugging
IEEE Access, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Basım Tarihi: 2026
- Doi Numarası: 10.1109/access.2026.3737031
- Dergi Adı: IEEE Access
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC, Directory of Open Access Journals
- Anahtar Kelimeler: AI scaffolding, cognitive load, Generative AI, learning transfer, metacognition, programming education
- Galatasaray Üniversitesi Adresli: Hayır
Özet
Generative artificial intelligence is reshaping programming education, yet its effects on skill development depend partly on howlearners interact with artificial intelligence-supported systems. This study introduces the Artificial Intelligence-Scaffolding Interaction Framework, which conceptualizes constrained, question-driven artificial intelligence as a cognitive scaffold rather than a direct solution provider. A quasi-experimental study with 54 novice programmers compared Socratic artificial intelligence scaffolding with traditional Web-based information retrieval during debugging activities. Students in the Socratic condition obtained higher immediate debugging-transfer scores, corresponding to a moderate effect size (Hedges’ g = 0.45), although the primary between-group difference did not reach conventional statistical significance (p =.100). The largest item-level difference was observed for mental code tracing. Perceived-workload profiles also showed lower Effort in the Socratic condition; however, this difference did not remain statistically significant after correction for multiple comparisons. Exploratory moderation analyses detected no significant interaction with prior academic achievement or artificial intelligence familiarity, a pattern consistent with, but not sufficient to establish, the proposed Equalizer Hypothesis. Post-task reflections in the Socratic condition were longer and contained more reasoning-oriented language, whereas trial-and-error references were more common in the search condition. Together, the findings suggest constrained Socratic scaffolding as a promising instructional design for debugging education while highlighting the importance of distinguishing observed learning outcomes from the cognitive mechanisms proposed to explain them.