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[2602.20206] Mitigating "Epistemic Debt" in Generative AI-Scaffolded Novice Programming using Metacognitive Scripts
Summary
This paper explores the concept of 'Epistemic Debt' in novice programming using generative AI, proposing metacognitive scripts to enhance learning and maintainability of AI-generated code.
Why It Matters
As generative AI tools become more prevalent in programming education, understanding their impact on cognitive skill acquisition is crucial. This research highlights the risks of over-reliance on AI, which can lead to a lack of foundational skills among novice programmers, ultimately affecting software maintainability.
Key Takeaways
- Generative AI can lower barriers for novice programmers but may lead to 'Epistemic Debt'.
- Unrestricted AI use results in lower competence and higher failure rates in maintenance tasks.
- Scaffolded AI approaches improve learning outcomes by enforcing metacognitive practices.
- The study emphasizes the need for pedagogical frameworks in AI-assisted learning.
- Future educational tools should incorporate mechanisms to promote self-scaffolding.
Computer Science > Software Engineering arXiv:2602.20206 (cs) [Submitted on 22 Feb 2026] Title:Mitigating "Epistemic Debt" in Generative AI-Scaffolded Novice Programming using Metacognitive Scripts Authors:Sreecharan Sankaranarayanan View a PDF of the paper titled Mitigating "Epistemic Debt" in Generative AI-Scaffolded Novice Programming using Metacognitive Scripts, by Sreecharan Sankaranarayanan View PDF HTML (experimental) Abstract:The democratization of Large Language Models (LLMs) has given rise to ``Vibe Coding," a workflow where novice programmers prioritize semantic intent over syntactic implementation. While this lowers barriers to entry, we hypothesize that without pedagogical guardrails, it is fundamentally misaligned with cognitive skill acquisition. Drawing on the distinction between Cognitive Offloading and Cognitive Outsourcing, we argue that unrestricted AI encourages novices to outsource the Intrinsic Cognitive Load required for schema formation, rather than merely offloading Extraneous Load. This accumulation of ``Epistemic Debt" creates ``Fragile Experts" whose high functional utility masks critically low corrective competence. To quantify and mitigate this debt, we conducted a between-subjects experiment (N=78) using a custom Cursor IDE plugin backed by Claude 3.5 Sonnet. Participants represented "AI-Native" learners across three conditions: Manual (Control), Unrestricted AI (Outsourcing), and Scaffolded AI (Offloading). The Scaffolded condition utilized...