Mitigating "Epistemic Debt" in Generative AI-Scaffolded Novice Programming using Metacognitive Scripts
arXiv:2602.20206v2 Announce Type: replace-cross Abstract: The democratization of Large Language Models has given rise to vibe coding, where novice programmers prioritize semantic intent over syntactic implementation. Without pedagogical guardrails, we argue this is fundamentally misaligned with cognitive skill acquisition. Drawing on Kirschner's distinction between cognitive offloading and outsourcing, 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: developers 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
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Abstract:The democratization of Large Language Models has given rise to vibe coding, where novice programmers prioritize semantic intent over syntactic implementation. Without pedagogical guardrails, we argue this is fundamentally misaligned with cognitive skill acquisition. Drawing on Kirschner's distinction between cognitive offloading and outsourcing, 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: developers 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 were recruited via Prolific and this http URL to represent AI-native learners. We compared three conditions: manual (control), unrestricted AI (outsourcing), and scaffolded AI (offloading). The scaffolded condition employed a novel Explanation Gate -- a real-time LLM-as-a-Judge framework enforcing a teach-back protocol before generated code could be integrated. Results reveal a collapse of competence: both AI groups significantly outperformed the manual control on functional utility (p < .001) and did not differ from each other (p = .64), yet unrestricted AI users suffered a 77% failure rate on a subsequent 30-minute AI-blackout maintenance task, vs. only 39% in the scaffolded group. Qualitative analysis suggests successful vibe coders naturally self-scaffold, treating AI as a consultant rather than a contractor. We discuss implications for AI-generated software maintainability and propose that future learning systems must enforce metacognitive friction to prevent mass production of unmaintainable code. Replication package: this https URL
Subjects:
Software Engineering (cs.SE); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Emerging Technologies (cs.ET); Multiagent Systems (cs.MA)
Cite as: arXiv:2602.20206 [cs.SE]
(or arXiv:2602.20206v2 [cs.SE] for this version)
https://doi.org/10.48550/arXiv.2602.20206
arXiv-issued DOI via DataCite
Submission history
From: Sreecharan Sankaranarayanan [view email] [v1] Sun, 22 Feb 2026 21:25:04 UTC (470 KB) [v2] Tue, 31 Mar 2026 05:12:15 UTC (1,249 KB)
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