# Mitigating "Epistemic Debt" in Generative AI-Scaffolded Novice Programming using Metacognitive Scripts

arXiv: https://arxiv.org/abs/2602.20206 · HTML: https://mlsanity.com/paper/2602.20206/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 42 references that are in the map.
- First posted on arXiv 22 Feb 2026.

## Builds on (most cited first, 5 of 5)

- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Self-Preference Bias in LLM-as-a-Judge](https://mlsanity.com/paper/2410.21819/) (2024)
- [Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task](https://mlsanity.com/paper/2506.08872/) (2025)
- [Beware of Metacognitive Laziness: Effects of Generative Artificial Intelligence on Learning Motivation, Processes, and Performance](https://mlsanity.com/paper/2412.09315/) (2024)
- [Beyond the Surface: Measuring Self-Preference in LLM Judgments](https://mlsanity.com/paper/2506.02592/) (2025)

## Built on by (most cited first, 2 of 2)

- [The Substrate Collapse: AI Code Generation Invalidates Authorship-Based Knowledge Metrics](https://mlsanity.com/paper/2606.20882/) (2026)
- [Agents That Teach: Towards Designing Incidental Learning Back into AI-Assisted Software Development](https://mlsanity.com/paper/2607.06101/) (2026)

## Abstract (arXiv)

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 UserInterviews.com 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: https://github.com/sreecharansankaranarayanan/vibecheck