# From Generation to Adaptation: Comparing AI-Assisted Strategies in High School Programming Education

arXiv: https://arxiv.org/abs/2506.15955 · HTML: https://mlsanity.com/paper/2506.15955/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2025.

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

- [Evaluating the Effectiveness of Large Language Models in Solving Simple Programming Tasks: A User-Centered Study](https://mlsanity.com/paper/2507.04043/) (2025)
- [Exploring Teacher-Chatbot Interaction and Affect in Block-Based Programming](https://mlsanity.com/paper/2603.20211/) (2026)

## Abstract (arXiv)

This exploratory case study investigated two contrasting pedagogical approaches for LCA-assisted programming with five novice high school students preparing for a WeChat Mini Program competition. In Phase 1, students used LCAs to generate code from abstract specifications (From-Scratch approach), achieving only 20% MVP completion. In Phase 2, students adapted existing Minimal Functional Units (MFUs), small, functional code examples, using LCAs, achieving 100% MVP completion. Analysis revealed that the MFU-based approach succeeded by aligning with LCA strengths in pattern modification rather than de novo generation, while providing cognitive scaffolds that enabled students to navigate complex development tasks. The study introduces a dual-scaffolding model combining technical support (MFUs) with pedagogical guidance (structured prompting strategies), demonstrating that effective LCA integration depends less on AI capabilities than on instructional design. These findings offer practical guidance for educators seeking to transform AI tools from sources of frustration into productive learning partners in programming education.