# A Survey of LLM-Based Applications in Programming Education: Balancing Automation and Human Oversight

arXiv: https://arxiv.org/abs/2510.03719 · HTML: https://mlsanity.com/paper/2510.03719/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Oct 2025.

## Built on by (most cited first, 6 of 6)

- [Trust and Reliance on AI in Education: AI Literacy and Need for Cognition as Moderators](https://mlsanity.com/paper/2604.01114/) (2026)
- [What Drives Students' Use of AI Chatbots? Technology Acceptance in Conversational AI](https://mlsanity.com/paper/2602.20547/) (2026)
- [Personalized Worked Example Generation from Student Code Submissions Using Pattern-based Knowledge Components](https://mlsanity.com/paper/2604.24758/) (2026)
- [Retrieval-Augmented Tutoring for Algorithm Tracing and Problem-Solving in AI Education](https://mlsanity.com/paper/2605.12988/) (2026)
- [Automated Program Repair of Uncompilable Student Code](https://mlsanity.com/paper/2510.06187/) (2025)
- [Human-in-the-Loop Control of Objective Drift in LLM-Assisted Computer Science Education](https://mlsanity.com/paper/2604.00281/) (2026)

## Abstract (arXiv)

Novice programmers benefit from timely, personalized support that addresses individual learning gaps, yet the availability of instructors and teaching assistants is inherently limited. Large language models (LLMs) present opportunities to scale such support, though their effectiveness depends on how well technical capabilities are aligned with pedagogical goals. This survey synthesizes recent work on LLM applications in programming education across three focal areas: formative code feedback, assessment, and knowledge modeling. We identify recurring design patterns in how these tools are applied and find that interventions are most effective when educator expertise complements model output through human-in-the-loop oversight, scaffolding, and evaluation. Fully automated approaches are often constrained in capturing the pedagogical nuances of programming education, although human-in-the-loop designs and course specific adaptation offer promising directions for future improvement. Future research should focus on improving transparency, strengthening alignment with pedagogy, and developing systems that flexibly adapt to the needs of varied learning contexts.