The Effects of Structured LLM-Generated Feedback on Programming Assignment Performance

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Education & Learning — Educational Technology (EdTech), Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

A study investigated the impact of structured large language model (LLM)-generated feedback on programming assignment performance, addressing the limitations of traditional compiler messages and unscalable instructor feedback. Researchers designed three distinct LLM feedback types, varying in guidance level, and compared them against a baseline where students received only compiler error messages. The analysis focused on students' time to solution and number of attempts, also considering their programming experience. Results from an online programming course demonstrated that LLM-generated feedback correlated with a faster time to solution compared to the no-feedback baseline. Interestingly, less guided feedback exhibited slightly stronger positive effects. These findings highlight the critical role of feedback structure in student progress and underscore the need for further research into adaptive feedback designs and long-term learning outcomes.

Key takeaway

For educators designing programming courses, consider integrating structured LLM-generated feedback to significantly accelerate student problem-solving. Your students could achieve faster solutions compared to relying solely on compiler errors, with less guided feedback potentially yielding better results. Explore adaptive feedback designs that balance guidance with student autonomy to optimize learning outcomes and scale support effectively.

Key insights

LLM-generated feedback, especially less guided forms, significantly reduces programming assignment solution time for students.

Principles

• Feedback structure impacts student problem-solving.
• Automated feedback can scale beyond instructors.
• Less guided feedback may be more effective.

Method

The study compared three LLM-generated feedback types (varying guidance) against a compiler-only baseline in an online programming course, measuring time to solution and attempts.

In practice

• Implement LLM feedback for faster student solutions.
• Experiment with varied guidance levels in feedback.
• Consider LLM feedback for scaling programming education.

Topics

• Large Language Models
• Automated Feedback
• Programming Education
• Student Performance
• Feedback Design
• Educational Technology

Best for: Research Scientist, AI Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.