Understanding the Role of Large Language Models in Competitive Programming

2025-09-19

AI Summary

This study investigates the systemic impact of large language models (LLMs) on competitive programming ecosystems, focusing on fairness crises and governance failures exacerbated by AI-assisted cheating. Employing a mixed-methods approach—including 37 in-depth interviews and 207 globally distributed participant surveys—the research identifies how LLMs reshape workflows and role boundaries among competitors, problem authors, coaches, and platform administrators. It proposes a novel, chess-inspired multi-tiered governance framework integrating real-time LLM behavior detection, peer-based oversight, and offline cross-verification of submissions—balancing technical augmentation with integrity preservation. The study uncovers critical regulatory gaps in current competition policies and offers actionable, empirically grounded interventions. By bridging theory and practice, it establishes both a conceptual foundation and an implementable governance paradigm for high-fidelity technical competitions in the AI era.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Philosophy and Ethics of AI: AI & Law, Justice, Regulation & Governance

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

This paper investigates how large language models (LLMs) are reshaping competitive programming. The field functions as an intellectual contest within computer science education and is marked by rapid iteration, real-time feedback, transparent solutions, and strict integrity norms. Prior work has evaluated LLMs performance on contest problems, but little is known about how human stakeholders -- contestants, problem setters, coaches, and platform stewards -- are adapting their workflows and contest norms under LLMs-induced shifts. At the same time, rising AI-assisted misuse and inconsistent governance expose urgent gaps in sustaining fairness and credibility. Drawing on 37 interviews spanning all four roles and a global survey of 207 contestants, we contribute: (i) an empirical account of evolving workflows, (ii) an analysis of contested fairness norms, and (iii) a chess-inspired governance approach with actionable measures -- real-time LLMs checks in online contests, peer co-monitoring and reporting, and cross-validation against offline performance -- to curb LLMs-assisted misuse while preserving fairness, transparency, and credibility.

Problem

• Investigating human adaptation to LLMs in competitive programming
• Addressing AI misuse and governance gaps for fairness
• Proposing real-time checks and peer monitoring solutions

Innovation

• Empirical analysis of evolving human workflows
• Analysis of contested fairness norms in programming
• Chess-inspired governance with real-time LLM checks