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, as well as an API-based crawl of Codeforces contest logs (2022–2025) for quantitative analysis, 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.

Autistic children exhibit heterogeneous oral language impairments, necessitating educators to implement personalized teaching content. However, preparing personalized materials remains time-intensive and difficult to maintain coherence, while generative AI’s recent advances in creating customized content show potential to support this process. We first conducted video analysis from educators’ one-on-one classes with autistic students and conducted interviews with therapists to understand their challenges in current teaching practices. Then, we developed a generative AI-empowered prototype, LingoLift, which supports educators to create interest-based, ability-adapted, and coherent teaching materials according to children’s profiles. Finally, we conducted a three-week deployment study with 10 educator-student dyads completing 30 lessons with LingoLift in a specialized education school. Results showed that LingoLift significantly improved lesson preparation efficiency, reduced educators’ workload, and enabled children to achieve positive learning outcomes. We observed educators’ adaptive extensions and innovations, revealing insights into design considerations and future opportunities for AI-assisted inclusive education.