Novel Problem Generation for Programming Pedagogy in the LLM Era: A Non-Leakage Methodology

Vietnamese chuyên Tin and IOI-feeder programming pedagogy now operates inside a sharp constraint: the canonical public competitive-programming corpus (Codeforces archive, USACO, AtCoder, VNOI, Project Euler) and the standard programming-LLM benchmarks (HumanEval, MBPP, APPS) are already absorbed into contemporary code-capable model pre-training, so any assessment item that names a known problem, a known solution kernel, or a known reduction is leakage-vulnerable by construction. Reusing the public corpus as-is for assessment thus measures the learner's access to a model more than the learner's mastery of the underlying skill. This paper synthesises the training-set-contamination literature (Sainz et al. 2023; Riddell, Ni and Cohan 2024; Yang et al. 2023; Roberts et al. 2023; Jain et al. 2024 LiveCodeBench; Carlini et al. 2021), the LLM-hallucination and verification literature (Magesh et al. 2024; Mitchell et al. 2023 DetectGPT), the parametric-exercisegeneration tradition in CS education (Ihantola et al. 2010; Edwards et al. 2014 Pythy), variation theory (Marton and Booth 1997; Marton, Runesson and Tsui 2004), code-similarity detection (Aiken et al. 2003 MOSS winnowing; Prechelt et al. 2002 JPlag), fuzzing-for-coverage discipline (Sutton et al. 2007; Klees et al. 2018), problem-posing in mathematics education (Pólya 1945; Singer, Ellerton and Cai 2013), competitive-programming setter craft (Paşin, Schmid and Sundholm 2022; IOI 2024 Syllabus), and the AI-era assessment-design empirical anchor (Bastani et al. 2024 GPT-Tutor RCT; Kosmyna et al. 2025 cognitive-debt EEG) into a four-rule non-leakage criterion set and a fourtechnique methodology — T1 parametric variation, T2 uniquecontext grounding, T3 compositional novelty, T4 audit-via-LLMself-test — applied as a problem-set design recommendation (60 percent T1 / 25 percent T2 / 15 percent T3, all gated by T4) with an explicit deployment workflow and audit-loop integration. The methodology is honest that novelty is a moving target against an advancing training cutoff; the contribution is a structurally durable workflow, not a permanent solution.