When AI Is Wrong on Purpose: How Students Respond to Buggy GenAI Code

As generative AI becomes central to software development, CS education is shifting toward prompt-centered workflows where students describe intended behavior in natural language to elicit code. But professional practice demands careful review of GenAI output that may look correct yet harbor subtle faults — a challenge in CS1, where current models solve tasks correctly and dull students' incentive to inspect generated code. Padurean et al. (2026) investigate how prompt-centered programming activities can be designed to deliberately surface buggy GenAI code, forcing students to practice verification rather than blind trust. The work speaks directly to the Over Reliance problem and the Cognitive Offloading temptation of strong code models, and complements Code Review GenAI Cs1, which found oral code review preserves learning outcomes under rising AI use. By intentionally injecting faults, the approach reframes Generative AI in CS Education as a metacognitive training ground rather than an answer engine, reinforcing AI Literacy and Metacognition. Implications for Student Experience and Formative Assessment design suggest that 'wrong-on-purpose' perturbations are a scalable way to build debugging habits in an era where Reshaping CS Education GenAI is already underway.

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Victor-Alexandru Padurean, Kaitlin Riegel, Alkis Gotovos, Jyotika Mahapatra, Ahana Ghosh, Adish Singla (2026). When AI Is Wrong on Purpose: How Students Respond to Buggy GenAI Code. arXiv:2607.05068. arXiv:2607.05068 [cs.SE, cs.CY].