Beyond AI Delegation: A Prompt Pattern Framework for Productive Struggle and Evaluative Judgement in Secure Coding Education
For educators in computing, this framework provides a structured way to integrate GenAI without undermining student learning, though it remains a design proposal without empirical validation.
The paper introduces a prompt pattern framework for secure coding education that prevents students from delegating reasoning to AI while preserving productive struggle and evaluative judgement. The framework maps nine prompt patterns to pedagogical constructs and demonstrates applicability in an Advanced Secure Coding module design.
Large language models make it easy for students to delegate writing, analysis, and problem-solving to automated systems, bypassing the effortful engagement that produces lasting understanding. We introduce a practical framework that helps educators keep GenAI in the course without removing the cognitive demands that make it worthwhile. We apply Design Science Research (DSR) to synthesise and adapt a taxonomy of nine prompt engineering patterns from established catalogs in the computer science literature, mapped to two pedagogical constructs: Productive Struggle and Evaluative Judgement. A course design for an Advanced Secure Coding module, structured using the DELTA framework, demonstrates the artifact's applicability. Nine prompt patterns, each mapped to a specific pedagogical function, give instructors fine-grained control over how students interact with AI. The secure coding design shows how three patterns (Flipped Interaction, Alternative Approaches, and Cognitive Verifier) scaffold vulnerability discovery and remediation while keeping students in the reasoning role. The framework provides a replicable approach to designing AI-augmented learning experiences that preserve student reasoning, and establishes a structured basis for future empirical evaluation in live course settings.