Expanding Pedagogical AI Scaffolding to IT and Cybersecurity Education: A Call for Multi-Institutional Validation

Abstract

While large language models have rapidly infiltrated technical education, evidence suggests their pedagogical impact depends critically on how they are structured and integrated into learning workflows. We present Pedalogical, a framework and platform for embedding metacognitive scaffolding and Feynman Technique-inspired prompts into AI assistants. In preliminary studies within Data Structures courses, students using our pedagogically-structured tool significantly outperformed peers using unstructured ChatGPT (mean scores: 92.7 vs. 74.3, p < 0.001), with greatest gains in conceptual reasoning and documentation quality. We now seek collaborators to validate and extend this framework to IT and cybersecurity education contexts, where structured reasoning about system vulnerabilities, threat modeling, and security trade-offs demands similar metacognitive awareness. This lightning talk will demonstrate the Pedalogical platform, share our scaffolding design patterns, and invite institutions to participate in multi-site validation studies exploring how pedagogical AI design impacts learning outcomes in security analysis, network troubleshooting, and incident response scenarios.

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