Integrating Generative AI in Design Education: Metacognitive Patterns in AI-Naive Students

This mid-intervention report documents an exploratory pedagogical intervention, financed through the ER2Digit European Digital Innovation Hub initiative, integrating Large Language Models into a Service-Design course at the University of Bologna. The study addresses the underexplored challenge of AI literacy development in "AI-naive"populations, investigating how fifth-year design students with little prior exposure to generative tools appropriate LLM-based assistants within authentic public administration service design projects. The intervention's original objective of advanced professional integration was radically reformulated into embedded AI literacy development when initial assessment revealed students' poor familiarity with conversational AI interfaces, privacy implications, and foundational mechanisms of probabilistic language generation.At the mid-point of intervention (week 6/12), preliminary findings reveal one unexpected pattern that constitutes this report's central contribution: intentional friction, where technical failures and imperfect AI outputs paradoxically generate deeper understanding of LLM behavior than seamless interactions. Two additional dynamics - assessment blind spots in output evaluation, and ethical pragmatism emerging through situated practice - are noted as preliminary observations warranting post-intervention investigation. This work-in-progress contributes early, real-time documentation of AI appropriation under maximal readiness gaps - a context typically excluded from controlled studies but increasingly common in educational institutions.