Bridging the Physicality Gap: Conversational Repair Strategies in AI-Supported Maker Learning

Abstract

Generative artificial intelligence (AI) systems, specifically Large Language Models (LLMs), are increasingly used as collaborative problem-solving tools, yet their efficacy in physical environments like makerspaces remains unexplored. This study presents a cross-generational UX analysis of adults and parent-child dyads interacting with conversational AI (Copilot, ChatGPT) during physical engineering tasks. Through episodic qualitative coding of 54 interaction episodes across two multi-layer maker activities (paper roller coasters and squishy circuits), we identify the "Physicality Gap". We introduce the "Prompt Repair Taxonomy", that captures "Context Grounding," "Constraint Reframing," "Granularity Adjustment," and "Logic Interrogation", which demonstrates that while AI excels at text-based ideation, it lacks spatial and material awareness. Users are forced into a high cognitive state of "Critical Verification" to debug AI outputs against physical constraints. Finally, we outline UX design implications for future digital library infrastructures and argue for multi-modal, spatially aware AI interfaces.