AI-assisted apprenticeship: Evaluating LLMs as real-time tutors for hands-on construction training

Traditional apprenticeship models struggle to scale as the construction industry faces a growing shortage of skilled workers and an aging workforce. This study evaluates the potential and strategies of Large Language Models (LLMs) to support apprentices in learning hands-on construction tasks through real-time, conversational instruction. Drawing on prior research in conversational AI and intelligent tutoring systems, we conduct a comparative analysis of LLM-based guidance versus traditional video demonstrations in controlled masonry tasks. Through a mixed-methods approach, we assess task performance, interaction patterns, and participants’ self-reported confidence and understanding. Findings from our exploratory comparative study suggest that LLMs can deliver relevant, adaptive, and context-aware procedural guidance. However, limitations emerged in conveying tacit knowledge and adapting tool use to the specific task context. The results underscore the importance of interface design and instructional modality in sustaining engagement. This work offers early insights into the design of scalable, AI-assisted learning systems for skilled trades.