2026 ASEE Annual Conference & Exposition

Artificial intelligence offers new opportunities to extend education by providing adaptive and personalized support. In manufacturing education, where evolving technologies and widening skill gaps challenge both students and practitioners, these tools can help provide individualized learning guidance. This work continues the development of a GPT-based Manufacturing Advisor that aims to function as a personal tutor by assessing learner expertise and guiding question-based exploration of manufacturing topics.
Earlier work established a foundation of manufacturing questions organized into three categories: process, process parameters, and sub-process. Building on this structure, the present study introduces fourth category of design for manufacturability, classifying questions as WH-types and expertise modeling framework that predicts question progression and evaluates how question sequencing affects system behavior. Using Bloom’s taxonomy and a probabilistic model of question transitions inspired by prior works in educational modeling, the system predicts logical chains of up to five questions for each expertise level—beginner, intermediate, and expert. These guided chains serve two purposes: they suggest next-step questions that support conceptual growth, and they provide a mechanism for estimating a user’s level based on question patterns.
The study also compares model responses when the same questions are asked individually versus within these guided chains. Responses are evaluated by their length, topical relevance, and vocabulary complexity. Preliminary results indicate that contextually linked question chains lead to more detailed and technically consistent answers, suggesting that sequencing improves both content depth and alignment with manufacturing reasoning.
This work advances the Manufacturing Advisor from static question answering toward adaptive tutoring. These developments move the system closer to serving as an AI-based tutor that supports scalable, flexible learning pathways in manufacturing education.