WORK IN PROGRESS: A Computer-Aided Design Intelligent Tutoring System Teaching Strategic Flexibility

Abstract

Taking a Computer-Aided Design (CAD) class is a prerequisite for Mechanical Engineering freshmen at many universities, including at Washington State University. The traditional way to learn CAD software is to follow examples and exercises in a textbook. However, using written instruction is not always effective because textbooks usually support single strategy to construct a model. Missing even one detail may cause the student to become stuck, potentially leading to frustration.To make the learning process easier and more interesting, we designed and implemented an intelligent tutorial system for an open source CAD program, FreeCAD, for the sake of teaching students some basic CAD skills (such as Boolean operations) to construct complex objects from multiple simple shapes. Instead of teaching a single method to construct a model, the program first automatically learns all possible ways to construct a model and then can teach the student to draw the 3D model in multiple ways. Previous research efforts have shown that learning multiple potential solutions can encourage students to develop the tools they need to solve new problems.This study compares textbook learning with learning from two variants of our intelligent tutoring system. The textbook approach is considered the baseline. In the first tutorial variant, subjects were given minimal guidance and were asked to construct a model in multiple ways. Subjects in the second tutorial group were given two guided solutions to constructing a model and then asked to demonstrate the third solution when constructing the same model. Rather than directly providing instructions, participants in the second tutorial group were expected to independently explore and were only provided feedback when the program determined he/she had deviated too far from a potential solution. The three groups are compared by measuring the time needed to 1) successfully construct the same model in a testing phase, 2) use multiple methods to construct the same model in a testing phase, and 3) construct a novel model.

Communication in Ai-Assisted Teams During an Interdisciplinary Drone Design Problem

“…A strand of research in this area focuses on the effectiveness of human-AI collaboration in augmenting the ability of humans and/or AI in solving complex problems. Among them, Hu and Taylor (2016) show that a computer-aided design intelligent tutor helps students apply their learned knowledge to solve novel problems, providing suggestions and guiding their exploration. Instructional design agents support novice designers in exploring complex design spaces through a design study with a solar farm design problem (Schimpf et al, 2019).…”

Part-Aware Product Design Agent Using Deep Generative Network and Local Linear Embedding

“…There are also learning-based/data-driven design platforms. For example, Hu and Taylor [3] developed a CAD platform with an intelligent tutoring system. The system can first learn all possible ways to design a 3D model and then instruct users to draw 3D models.…”

“…In the design field, AI has also greatly facilitated human designers' decisionmaking in different design processes. For example, researchers have successfully embedded intelligent agents into traditional computer-aided design (CAD) software (e.g., [2]) and some custom research platforms (e.g., [3,4]) in support of conceptual design and design optimization. This could save a large amount of human labor, thus significantly shorten the cycle and iteration of product design and development.…”

Toward Hybrid Teams: A Platform to Understand Human-Computer Collaboration During the Design of Complex Engineered Systems

“…Kumar et al (2010) developed a tutor, Avis, to communicate relevant theoretical knowledge with designers through text conversations. More advanced agents can learn relevant design knowledge themselves and then share what they have learned with human designers (Hu and Taylor, 2016). Another research category consists of computational design agents that, through mimicking cognitive processes, can independently understand and explore given design spaces.…”