2025 ASEE Annual Conference & Exposition

This abstract for a full paper addresses the integration of artificial intelligence (AI) topics into introductory engineering courses. With the proliferation of AI into everyday life, it is important to introduce the topic early in the engineering curriculum. This paper will focus on generative AI and machine learning topics using two different educational strategies. The objective of this research is to explore students’ comprehension of AI and their motivation to engage in AI learning after being introduced to AI tools.

In a first-semester project engineering course, generative AI was introduced as a tool. Students were guided on the ethical and effective use of generative AI and discussed its limitations. The students were given the option to use generative AI for their writing assignments. A survey instrument was used to assess their previous knowledge and use of generative AI, as well as their understanding of its limitations. A survey was also administered to the students after the writing assignments to evaluate changes in perception, use, and knowledge of generative AI technology.

In a separate introductory multidisciplinary engineering Grand Challenges class, students were presented with machine learning algorithms before starting a team project. The learning module introduced AI algorithms in relation to the engineering Grand Challenges specifically in the healthcare field. The module introduced the students to fuzzy logic and neural networks using MATLAB, emphasizing the ethical considerations and appropriate use of AI as a supportive tool rather than a standalone solution. The exercises used simplified examples with limited inputs, allowing students to grasp the complexities involved in applying machine learning models to solve real-world problems. For the project, the students worked in teams on innovative future solutions to Grand Challenges problems. The students were given the opportunity to utilize machine learning techniques in their projects. Data was collected through a survey instrument administered before the machine learning module and after the completion of the project. The surveys gauged students’ knowledge of AI concepts, understanding of its limitations, and perceptions of how AI tools could address Grand Challenges. This paper discusses the results of the surveys from both courses and focuses on the students’ perceived learning, their understanding of AI, and their interest in pursuing AI topics in the future.