2026 ASEE Annual Conference & Exposition

The use of generative artificial intelligence (GAI) is becoming more prevalent amongst students. Employers are increasingly expecting graduates to be competent in the use of GAI at work. However, the level of competence and understanding of these tools can vary widely based on the experiences of a given student. Since GAI is now a publicly available tool, it is important that students receive training on the appropriate use, ethical implications, and limitations before using this tool in coursework. To inform these efforts, the authors developed an AI literacy framework designed for quantitative experimentation courses. This framework, presented at ASEE in 2025, guided the development of the instructional methods described in this paper.

In the fall of 2025, Mechanical Engineering and Library faculty piloted a series of learning modules focused on AI literacy in quantitative experimentation. These modules consisted of instructional materials, assignments, and assessments. The learning outcomes focused on teaching students about the uses, ethical implications, and limitations of AI as it applies to aspects of experimental instruction. For this project, learning modules were built to teach students about how to use GAI in laboratory classes for: i) literature review and ii) data post-processing and plotting. The modules included pre-work (short videos with knowledge checks), a teacher-led demonstration, followed by directed practice. Students were given an initial self-assessment. After the instruction, student artifacts were collected, including ChatGPT conversations, short reflective essays on the process of interacting with GAI, and final assignments. In addition, the faculty of the courses were interviewed to measure student engagement with AI following the discussion. The materials were developed with enough flexibility to be adapted for other laboratory courses across varying disciplines. This was demonstrated through the piloting of modules in two lower-division physics and eight upper-division engineering laboratory courses. The methodology and some initial qualitative findings are presented. The lessons learned from this work will inform the creation of instructional materials to support adoption in laboratory‑based courses across disciplines.