2026 ASEE Annual Conference & Exposition

Artificial intelligence (AI) is an emerging pedagogical tool in engineering education that is being utilized to facilitate student understanding of core class concepts while simultaneously preparing them for AI-integrated work tasks after graduation. While AI can be a powerful resource, it can also hinder active student learning by generating answers for students instead of asking students to generate and integrate course concepts themselves. In this work-in-progress research, Bloom’s taxonomy was mapped onto a decision matrix to help professors determine how to best integrate AI into their various class activities to improve student learning without undermining foundational understanding of core concepts. Professors from two universities created and used a decision matrix to generate multiple assignments in six different courses. Classes were all Industrial & Systems Engineering but ranged in topics and included such content as facilities design, human factors, statistics and forecasting, work design, project management, and operations research. For each assignment, a list of questions from the decision matrix were mapped onto a Bloom’s Taxonomy level and the outcomes from faculty observations were provided. Analysis of those outcomes across all the courses yielded several common themes. Recommendations include designing assignments to foster independent core knowledge, AI-dependent knowledge (e.g., how to effectively use AI), and encouraging AI-based skills in ascertaining the quality of AI-generated information.