Guidelines for Generative AI in Engineering Design Teaching and Learning

• In Event: Ethics and Guidelines for AI-Assisted Assessment Across Professions

Abstract

Generative AI (GenAI) is increasingly prominent throughout all sectors of higher education, and engineering education is no exception. Like other disciplines, how GenAI can and should be used by engineering students can vary by many factors, including (1) the developmental level and preparedness of students, (2) the capacity of GenAI to provide applicable outputs to problems of varying complexity, and (3) the availability of strategies and scaffolding to guide students’ GenAI use in ways that advance (rather than inhibit) learning. Thus, providing students with coherent policies and a solid ethical foundation for GenAI use is thus necessary. However, given the relative nascency of GenAI, community-wide accepted guidelines and standards are still emerging.
This presentation will highlight prominent policies and guidelines for GenAI use as described by 17 engineering instructors who have moderate to high levels of expertise and experience leveraging GenAI in engineering design teaching and learning. Each instructor participated in an approximately 90-minute semi-structured interview, wherein they identified best practices for teaching engineering design, discussed SWOT (strengths, weaknesses, opportunities, and threats) elements associated with GenAI use in design teaching and learning, and identified guidelines or policies for leveraging GenAI in design teaching and learning in light of these considerations. Emergent findings highlight the need for context-specific guidelines that embrace both instructor and student autonomy and that are responsive to the ongoing developments in GenAI, GenAI use in higher education, and context-specific and community-vetted perspectives. By highlighting the views of instructors in a specific domain of engineering instruction (i.e., design), this talk will provide insights specific to this domain but that are transferrable to other disciplines where design or similar activities are prominent. Moreover, this study will provide curricular and institutional policy considerations for leveraging GenAI to advance student learning in ways that are conducive to foundational goals of the higher educational enterprise.

Author

• Justin L. Hess, Purdue University