Generative artificial intelligence is a hotly debated issue in the current landscape of educational research, with educators’ abilities to utilise this powerful tool falling by the wayside as institutions focus instead on regulation. Current research on generative AI in engineering education, whilst in its infancy, places a large onus on studying students and how they use such services. Therefore, little is currently known regarding current and proposed uses of generative AI by engineering educators and academics. Its potential in enhancing educational methodologies often remains underexplored amidst regulatory concerns. This is especially true for the field of diversity, equity and inclusion where generative AI has been used in numerous ways to cultivate more equitable outcomes for engineering students. Our ongoing research aims to elucidate these current and proposed uses of AI to understand how it can be used to create equitable learning environments for undergraduate engineering students. This research aligns with CDIO Standards by investigating how generative AI can support active learning environments (CDIO Standard 8) and integrate diverse learning preferences into the engineering curriculum (CDIO Standard 7). Through a reflexive thematic analysis of six semi-structured interviews with academics from Monash University's Faculty of Engineering, the main themes of Adaptive Integration, Balancing Efficiency with Deep Learning and Empowering Through Training and Resource Allocation were discovered. Future research should centre around uncovering the mechanisms of algorithmic bias in the field of engineering, assessing the efficacy of generative AI powered pedagogical interventions in achieving equity, diversity and inclusion as well as the development of faculty scaffolded ethical guidelines and frameworks for the use of generative AI tools.
Affiliations:
Monash University, Melbourne, Australia
Keywords:
Equity
diversity
Inclusion
Artificial Intelligence
CDIO Standard 7
CDIO Standard 8
Year:
2024
Reference:
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