As Artificial Intelligence (AI) advancements create new ethical issues and challenge existing legal regulations, traditional ethical principles and data ethics education often fail to reflect these nuances. This study explores how collaborative game design among students with diverse backgrounds fosters transformative learning in data ethics within a college course. It examines a conversation about motivations for enrolling in the course, pre‐course reflections, and post‐course reflections from 32 participants in a semester‐long credit‐based class. Thematic analysis reveals parallels with Mezirow's transformative learning theory (1978) in four key phases of transformative learning. The preliminary findings demonstrate that students not only gained data ethics literacy but also internalized ethical thinking as part of their future goals. This poster highlights the potential of interdisciplinary and collaborative design‐based pedagogy to cultivate digital ethical self‐awareness and ethical judgements when navigating ethical issues in AI or machine learning (ML).
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Transforming Perspectives on Data Ethics through Collaborative Game Design
Semantic Scholar · 2025
Abstract
As Artificial Intelligence (AI) advancements create new ethical issues and challenge existing legal regulations, traditional ethical principles and data ethics education often fail to reflect these nuances. This study explores how collaborative game design among students with diverse backgrounds fosters transformative learning in data ethics within a college course. It examines a conversation about motivations for enrolling in the course, pre‐course reflections, and post‐course reflections from 32 participants in a semester‐long credit‐based class. Thematic analysis reveals parallels with Mezirow's transformative learning theory (1978) in four key phases of transformative learning. The preliminary findings demonstrate that students not only gained data ethics literacy but also internalized ethical thinking as part of their future goals. This poster highlights the potential of interdisciplinary and collaborative design‐based pedagogy to cultivate digital ethical self‐awareness and ethical judgements when navigating ethical issues in AI or machine learning (ML).