Collaborative Research: Using Digital Narratives and Data Analytics to Enhance Ethical Judgment Education and Assessment

This project aims to serve the national interest by improving educational interventions and assessments of engineering students' ethical judgments. Ethical lapses in engineering practice can result in loss of life, property damage, infrastructure failures, and environmental harm. By better preparing students to make more ethical decisions, the project seeks to produce significant societal and economic benefits. This Level 2 Engaged Student Learning project integrates a game-based educational intervention that immerses students in realistic, narrative-driven scenarios requiring ethical decision-making and qualitative discussion. Additionally, the project leverages advanced machine learning and large-language model AI tools to assess student responses, creating a scalable and dynamic tool for ethics education and assessment. These innovations are expected to contribute to advances in teaching practices and the broader integration of ethical reasoning into engineering curricula. The project has two primary goals: to investigate the impact of contextualized information on student ethical judgments and to explore the affordances of large-language models (LLMs) and natural language processing (NLP) in assessing student responses. Using a game-based intervention, students engage with engineering-contextualized ethical dilemmas that provide varying contextual cues, hypothesized to promote more nuanced and situated ethical reasoning. The project team intends to code student narratives to identify themes and ethical reasoning complexity, using this data to train LLM/NLP models for categorization of responses. This approach represents a significant step toward scalable, data-driven assessment of ethical judgment. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the project supports the creation, exploration, and implementation of promising practices and tools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.