Problem
Research questions and friction points this paper is trying to address.
empathy
software engineering education
artificial intelligence
AI-enabled systems
societal impact
Teaching Empathy in Software Engineering Education in the Age of Artificial Intelligence
🤖 AI Summary
This study addresses the lack of systematic guidance in software engineering education during the current AI era, which hinders the effective cultivation of students’ empathy toward users, stakeholders, and the societal impacts of AI systems. Through qualitative methods, the authors conducted inductive coding and thematic analysis of educators’ practices to propose five actionable pedagogical strategies: situating AI systems within their social contexts, integrating fairness and accessibility into design evaluations, foregrounding diverse user perspectives, reinforcing stakeholder accountability, and embedding structured reflection mechanisms. The findings demonstrate that empathy can be seamlessly integrated into core software engineering curricula, significantly enhancing students’ critical awareness of AI bias, accessibility, accountability, and broader societal consequences.
📝 Abstract
Empathy has been discussed as a relevant human capability in software engineering, particularly in activities that require understanding users, stakeholders, and the societal implications of technological systems. This relevance becomes more pronounced in the context of artificial intelligence, where software increasingly participates in decisions that affect diverse individuals and communities. However, limited guidance exists on how empathy can be integrated into technical software engineering education in ways that connect with the development of AI-enabled systems. This study investigates teaching practices that educators use to incorporate empathy into software engineering courses. Using qualitative analysis of educator-reported practices, we identified five categories through which empathy is operationalized within technical coursework: societal framing of AI systems, fairness and accessibility considerations in design and evaluation, representation of diverse users, stakeholder role awareness and responsibility, and structured reflection and feedback during development processes. The findings indicate that empathy can be embedded within core development activities rather than taught as a separate topic, enabling students to reason about bias, accessibility, accountability, and the societal consequences of AI technologies. These results contribute a structured view of how empathy-oriented practices can be incorporated into software engineering education to support the preparation of students who will develop AI-enabled systems.
Innovation
Methods, ideas, or system contributions that make the work stand out.
empathy
software engineering education
AI ethics
inclusive design
societal impact
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