Summary
As data and AI systems become more common, it's important that computer science students learn about their social impact and ethics. The authors designed a teaching framework called HELIX that helps students build knowledge, make decisions, and feel empowered about human-centered data science. They tested this framework in a graduate programming class with 22 students, using readings and activities to encourage reflection on ethical issues. The framework was practical to use and showed promise in helping students think more deeply about the human side of data science. The authors also shared teaching materials and advice to help others add similar content without overwhelming students.
Human-centered data scienceEthics in computingProgramming educationAlgorithmic decision-makingGraduate computer science coursesEducational frameworksData science teachingSocial impact of AICurriculum developmentStudent self-reflection
Authors
Victoria Chui, Kelly McConvey, Daniel Chui, Malayna Bernstein, Shion Guha
Abstract
As AI and data-driven systems pervade practice, there is an imperative for instructors to embed societal impact and ethics content into computing courses. In response, we present the Human-Centered Education for Learning in Information and eXplainable Computing (HELIX) framework for information science programs, organized around three iterative pillars - knowledge building, decision-making, and empowerment - with concrete actions for instructors and students. We applied the framework in a graduate, introductory programming course using readings, algorithmic design activities, and scenario-based reflections. We present a pilot implementation of this framework to examine changes in students' (n=22) knowledge acquisition, decision-making processes, and self-reflection regarding human-centered perspectives in data science. We release an anonymized materials kit (survey, assignments, analysis code) to support adoption. We discuss design tensions (workload, assessment, relevance to diverse information science learners) and provide guidelines for integrating human-centered content without overwhelming technical outcomes. Findings suggest that the HELIX Framework is feasible in information science contexts and future work should use comparative survey assessment to strengthen causal inferences.