Date
Publisher
arXiv
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.
Study at a glance
- Question: How do graduate students' knowledge, attitudes, and ethical decision-making change after completing an embedded human-centered programming curriculum?
- Setting and data: Introductory graduate programming course in an information science department, 22 participating master's students.
- Method: Pre- and post-course survey analysis (Wilcoxon signed-rank tests) and brief qualitative coding of open-ended reflections.
Why it matters
As AI and data-driven systems pervade society, instructors need to embed ethics and human-centered perspectives into computing courses without overloading technical learning goals.
Key findings
- Overall survey scores improved significantly from pre-course to post-course (mean 72.3 to 75.8, p = 0.001), driven primarily by positive shifts in attitudes and perceptions toward ethical responsibility (p = 0.002).
- Measured knowledge and scenario-based ethical decision-making scores showed minimal change from pre- to post-course.
- Twenty-one of 22 students reported somewhat or significantly improved understanding of ethical data science, frequently anticipating workplace challenges around data privacy, general ethics, and algorithmic bias.
Summary drafted with AI from the full paper and may contain errors. See the full paper for details.
What is the application?
Who is the user?
Who age?
Why use AI?
Study design
