Embedded Human-Centered Data Science in a Graduate Programming Course: A Framework and Case Study

2026-09-08

AI Summary

为解决AI教育中缺乏社会影响和伦理内容的问题，通过HELIX框架在研究生编程课程中引入人类中心视角，采用阅读、算法设计及情景反思等方法。

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.

Problem

• AI
• data-driven systems
• societal impact
• ethics
• information science programs

Innovation

• Human-Centered Data Science
• Ethics in Computing Courses
• HELIX Framework