A Justice Lens on Fairness and Ethics Courses in Computing Education: LLM-Assisted Multi-Perspective and Thematic Evaluation
We are proud to share one of the outputs of the Decolonization of STEM Curriculum working group. A new paper published on arXiv titled “A Justice Lens on Fairness and Ethics Courses in Computing Education: LLM-Assisted Multi-Perspective and Thematic Evaluation.”
This work is the result of a collaborative effort by Kenya S. Andrews (Brown University), Deborah Dormah Kanubala (Saarland University), Kehinde Aruleba (University of Leicester), Francisco Enrique Vicente Castro (New York University), and Renata A. Revelo (University of Illinois).
The paper examines how fairness, ethics, and justice are represented in AI and computing course syllabi across U.S. universities, with a particular focus on how these concepts are framed, prioritized, and operationalized in formal educational documents. It introduces a justice-oriented evaluation rubric designed to go beyond surface-level mentions of ethics and assess whether courses meaningfully engage with issues such as historical inequities, harm, accessibility, representation, and accountability. To enable systematic and scalable analysis, the study combines this rubric with large language models that simulate multiple evaluator perspectives—including instructors, departmental leadership, institutional reviewers, and external evaluators. This multi-perspective approach reveals both visible practices and hidden gaps in current curricula, highlighting how justice-oriented elements may be interpreted differently depending on institutional role and context. The findings offer concrete insights for educators and institutions seeking to design AI and computing courses that are not only technically rigorous, but also socially grounded, inclusive, and responsive to real-world impacts.