Publication Date

8-2025

Abstract

In this manuscript, we explore the intersection of artificial intelligence (AI) and equitable learning in higher education, focusing on data science as a subset of AI and social justice as the core theme of equity. Our investigation sheds light on the nuanced tensions inherent in employing data science for social justice. Rooted in situated perspectives of learning and consequential learning, our study employs an instrumental case-study methodology and analysis techniques from interaction and conversation analysis. Collaborating with three undergraduate students and an urban farm, the students used data science practices to highlight inequities surrounding food justice and access to food. Our findings reveal three key tensions: (1) the undergraduates' discourse on simplicity versus complexity in utilizing data science for social justice, (2) the challenges of balancing data science with social justice imperatives, and (3) the successful application of data science by the students in their food justice project, culminating in a presentation of their findings to the farm's director. We conclude by discussing implications for research and the use of data science in social justice projects

Document Type

Article

Keywords

Data science education, Artificial intelligence, Social justice, Consequential learning, Urban farming, Food justice, Higher education

Disciplines

Agricultural Education | Data Science | Food Security | Food Studies | Geographic Information Sciences | Higher Education | Science and Mathematics Education | Social Justice

Publisher

Springer Nature

DOI

https://doi.org/10.1007/s12528-025-09466-0

Source

Journal of Computing in Higher Education

Creative Commons License

Creative Commons Attribution-NonCommercial 4.0 International License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

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