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Abstract As generative artificial intelligence (AI) becomes increasingly integrated into society and education, more institutions are implementing AI usage policies and offering introductory AI courses. These courses, however, should not replicate the technical focus typically found in introductory computer science (CS) courses like CS1 and CS2. In this paper, we use an adjustable, interdisciplinary socio‐technical AI literacy framework to design and present an introductory AI literacy course. We present a refined version of this framework informed by the teaching of a 1‐credit general education AI literacy course (primarily for freshmen and first‐year students from various majors), a 3‐credit course for CS majors at all levels, and a summer camp for high school students. Drawing from these teaching experiences and the evolving research landscape, we propose an introductory AI literacy course design framework structured around four cross‐cutting pillars. These pillars encompass (1) understanding the scope and technical dimensions of AI technologies, (2) learning how to interact with (generative) AI technologies, (3) applying principles of critical, ethical, and responsible AI usage, and (4) analyzing implications of AI on society. We posit that achieving AI literacy is essential for all students, those pursuing AI‐related careers, and those following other educational or professional paths. This introductory course, positioned at the beginning of a program, creates a foundation for ongoing and advanced AI education. The course design approach is presented as a series of modules and subtopics under each pillar. We emphasize the importance of thoughtful instructional design, including pedagogy, expected learning outcomes, and assessment strategies. This approach not only integrates social and technical learning but also democratizes AI education across diverse student populations and equips all learners with the socio‐technical, multidisciplinary perspectives necessary to navigate and shape the ethical future of AI.
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Responsible AI literacy: A stakeholder-first approach
The need for citizens to better understand the ethical and social challenges of algorithmic systems has led to a rapid proliferation of AI literacy initiatives. After reviewing the literature on AI literacy projects, we found that most educational practices in this area are based on teaching programming fundamentals, primarily to K-12 students. This leaves out citizens and those who are primarily interested in understanding the implications of automated decision- making systems, rather than in learning to code. To address these gaps, this article explores the methodological contributions of responsible AI education practices that focus first on stakeholders when designing learning experiences for different audiences and contexts. The article examines the weaknesses identified in current AI literacy projects, explains the stakeholder-first approach, and analyzes several responsible AI education case studies, to illustrate how such an approach can help overcome the aforementioned limitations. The results suggest that the stakeholder-first approach allows to address audiences beyond the usual ones in the field of AI literacy, and to incorporate new content and methodologies depending on the needs of the respective audiences, thus opening new avenues for teaching and research in the field.
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- PAR ID:
- 10514463
- Publisher / Repository:
- Big Data & Society
- Date Published:
- Journal Name:
- Big Data & Society
- Volume:
- 10
- Issue:
- 2
- ISSN:
- 2053-9517
- Subject(s) / Keyword(s):
- responsible AI AI education ethical AI AI literacy AI fairness AI accountability
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1177/20539517231219958
