Reframing AI Literacy through an Ethics- and Justice-Focused Lens: A Qualitative Analysis of Social Science Students' Experiences and Perspectives

Background and Purpose: As artificial intelligence (AI) increasingly shapes education and professional practice, AI literacy is gaining recognition as a necessary competency across disciplines. However, dominant discourses on AI literacy emphasize technical skills and computational thinking, overlooking the ethical, emotional, and value-based dimensions that are especially salient in human-centered fields (Long & Magerko, 2020; Raji et al., 2021). While recent studies report mixed levels of AI literacy and ambivalent attitudes among students (Laupichler et al., 2023), few have explored how students in disciplines such as social work, psychology, and education conceptualize and relate to AI. This represents a critical gap, as students in these fields will be among the frontline professionals navigating AI's societal impacts, often with limited training or guidance on its ethical and relational implications. Accordingly, this study explores how social science students understand, use, and emotionally and ethically engage with AI technologies.

Methods: We conducted four semi-structured focus groups with a total of 24 undergraduate and graduate students from social science disciplines at a large public university in the U.S. South. Using thematic analysis, we examined how students define AI, perceive its relevance, describe their experiences with AI tools, and negotiate ethical and professional boundaries around its use.

Results: Students' knowledge about AI varied significantly, from basic understandings of AI as automation to nuanced descriptions of machine-learning models replicating human cognition. While acknowledging AI’s practical benefits (e.g., text summarization, lesson preparation), many participants expressed emotional ambivalence, anxiety, and ethical uncertainty around its use. Graduate students highlighted particular unease regarding AI's rapid, compulsory integration into curricula without sufficient critical discussion. They raised concerns about AI perpetuating biases, exploitative industry practices, confidentiality breaches, potential job displacement, and environmental harms due to resource-intensive operations. These concerns were shaped by their disciplinary values and professional identities rooted in care, social responsibility, and justice. Participants called for structured opportunities to critically reflect on the social implications of AI, as well as clear, discipline-specific guidance for responsible use.

Conclusions and Implications: Findings suggest that AI literacy education in the social sciences must extend beyond technical skill-building to include guidance on ethical use and social justice. Participants themselves recognized the importance of ethics- and justice-oriented approaches to AI and highlighted a lack of such opportunities in their education, which illuminates the need for a more intentional, values-driven curriculum in social science disciplines. Drawing on critical AI studies and pedagogical frameworks focused on ethics and justice (e.g., Garrett et al., 2020; Raji et al., 2021), we suggest that AI literacy efforts should center student reflection and dialogue around the ethical challenges, professional responsibilities, and socioenvironmental implications that arise when using AI in contexts involving human care, decision-making, and equity. Institutions must also invest in faculty development to equip educators with the tools to teach AI through socially grounded, discipline-specific frameworks. Ultimately, integrating AI into social science curricula requires active, inquiry-based approaches that align with the relational and justice-driven values of these fields.