Session: AI Literacy, Attitudes, and Equity In Social Work and Allied Social Sciences Education: Collective Insights from Multi-Method, Multi-University Investigations (Society for Social Work and Research 31st Annual Conference)

As artificial intelligence (AI) becomes increasingly embedded in academic and professional settings, social work researchers face an urgent question: what do students in our field actually know about AI, how are they using it, and what do they think about it? Despite a growing body of AI literacy research, social work and allied social science students remain underrepresented in that literature, though their distinct disciplinary values, including commitments to social justice, equity, and relational practice, create a unique context for understanding how they engage with and make sense of AI. This symposium addresses that gap through a coordinated, multi-method investigation of AI use, knowledge, attitudes, and learning needs among social work and allied social science students across four U.S. universities.

The first study provides a descriptive portrait of AI use patterns, self-rated knowledge, attitudes, and learning needs among social science students (N = 396) across three U.S. universities. Students reported strong ethical awareness but substantially lower confidence in using AI effectively or ethically. Over 70% felt inadequately trained, with most learning through self-study rather than discipline-specific coursework.

The second study shifts the analytic focus to predictors of AI self-knowledge, using hierarchical multiple regression to identify self-directed learning and financial situation as the most meaningful predictors, while formal coursework and institutional exposure are not. The link between AI knowledge and financial resources raises equity concerns that are especially salient in a field committed to social justice.

The third study examines attitudinal profiles by classifying students into four distinct groups and using multinomial logistic regression to identify predictors of profile membership. AI use frequency emerged as the strongest predictor, suggesting that students’ orientations toward AI are shaped more through direct engagement than through formal education or institutional guidelines.

The fourth study provides qualitative depth. Focus groups conducted prior to the survey design phase explored how students conceptualize AI literacy, navigate voluntary and involuntary AI use, and experience the institutional climate in their programs, offering contextual grounding for the quantitative findings.

The fifth study examines how self-perceived technical and ethical AI knowledge shape students’ attitudes toward the ethical use of AI in educational and professional practice settings, using the Theory of Planned Behavior as a guiding framework (N = 97).

Across all five studies, several consistent themes emerge: students engage with AI primarily through informal, self-directed pathways rather than institutional guidance; ethical awareness does not translate into ethical competency; financial disparities in AI knowledge raise concerns about equitable preparation; and current educational structures may not be adequately preparing students for responsible and critical AI use. Methodologically, the symposium integrates qualitative and quantitative approaches across multiple universities and disciplines, offering a more comprehensive picture of students’ AI experiences. Substantively, the symposium moves beyond documenting that students use AI to examine why patterns differ and how institutional and social factors shape what students know and how they feel about it. Taken together, these findings offer an empirical basis for developing structured, discipline-specific AI learning opportunities that reflect students’ real experiences and professional values.