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The rapid rise of artificial intelligence (AI) in academic settings has generated both enthusiasm and ethical concerns among students, educators, and researchers. Scholars have emphasized pressing issues, including algorithmic bias, lack of transparency, data privacy risks, academic misconduct, and broader social and policy related implications. Despite increasing attention to AI in academic settings, a comprehensive synthesis of the literature on ethical considerations among students in the United States (U.S.) remains lacking. This scoping review aims to examine available empirical literature on AI ethical considerations among students in the U.S. educational settings, characterize patterns of AI use, identify research gaps, and explore actionable implications for higher education practice and policy.
Methods
This scoping review was conducted in accordance with Joanna Briggs Institute’s six-stage framework, including research question formulation, study identification, selection, data charting, synthesis and reporting, and expert consultation. A systematic search of five electronic databases, including ERIC, PsycINFO, Education Source, PROQUEST, and Scopus, was conducted using a comprehensive search string developed in consultation with a research librarian. Studies were eligible for inclusion if they were peer-reviewed empirical articles published in English, conducted in U.S. education settings, and examined AI ethical considerations. Following independent title and abstract screening and full-text review conducted by two investigators, fourteen peer-reviewed empirical studies met inclusion criteria and were selected for final review. Data were systematically extracted across key domains including study characteristics, participant demographics, types of AI use, and ethical considerations. Data selection and analysis were conducted using Covidence.
Results
Findings revealed that students notably used AI to generate ideas, organize tasks, and for academic assignments like writing papers. Ethical concerns most frequently identified included bias, data privacy, academic misconduct, lack of institutional transparency, and equity disparities in AI access and use. Studies consistently found that students with higher levels of digital literacy and institutional support were more likely to engage with AI tools effectively and ethically. Barriers to ethical AI use included insufficient institutional guidelines, limited faculty training, and students' uncertainty about appropriate boundaries for AI use in academic work. Undergraduate students and those from underrepresented backgrounds were disproportionately affected by these barriers. Facilitators included clear course-level communication, structured educational interventions, and institutional policies that explicitly addressed ethical AI use.
Conclusions
These findings have important implications for social work education, research, and policy. In practice, social work programs should integrate structured training on ethical AI use, emphasizing issues such as bias, data privacy, equity, and academic integrity, to ensure students develop digital competencies while upholding human-centered and culturally responsive approaches. Institutions can facilitate ethical engagement by providing clear guidelines, faculty development, and course-level communication that clarify appropriate AI use in social work contexts. For research, there is a need to examine how AI tools impact social work students’ decision-making, ethical reasoning, and client-centered practice, particularly among diverse populations. Policy implications include the development of institutional standards and frameworks that promote equitable access to AI technologies, safeguard ethical use, and support responsible integration of AI into social work education and professional practice.