As generative artificial intelligence (AI) rapidly reshapes higher education, institutions face a critical ethical paradox: while adoption accelerates, the roles of responsibility, fairness, and moral judgment in AI integration remain insufficiently theorized and empirically examined. We conducted a study that examined how preservice teachers perceive and navigate the ethical dimensions of generative AI use in academic contexts, focusing on responsibility, bias, and acceptability. Using a cross-sectional survey design, data were collected from approximately 250 participants working on teaching certifications and masters degrees at a graduate school of education at a private university located in the Northeastern United States. The survey measured ethical responsibility, fairness and bias sensitivity, perceived usefulness, perceived ease of use, AI–TPACK, trust, social influence, and behavioral intention. Quantitative analyses included descriptive statistics, reliability testing, correlational analysis, and multiple regression. Open-ended responses were analyzed thematically to deepen interpretation of qualitative findings. Results revealed a critical disconnect. Although participants report strong ethical commitments regarding fairness, bias, and accountability, these factors do not significantly predict behavioral intention to use AI. Instead, perceived usefulness and trust emerge as the dominant predictors, accounting for the majority of variance in adoption. This finding highlights a gap between ethical reasoning and practical decision-making in AI integration. Additionally, participants prioritized ethics, privacy, and bias in professional development, indicating strong needs for advanced technical training. This study extends traditional technology acceptance frameworks and contributes to emerging scholarship on responsible AI in higher education by modeling ethical responsibility and fairness as independent constructs within AI adoption research. The findings offer important implications for institutional policy, teacher preparation, and the development of governance structures that align ethical commitments within AI practice in higher education.

Authors:
Ksenia Anisimova, Fordham University, United States
Su-Je Cho, Fordham University, United States
Shiva Rose Mohsenian, Fordham University, United States

About the Presenter(s)
Ksenia Anisimova, Ph.D. candidate in Curriculum & Instruction (Special Education) at Fordham University. Interested in literacy, behavior, and AI in education.

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