Autonomy Versus Algorithm: A Replication Study of Student Perspectives

This book examines how undergraduate students interpret ethical boundaries around generative AI use in higher education. Using a mixed methods replication design, the study surveys 200 undergraduates across multiple disciplines in three Nigerian universities to measure agreement with five AI ethics dimensions: beneficence, non maleficence, justice, autonomy, and explicability. Results show autonomy as the strongest priority, while explicability ranks lowest, suggesting students care more about preserving independent decision making than understanding how AI systems produce outputs. Regression analyses indicate technology proficiency predicts stronger endorsement of beneficence and autonomy, while gender predicts non maleficence and justice. Qualitative findings highlight practical concerns about misinformation, erosion of creativity and skill, privacy, bias, job displacement, and unequal access shaped by infrastructure and data costs, alongside views that responsibility lies primarily with users. The book concludes with implications for AI ethics guidance, awareness programs, and curriculum integration tailored to the Nigerian higher education context.