Academic Integrity in the Age of AI: Educator and Student Opinions on Plagiarism Detection Tools

Abstract

This study examines educators' and students' perceptions of AI-powered and traditional plagiarism detection tools in Nigerian universities, focusing on their effectiveness in identifying AI-generated content. Using a descriptive survey design, data was collected using a questionnaire from 120 lecturers and 450 students across six federal universities. The instrument was validated and a reliability test was conducted yielding a reliability coefficient of 0.84. the data collected were analysed using descriptive statistics of mean and standard deviation and inferential statistics of independent t-test. The results revealed significant differences in confidence levels, with lecturers demonstrating stronger belief in both AI-powered and traditional detection tools compared to students. The findings highlight a critical perception gap, where educators' institutional experience fosters trust in these systems, while students exhibit skepticism due to concerns about accuracy and fairness. The study concludes that overreliance on detection technologies without addressing stakeholder perception gaps may undermine academic integrity efforts. Recommendations include among others include adopting multi-layered assessment strategies combining AI detection with human evaluation, fostering dialogue and writing skill development is advised, while developers should improve algorithmic transparency.