A Quantitative Investigation of Graduate Student Perceptions of Human-Generated and AI-Generated Reviews in a Cyber-Social Learning Platform

This quantitative research paper examines graduate student perceptions regarding reviews provided by both human peers and AI-generated algorithms within a cyber-social learning platform. The study employs a structured survey instrument distributed to a sample of 41 graduate students enrolled in diverse academic programs. The primary objectives include assessing students’ attitudes toward reviews generated by human peers and AI algorithms, exploring the perceived effectiveness, credibility, and trustworthiness of both types of reviews, and identifying potential factors influencing student preferences. Findings showed a nuanced set of perceptions. The hypothesis that learners would have more positive perceptions of human-generated AI was confirmed. However, it was shown that learners preferred receiving both human- and AI-generated reviews in the future. Findings on the mediating effects of individual demographics were mixed with some characteristics demonstrating stronger mediating influence. The hypothesis that learners would have a positive perception of the learning platform was confirmed. The paper concludes with a discussion of the philosophical and practical implications of these findings.