Abstract
This study investigates students’ perceptions of feedback from Generative Artificial Intelligence (GenAI) and human peers in cyber-social learning environments. The research aims to understand the effectiveness of different feedback mechanisms in dynamic educational spaces where digital platforms and social interactions converge. This study contributes to the ongoing discourse on integrating digital tools in education, with implications for educational design and practice in the evolving cyber-social learning landscape. This research employs a mixed-methods approach using structured surveys containing quantitative rating scales and qualitative open-ended items. The study was conducted at an American university in Spring 2024, involving 86 participants from various degree programs within the College of Education. Findings reveal nuanced student perspectives on the strengths and weaknesses of both feedback types, with AI reviews perceived as higher quality and more useful, while actionability was comparable between AI and human reviews. The study’s limitations include its context-specific nature and the use of a particular learning platform. This research provides empirical evidence to inform the development of effective feedback strategies in cyber-social learning environments, suggesting that a combination of GenAI and human-generated feedback could offer the best of multiple qualitatively different types of intelligence.
Presenters
Christopher HughesAdjunct Instructor, General Studies, The University of the People, United States Akash Saini
Teaching Assistant, Education Policy, Organization and Leadership, University of Illinois at Urbana-Champaign, United States
Details
Presentation Type
Paper Presentation in a Themed Session
Theme
KEYWORDS
Generative AI, Feedback, Reviews, Cyber-Social Learning, Higher Education
