Abstract
This study compares the effectiveness of two leading AI platforms, Google Gemini and Co-Pilot, in providing feedback on written assignments. As AI tools become integral in education, understanding their strengths and limitations is essential. Grounded in Constructivist Learning Theory, Cognitive Load Theory, and Feedback Intervention Theory, this research evaluates the feedback mechanisms of each AI system. The study also examines the impact of prompt engineering on feedback quality. Using a mixed-methods approach, combining quantitative analysis and qualitative insights from user experiences, the research identifies distinct characteristics of each platform. The findings reveal varying degrees of alignment with educational principles, highlighting the critical role of prompt design in optimizing AI-generated feedback. This study provides valuable insights for educators, developers, and policymakers on utilizing AI technologies to enhance student learning outcomes.
Presenters
Irum NazAssistant Professor, College of General Education, Communications, University of Doha for Science and Technology, Ad Dawhah, Qatar
Details
Presentation Type
Paper Presentation in a Themed Session
Theme
KEYWORDS
AI IN EDUCATION, GOOGLE GEMINI, CO-PILOT, FEEDBACK MECHANISMS, CONSTRUCTIVIST LEARNING
