AICoFe System Classroom Peer Assessment Setup

Paper context

Paper title: Leveraging Peer, Self, and Teacher Assessments for Generative AI-Enhanced Feedback Abstract: Providing timely and meaningful feedback remains a persistent challenge in higher education, especially in large courses where teachers must balance formative depth with scalability. Recent advances in Generative Artificial Intelligence (GenAI) offer new opportunities to support feedback processes while maintaining human oversight. This paper presents an study conducted within the AICoFe (AI-based Collaborative Feedback) system, which integrates teacher, peer, and self-assessments of engineering students' oral presentations. Using a validated rubric, 46 evaluation sets were analyzed to examine agreement, correlation, and bias across evaluators. The analyses revealed consistent overall alignment among sources but also systematic variations in scoring behavior, reflecting distinct evaluative perspectives. These findings informed the proposal of an enhanced GenAI model within AICoFe system, designed to integrate human assessments through weighted input aggregation, bias detection, and context-aware feedback generation. The study contributes empirical evidence and design principles for developing GenAI-based feedback systems that combine data-based efficiency with pedagogical validity and transparency. Passages referencing this figure: As illustrated in Figure 1 , students had two roles: evaluator (two peers per presentation) and presenter. Figure 1: Classroom setup of the peer assessment activity using the AICoFe system III-B Assessment Instrument (Rubric) The presentations were assessed using a rubric implemented in the AICoFe system.