Enhancing the Professional Development of Engineering Students through an AI-Based Collaborative Feedback System

• Published in: IEEE Global Engineering Education Conference pp. 1 - 9
• Main Authors: Becerra, Alvaro, Cobos, Ruth
• Format: Conference Proceeding
• Language: English
• Published: IEEE 22.04.2025
• Subjects: Collaboration; Dashboard; Engineering students; Feedback; GenAI; Generative AI; Generative Artificial Intelligence; Learning (artificial intelligence); Learning Analytics; Oral Presentation; Peer Assessment; Skill Development; Visualization
• ISSN: 2165-9567
• DOI: 10.1109/EDUCON62633.2025.11016499

Peer and self-assessment are widely recognized as effective strategies for fostering critical thinking, reflective learning, and the development of professional skills in educational contexts. These approaches enable students to actively participate in the learning process by evaluating their own work and that of their peers. Despite their potential benefits, traditional assessment methods often encounter significant challenges, such as inconsistent feedback quality, limited student engagement, and a lack of actionable insights that students can use to improve their performance. Addressing these limitations, this paper introduces AICoFe ("Artificial Intelligence-based Collaborative Feedback system"), an innovative platform designed to enhance the feedback process through the integration of generative artificial intelligence (GenAI) and Learning Analytics dashboards. AICoFe facilitates both peer and self-assessments using rubric-based frameworks that combine quantitative scores with qualitative observations. By leveraging GenAI, through an adapted version of GePeTo, the system provides personalized, actionable feedback tailored to individual student performance. This feedback is displayed on Learning Analytics dashboards, which also allow students to compare their performance against peers and reflect on their results through comparative graphs. Additionally, AICoFe includes video recordings of student performances to promote self-reflection and a deeper understanding of strengths and areas for improvement. These functionalities enable students to engage more effectively with the provided feedback, fostering continuous learning and development. The system's effectiveness was evaluated through a case study involving final-year engineering students tasked with improving their oral presentation skills. A customized rubric was designed to assess various aspects of effective presentations. Preliminary findings demonstrated that the feedback provided by AICoFe was perceived as clear, specific, and actionable. The study underscores the transformative potential of combining AI-driven feedback with dynamic visualization tools to create a holistic and engaging assessment process. Future work will explore additional features, such as advanced video and audio analysis, and expand the system's application to other skill areas, solidifying its role as a versatile tool for modern educational needs.