EduPal leaves no professor behind: Supporting faculty via a peer-powered recommender system
Nourhan Sakr, Aya Salama, Nadeen Tameesh, Gihan Osman
TL;DR
EduPal addresses the gap in scalable, personalized pedagogical support for faculty post-COVID by combining crowd-sourced knowledge with a knowledge-based recommender system delivered through a chatbot. The two-stage pipeline uses user-based collaborative filtering and a symbolic expert system to propose context-aware pedagogical recommendations drawn from a curated knowledge bank, refined by expert input and user feedback. The approach is grounded in a data-driven taxonomy built from interviews, surveys, and expert insights, and is demonstrated through a local STEM prototype at the American University in Cairo with favorable usability feedback. Results indicate EduPal can automate knowledge distillation, enhance peer-to-peer guidance, and scale to underresourced settings, with additional potential as a screening tool for instructional design consultations.
Abstract
The swift transitions in higher education after the COVID-19 outbreak identified a gap in the pedagogical support available to faculty. We propose a smart, knowledge-based chatbot that addresses issues of knowledge distillation and provides faculty with personalized recommendations. Our collaborative system crowdsources useful pedagogical practices and continuously filters recommendations based on theory and user feedback, thus enhancing the experiences of subsequent peers. We build a prototype for our local STEM faculty as a proof concept and receive favorable feedback that encourages us to extend our development and outreach, especially to underresourced faculty.