A novel approach to automated essay scoring using multi-agent systems. By leveraging debate between specialized agents, the framework provides a more reliable and nuanced evaluation, surpassing traditional single-agent methods and bringing scoring closer to human-level agreement.
Implemented Theory of Mind assessment for individuals with Autism by developing a LLaMA-3 chatbot to administer the Faux Pas Recognition Test. The system utilizes a multi-phase pipeline that combines interactive rehabilitation dialogues with adaptive hints, an automated scoring phase using an LLM-as-judge, and generates clinician-ready reports to enhance social cognition and improve faux pas detection.
Automatic metadata tagging of educational content to enhance Intelligent Tutoring Systems (ITS). Leveraging NLP, machine learning, and domain-specific rules, it efficiently extracts both general and pedagogical metadata, improving personalized learning recommendations.
AI-powered digital learning framework designed for scalable, personalized, and cost-effective education. Framework integrates AI-based learner modeling, a personalized recommender system, and AI-assisted support for both learners and facilitators, aiming to address challenges in educational access, soft skills development, and large-scale implementation.
Explores the impact of collaborative learning (CL) on the learning environment and academic achievement in online reflective platforms (ORPs). The mixed-methods approach reveals that CL enhances student engagement, promotes a supportive learning atmosphere, and improves academic performance, with positive reflections from students on the collaborative and interactive nature of the online learning experience.
Identifies challenges in implementing cooperative learning (CL) in Iran's primary schools, including traditional teaching methods, stakeholder readiness, and lack of facilities. Solutions include improving curriculum, fostering positive attitudes, and enhancing teacher training for effective CL implementation.