A Conceptual Framework Highlighting Design Factors for Emotion-Aware Expert Systems in Education

Abstract

In today's classrooms, teachers are unable to give individual attention to each student, which delays timely feedback and personalized support to students. AI-based educational tools, such as Chatbots, are utilized by teachers to assist them in various routine tasks, including answering repeated student queries, preparing notes, setting test questions, and conducting quizzes. Research has shown that student emotions play a vital role in their learning. However, these tools do not consider the feelings of students in any aspect and hence, an adaptive learning environment is not available to them. To address this gap, this study proposes a conceptual framework for an emotion-aware expert system. The framework integrates a chatbot, a sentiment analysis engine and a teacher dashboard to enhance the teachers' pedagogy and the students' learning experience. Initially, a systematic literature review was conducted to identify the critical design factors required for developing a chatbot. A survey was conducted among the students and teachers to understand their perception of the design factors of an emotion-aware chatbot. The consolidated factors are used to develop the conceptual framework of the system. This framework understands learners' emotions, enhances student engagement and supports teachers in making better pedagogical decisions.