ELTC Framework: Revolutionizing MUET Learning by Crafting a Dynamic Chatbot Learning Experience

Abstract

Intelligent Tutoring Systems (ITSs) are designed to aid and enhance the teaching and learning process in a specific field of knowledge while acknowledging the uniqueness of each learner. Chatbots are becoming increasingly prevalent in contemporary life, with applications ranging from personal assistants on mobile devices to providing technical assistance over the phone lines in various sectors, including business, retail, health, and education. Unlike other fields, the application of chatbots in education requires careful pedagogical considerations. This article outlines a theoretical framework and theories used in developing a chatbot for the Malaysian University English Test (MUET) learning and testing, incorporating the ELTC (Education, Learning, Teaching, and Communication) Framework. The four underpinning theories of the theoretical framework in this study are education theory, language theory, technology theory, and communicative theory. The education theory adopted in this study is Lev Vygotsky's Social Constructivist Theory, aligning with the ELTC Framework's focus on active learning and interaction. The language theory involved is Krashen's Second Language Acquisition. Meanwhile, the technology theory includes Bower's Technology-Mediated Learning Theory and George Siemens's Connectivism Theory, resonating with the ELTC Framework's emphasis on utilizing technology for effective learning. The communicative theory, which highlights Social Presence Theory, underscores the importance of meaningful interaction in language learning. The suggested theoretical framework may be utilized as a reference for developing chatbot intelligent teaching systems. The chatbot can be expanded to other courses in future research. The assessment of the chatbot could be conducted with different participants and customized to meet the requirements of various learners with varying levels of language proficiency.