Bridging classrooms and technology: Supporting teaching practice with an LLM-powered ICALL system
Year: 2024
Type: conference-paper
Abstract: Recent years have witnessed the rapid advancement of Artificial Intelligence (AI) technologies, holding immense potential for revolutionising learning and teaching in the realm of Intelligent Computer-Assisted Language Learning (ICALL). However, most ICALL systems tend to prioritize learners’ perspectives over those of teachers, whose involvement is crucial for integrating these systems in real-life school contexts. This oversight often results in teachers' reluctance to adopt and introduce these systems in daily teaching, thereby limiting their potential use in schools and reducing the ecological validity of research findings in actual language learning environments. This article aims to illustrate the ICALL system “ARES” to support teachers in their daily teaching practice with the power of a Large Language Model (LLM) with cooperating teachers’ knowledge, enabling full integration into real-life school education. Aiming to foster the teachers’ use of ICALL systems in real-life English as a Foreign/Second Language (EFL/ESL) settings, the design and development of the system have employed a cooperative approach with educators to optimize its responsiveness to user needs. In this article, we specifically present features designed to support teachers in their daily teaching practice and outline further evaluation of the system via questionnaires and log data.
Source: EUROCALL Conference
Authors: Mihwa Lee, Björn Rudzewitz, Xiaobin Chen
Institution: University of Tübingen
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: bronze
OpenAlex ID: W4405576897