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Improving English Semantic Learning Outcomes through AI Chatbot-Based ARCS Approach

Title: Improving English Semantic Learning Outcomes through AI Chatbot-Based ARCS Approach
Language: English
Authors: Mei-Rong Alice Chen (ORCID 0000-0003-2722-0401)
Source: Interactive Learning Environments. 2025 33(6):3909-3924.
Availability: Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
Peer Reviewed: Y
Page Count: 16
Publication Date: 2025
Document Type: Journal Articles; Reports - Research
Education Level: Higher Education; Postsecondary Education
Descriptors: Semantics; English (Second Language); Second Language Learning; Synchronous Communication; Artificial Intelligence; Majors (Students); Undergraduate Students; Models; Academic Achievement; Teaching Methods; Anxiety; Student Motivation; Educational Technology; Learning Management Systems; Private Colleges; Foreign Countries; Second Language Instruction; Self Efficacy
DOI: 10.1080/10494820.2025.2454443
ISSN: 1049-4820; 1744-5191
Abstract: Comprehending abstract ideas in English semantics courses challenges English as a Foreign Language (EFL) learners. Teacher-centered methods fail to engage learners, decreasing motivation and contextual understanding. While the ARCS (Attention, Relevance, Confidence, Satisfaction) model emphasizes motivation, the conventional ARCS (C-ARCS) approach lacks interactivity. To address these limitations, this study proposed an AI chatbot-based ARCS (AIC-ARCS) approach to enhance interaction in English semantics learning. The AIC-ARCS approach integrates annotation and learner explanations of semantic concepts to an AI chatbot. A pre-test-post-test quasi-experimental design evaluated this approach. Fifty English-major undergraduates participated: twenty-five students used the AIC-ARCS approach, while twenty-five employed C-ARCS with peer discussions. Results showed the AIC-ARCS approach significantly improved semantic achievement and self-efficacy while reducing learning anxiety compared to C-ARCS. These findings suggest AI chatbots in ARCS-based frameworks create more engaging learning environments. This study highlights the potential of AI chatbot-enhanced pedagogies in transforming EFL instruction, particularly in English semantics. Future research should explore broader applications of AI chatbots and their long-term impact on learning.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1501717
Database: ERIC