Artificial Intelligence as an Assisted-Language Learning Tool: A Systematic Review of Its Efficacy in Enhancing Speaking Skills Among EFL Learners
DOI:
https://doi.org/10.24256/ideas.v13i2.9004Keywords:
Artificial Intelligence, Assisted-Language Learning, Blended Learning, EFL Speaking SkillsAbstract
This systematic literature review synthesizes empirical research from 2020 to 2025 to evaluate the efficacy of Artificial Intelligence (AI) as an assisted-language learning tool in enhancing the speaking skills of English as a Foreign Language (EFL) learners. Following the PRISMA framework, an analysis of 22 studies reveals that AI tools, categorized into speech recognition tutors, conversational chatbots, and comprehensive platforms, demonstrate significant potential in improving specific micro-skills. The evidence indicates that pronunciation is the most frequently enhanced aspect (cited in 19 out of 22 studies), followed by fluency (17 studies), vocabulary (14 studies), and grammar (13 studies). Key pedagogical drivers underpinning this efficacy are the provision of immediate, personalized feedback, adaptive learning paths, and the creation of low-anxiety practice environments, which also contribute to improved speaking confidence (noted in 12 studies). However, the review identifies persistent and significant challenges. These include technical limitations in processing non-native accents and spontaneous speech, a lack of human-like interaction for developing pragmatic and cultural competencies, and critical issues of equitable access and data privacy. Methodologically, the field is constrained by small-scale studies (samples of n=20 to n=93) and short intervention periods (4 to 14 weeks), which limit generalizability. The central conclusion is that successful outcomes are not dependent on the AI tools alone but hinge on their strategic, pedagogically-aligned integration within a blended learning ecosystem that complements human instruction. Consequently, this study provides practical implications for curriculum design and teacher training, emphasizing the need for pedagogical integration over mere tool adoption. It recommends future research to pursue longitudinal studies, more robust methodological designs, and the development of more linguistically and culturally adaptive AI technologies to bridge the gap between technological potential and equitable, effective educational practice.
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