Learning desk

MultiAgent EDU StackGather good sources. Teach what matters.
T3Mermaid to ASCII art (mermaid-ascii)T3Kimi K3, and what we can still learn from the pelican benchmarkT3Firefox in WebAssemblyT3Spot birds not golfT3[AINews] Kimi K3 2.8T-A50B: the largest open model ever released; Opus 4.8-class at Sonnet 5 pricingT1From physical surfaces to human-centric heat stress: LST and UTCI heat mapping reveals nonlinear effects of urban morphologyT1DualHNIE: Dual-Channel Hypergraph Learning for Node Importance Estimation in Heterogeneous Knowledge GraphsT1GenTL: A General Transfer Learning Model for Building Thermal DynamicsT1A short review on the maximum clique problem algorithms with classical, AI, and quantum methodsT1Man, Machine, and Masterpiece: Artistic Ownership in the AI EraT1HABIB_TAZ at SemEval-2026 Task 11: Disentangling Formal Logic from Content via Synthetic Training and Multi-Objective OptimizationT1How Well Does AI-Generated Feedback Work? Intrinsic and Extrinsic Evaluation across more than 20,000 EFL Essay DraftsT3Mermaid to ASCII art (mermaid-ascii)T3Kimi K3, and what we can still learn from the pelican benchmarkT3Firefox in WebAssemblyT3Spot birds not golfT3[AINews] Kimi K3 2.8T-A50B: the largest open model ever released; Opus 4.8-class at Sonnet 5 pricingT1From physical surfaces to human-centric heat stress: LST and UTCI heat mapping reveals nonlinear effects of urban morphologyT1DualHNIE: Dual-Channel Hypergraph Learning for Node Importance Estimation in Heterogeneous Knowledge GraphsT1GenTL: A General Transfer Learning Model for Building Thermal DynamicsT1A short review on the maximum clique problem algorithms with classical, AI, and quantum methodsT1Man, Machine, and Masterpiece: Artistic Ownership in the AI EraT1HABIB_TAZ at SemEval-2026 Task 11: Disentangling Formal Logic from Content via Synthetic Training and Multi-Objective OptimizationT1How Well Does AI-Generated Feedback Work? Intrinsic and Extrinsic Evaluation across more than 20,000 EFL Essay Drafts
← Dispatches

Artificial Intelligence in English Language Teaching and Learning: A Scoping Review of Intelligent Computer-Assisted Language Learning (2015–2025)

Primary research

#381

T1digested
Topic
Language Learning
First seen
2026-07-16 23:33:02
Last seen
2026-07-16 23:33:02

Source raw items (1)

  • Semantic Scholar2026-07-16 23:31:52
    Artificial Intelligence in English Language Teaching and Learning: A Scoping Review of Intelligent Computer-Assisted Language Learning (2015–2025)

    This scoping review primarily aims to synthesize empirical research on artificial intelligence in English language teaching and learning published from 2015 to 2025. The main question investigates how the integration, applications, and pedagogical roles of AI have evolved over the past decade. The significance of this study is that it uses Intelligent Computer-Assisted Language Learning as an interpretive lens to make sense of a rapidly shifting field, offering a framework to help educators navigate modern generative tools. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidance, 129 empirical studies were identified and analyzed using descriptive mapping and thematic analysis. The main findings indicate three overlapping evolutionary phases: an early system construction phase focused on tutoring, a mobile-and-voice phase emphasizing speech practice, and a generative phase dominated by large language models. The evidence base remains heavily concentrated in higher education, where AI frequently acts as a tutor, practice partner, or co-writer. For further use, this study recommends adopting teacher-mediated task designs, shifting assessments to focus on the learning process, and prioritizing longitudinal research in primary and under-resourced educational settings.