The impact of AI-enhanced interactive learning environments on EFL teachers’ computational thinking pedagogical knowledge
A multi-arm longitudinal experimental study
Bibliographic Data
| ID | 21632142 |
|---|---|
| Authors | Xiang Xu (0000-0002-7166-4897, Xinyang College of Agriculture and Forestry, corresponding author), Akbar Bahari (0000-0002-4575-6480, Urmia University) |
| Year | 2026 |
| Pages | 1-28 |
| Publication date | 2026-03-13 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Interactive Learning Environments (JOURNAL) |
| Journal identifiers | ISSN: 1049-4820 • E-ISSN: 1744-5191 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/10494820.2026.2631730 |
| OpenAlex | W7135172906 |
| Language | EN |
| References cited | 64 |
Despite growing AI integration into language education, evidence on how AI-enhanced interactive learning environments (AI-ILEs) cultivate EFL teachers' computational thinking pedagogical knowledge (CTPK) remains limited. This explanatory sequential mixed-methods study embedded a multi-arm randomized controlled trial within five Chinese universities. Quantitatively, 125 postgraduate EFL teachers were randomly assigned (1:1:1:1:1; block randomization stratified by site, experience, gender) to four AI-ILE interventions—Instructional Decomposition/Collaborative Planning, Pattern Recognition/Formative Assessment, Conceptual Abstraction/Mapping, Algorithmic Sequencing/Debugging—or non-AI control. CTPK was measured at three timepoints. MANCOVA with multiple imputation and inverse-probability weighting addressed missing data and attrition. Qualitatively, semi-structured interviews (n = 20; maximum-variation sampling) underwent dual-cycle thematic coding, supported by intercoder agreement and member checking. Integration occurred through joint display matrices generating meta-inferences. Results indicated clear intervention effects, with Decomposition/Collaborative Planning demonstrating the largest sustained CTPK gains. Qualitative themes revealed enhanced diagnostic precision requiring cultural calibration, professional identity shifts necessitating institutional support, and sustainability linked to collaborative communities. Meta-inferences showed convergence between quantitative efficacy and qualitative confidence gains, with divergence highlighting that efficacy alone does not ensure implementation without addressing algorithmic bias. Findings support prioritizing decomposition-based AI-ILEs while mandating culturally responsive design for scalable, equity-centered implementation
Computational Thinking · Educational technology · Electronic learning · Interactive computing · Interactive Learning · Interactive media · Interactive simulation · Teaching method · Intelligent Tutoring Systems and Adaptive Learning · Online Learning and Analytics · Teaching and Learning Programming
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| Citation velocity | historical |
|---|---|
| Highly cited | No |