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<p>The integration of Artificial Intelligence (AI) conversational agents in Computer-Assisted Language Learning (CALL) environments has received attention for its potential to enhance language learning outcomes. This study investigates the effects of AI conversational agents on EFL learners'&#xa0;self-regulated learning (SRL)&#xa0;and&#xa0;retention&#xa0;in CALL environments. A pretest–posttest experiment involving 203 EFL students was conducted,&#xa0;using pretest–posttest measures of SRL (goal-setting, monitoring, reflection) and retention (vocabulary/grammar recall). A&#xa0;mixed-methods approach&#xa0;evaluated the effectiveness of AI agents with&#xa0;adaptive feedback—defined here as real-time, error-specific corrections and scaffolded guidance tailored to individual learner performance.&#xa0;Quantitative results&#xa0;revealed that SRL pretest scores ranged from 2.98 to 3.10 (SD = 0.589–0.678), while posttest scores increased to 6.00–6.96 (SD = 0.194–0.808), reflecting&#xa0;a 133% improvement in SRL strategies. Retention pretest scores (3.00–3.24, SD = 1.355–1.366) also improved significantly.&#xa0;The adaptive feedback group outperformed other interventions, with posttest SRL scores 16% higher than non-adaptive groups (ΔM = 0.96, p &lt; 0.001), indicating stronger&#xa0;metacognitive strategy use&#xa0;and&#xa0;long-term knowledge retention.&#xa0;Qualitative analysis&#xa0;highlighted learners’ perceptions of adaptive feedback as critical for&#xa0;personalized goal-setting&#xa0;and&#xa0;error correction. The study underscores the need to integrate&#xa0;operationalized adaptive feedback strategies—such as dynamic error prioritization and scaffolded explanations—into AI agents to optimize SRL and retention in EFL contexts.&#xa0;</p>

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How artificially intelligent conversational agents influence EFL learners'self-regulated learning and retention

  • 期刊论文

  • 发表日期：

    2025年6月2日

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  • Qiaoling Du

摘要

The integration of Artificial Intelligence (AI) conversational agents in Computer-Assisted Language Learning (CALL) environments has received attention for its potential to enhance language learning outcomes. This study investigates the effects of AI conversational agents on EFL learners' self-regulated learning (SRL) and retention in CALL environments. A pretest–posttest experiment involving 203 EFL students was conducted, using pretest–posttest measures of SRL (goal-setting, monitoring, reflection) and retention (vocabulary/grammar recall). A mixed-methods approach evaluated the effectiveness of AI agents with adaptive feedback—defined here as real-time, error-specific corrections and scaffolded guidance tailored to individual learner performance. Quantitative results revealed that SRL pretest scores ranged from 2.98 to 3.10 (SD = 0.589–0.678), while posttest scores increased to 6.00–6.96 (SD = 0.194–0.808), reflecting a 133% improvement in SRL strategies. Retention pretest scores (3.00–3.24, SD = 1.355–1.366) also improved significantly. The adaptive feedback group outperformed other interventions, with posttest SRL scores 16% higher than non-adaptive groups (ΔM = 0.96, p < 0.001), indicating stronger metacognitive strategy use and long-term knowledge retention. Qualitative analysis highlighted learners’ perceptions of adaptive feedback as critical for personalized goal-setting and error correction. The study underscores the need to integrate operationalized adaptive feedback strategies—such as dynamic error prioritization and scaffolded explanations—into AI agents to optimize SRL and retention in EFL contexts.