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<p>The role of generative artificial intelligence (GenAI) in Informal Digital Learning of English (IDLE) remains underexplored, particularly regarding learner psychological mechanisms. Grounded in achievement goal theory, this study examines how mastery-approach goals influence motivated learning behavior in English acquisition through the mediating roles of enjoyment, L2 grit, and&#xa0;flow. A total of 1362 participants were divided into two groups&#xa0;based on their self-reported GenAI use: the GenAI-IDLE group (n = 709), utilizing AI tools, and the Traditional-IDLE group (n = 653), employing conventional methods. Partial least squares structural equation modeling (PLS-SEM) and multi-group path analysis were applied for comparative investigation. The results reveal: (1) In the full sample model, mastery-approach goals showed a positive direct effect on motivated&#xa0;learning behaviors, with significant mediating effects of enjoyment and flow, whereas L2 grit exhibits no mediating role; (2) Multi-group analysis further revealed that GenAI-supported IDLE strengthened the motivational pathways involving mastery-approach goals, enjoyment, flow, and motivated learning behavior, whereas the link between mastery-approach goals and L2 grit was stronger in traditional IDLE. These findings extend achievement goal theory to digital SLA contexts and suggest that GenAI-supported IDLE may be especially relevant to emotional and immersive motivational processes.</p>

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Generative AI and motivational pathways in informal English learning: the role of enjoyment and flow

  • 期刊论文

  • 发表日期：

    2026年7月17日

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  • Xiaowei Zhang,
  • Lianrui Yang,
  • Yu Cui,
  • Lingjie Tang,
  • Xiaolan Liu

摘要

The role of generative artificial intelligence (GenAI) in Informal Digital Learning of English (IDLE) remains underexplored, particularly regarding learner psychological mechanisms. Grounded in achievement goal theory, this study examines how mastery-approach goals influence motivated learning behavior in English acquisition through the mediating roles of enjoyment, L2 grit, and flow. A total of 1362 participants were divided into two groups based on their self-reported GenAI use: the GenAI-IDLE group (n = 709), utilizing AI tools, and the Traditional-IDLE group (n = 653), employing conventional methods. Partial least squares structural equation modeling (PLS-SEM) and multi-group path analysis were applied for comparative investigation. The results reveal: (1) In the full sample model, mastery-approach goals showed a positive direct effect on motivated learning behaviors, with significant mediating effects of enjoyment and flow, whereas L2 grit exhibits no mediating role; (2) Multi-group analysis further revealed that GenAI-supported IDLE strengthened the motivational pathways involving mastery-approach goals, enjoyment, flow, and motivated learning behavior, whereas the link between mastery-approach goals and L2 grit was stronger in traditional IDLE. These findings extend achievement goal theory to digital SLA contexts and suggest that GenAI-supported IDLE may be especially relevant to emotional and immersive motivational processes.