Scholarlink

<p>Situated in AI-assisted English learning, this study examined how emotions and engagement influence flow. Partial least squares-structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA) were employed to analyze the data from 270 Chinese EFL learners. PLS-SEM results indicated that positive emotions strongly and positively predicted flow, while negative emotions had no significant direct impact. Both emotions indirectly impacted flow via engagement. fsQCA identified two primary learner groups that experience high level of flow, namely “live wires” and “go-getters”. Furthermore, the “go-getters” group can be subdivided into “cheerful strivers” and “steady navigators”. Collectively, these combined results could provide deeper insights into reinforcing learners’ flow in AI-assisted English learning.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

The Effects of Achievement Emotions and Engagement on Flow in AI-assisted English Learning Contexts: Insights from PLS-SEM and fsQCA Approach

  • 期刊论文

  • 发表日期：

    2026年2月1日

查看全文
  • Xiaoxuan Yu,
  • Lianghong Hui

摘要

Situated in AI-assisted English learning, this study examined how emotions and engagement influence flow. Partial least squares-structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA) were employed to analyze the data from 270 Chinese EFL learners. PLS-SEM results indicated that positive emotions strongly and positively predicted flow, while negative emotions had no significant direct impact. Both emotions indirectly impacted flow via engagement. fsQCA identified two primary learner groups that experience high level of flow, namely “live wires” and “go-getters”. Furthermore, the “go-getters” group can be subdivided into “cheerful strivers” and “steady navigators”. Collectively, these combined results could provide deeper insights into reinforcing learners’ flow in AI-assisted English learning.