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<p>Amid the rapid integration of technology in education, this study investigates the divergent effects of digital factors on learning outcomes in a Sustainable Development Goal (SDG)-aligned English as a Foreign Language (EFL) in mathematic context. Specifically, it examines how technostress, perceived AI dependency, and generative AI usage predict EFL mathematic Metacognitive Reading Strategies, with reading motivation as a mediator and examined the moderating role of reading self-efficacy in the relationship between metacognitive reading strategies and SDG integration. A quantitative cross-sectional design was employed, collecting data via an online survey from <i>n</i> = 392 EFL mathematic university students. Analysis using Partial Least Squares Structural Equation Modelling (PLS-SEM) revealed a bifurcated effect: technostress negatively affected Metacognitive Reading Strategies through reduced motivation, whereas active generative AI use predicted Metacognitive Reading Strategies through increased motivation. Perceived AI dependency showed no significant effect. Crucially, reading self-efficacy not only directly predicted SDG integration but also strengthened the positive link between metacognitive reading strategies and SDG outcomes. The study’s novelty lies in juxtaposing technology’s stress-inducing and scaffolding roles within a unified framework centred on global competence. Implications call for pedagogies that mitigate digital burnout, strategically leverage AI as a motivational scaffold, and explicitly foster self-efficacy to equip learners as proactive agents for sustainable development.</p>

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The impact of technostress and generative AI on EFL mathematic metacognitive reading strategies and digital classroom burnout in SDG-aligned education

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月11日

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  • Qiao Jingjing,
  • Zhao Yan,
  • Xu Xiaobing,
  • Li Li,
  • Aasia Nusrat

摘要

Amid the rapid integration of technology in education, this study investigates the divergent effects of digital factors on learning outcomes in a Sustainable Development Goal (SDG)-aligned English as a Foreign Language (EFL) in mathematic context. Specifically, it examines how technostress, perceived AI dependency, and generative AI usage predict EFL mathematic Metacognitive Reading Strategies, with reading motivation as a mediator and examined the moderating role of reading self-efficacy in the relationship between metacognitive reading strategies and SDG integration. A quantitative cross-sectional design was employed, collecting data via an online survey from n = 392 EFL mathematic university students. Analysis using Partial Least Squares Structural Equation Modelling (PLS-SEM) revealed a bifurcated effect: technostress negatively affected Metacognitive Reading Strategies through reduced motivation, whereas active generative AI use predicted Metacognitive Reading Strategies through increased motivation. Perceived AI dependency showed no significant effect. Crucially, reading self-efficacy not only directly predicted SDG integration but also strengthened the positive link between metacognitive reading strategies and SDG outcomes. The study’s novelty lies in juxtaposing technology’s stress-inducing and scaffolding roles within a unified framework centred on global competence. Implications call for pedagogies that mitigate digital burnout, strategically leverage AI as a motivational scaffold, and explicitly foster self-efficacy to equip learners as proactive agents for sustainable development.