Scholarlink

<p>Integrating Artificial Intelligence (AI) in education has revolutionised various pedagogical practices, including supervision methods. E-supervision leveraging AI-driven tools offers real-time monitoring, personalised feedback, and adaptive learning support. Despite the growing adoption of AI in education, there is a lack of empirical studies evaluating its direct impact on academic achievement through e-supervision, marking a critical research gap. This study examines the efficacy of AI-based e-supervision in improving student academic behaviour, focusing on AI compatibility, perceived security risks, perceived privacy risks, perceived trust, and perceived usefulness of e-supervision on academic behaviour. A quantitative survey research design is employed, involving 424 undergraduate students as the sample. We generated an online survey and analysed the data using partial least squares structural equation modelling (PLS-SEM). Findings demonstrate that compatibility, perceived security risks, perceived privacy risks, and perceived usefulness significantly enhance academic behaviour, while perceived trust did not. The study concludes that while AI tools offer promising benefits for academic support, their effectiveness depends on careful alignment with students’ psychological needs. It provides educators and policymakers with insights to enhance AI-based supervision models for effective learning outcomes.</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

Efficacy of e-supervision on students’ academic behaviour in the era of artificial intelligence

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月4日

查看全文
  • Oluwaseyi Aina Gbolade Opesemowo,
  • Sulaimon Adewale,
  • Titilope Rachael Opesemowo

摘要

Integrating Artificial Intelligence (AI) in education has revolutionised various pedagogical practices, including supervision methods. E-supervision leveraging AI-driven tools offers real-time monitoring, personalised feedback, and adaptive learning support. Despite the growing adoption of AI in education, there is a lack of empirical studies evaluating its direct impact on academic achievement through e-supervision, marking a critical research gap. This study examines the efficacy of AI-based e-supervision in improving student academic behaviour, focusing on AI compatibility, perceived security risks, perceived privacy risks, perceived trust, and perceived usefulness of e-supervision on academic behaviour. A quantitative survey research design is employed, involving 424 undergraduate students as the sample. We generated an online survey and analysed the data using partial least squares structural equation modelling (PLS-SEM). Findings demonstrate that compatibility, perceived security risks, perceived privacy risks, and perceived usefulness significantly enhance academic behaviour, while perceived trust did not. The study concludes that while AI tools offer promising benefits for academic support, their effectiveness depends on careful alignment with students’ psychological needs. It provides educators and policymakers with insights to enhance AI-based supervision models for effective learning outcomes.