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<p>The purpose of this mixed-methods study was to examine the factors predicting college students’ intention to use AI for academic tasks. Extending the Technology Acceptance Model (TAM), four factors, including understanding AI features, AI for future careers, AI integration for learning, and ethical concerns about AI, were examined to determine whether those factors influence students’ intention to use AI in academic tasks. A total of 112 students majoring in strategic communication from a midwestern U.S. university participated in the study. The regression results showed that the more students perceived AI integration to enhance their learning and AI as an integral part of the future, the more likely they were to use AI tools for academic tasks. Conversely, the greater their ethical concerns about AI, the less likely they were to use AI tools for academic tasks. Additionally, interviews with ten students revealed their perspectives on using AI tools, including emerging and diminishing professional traits, as well as positive impacts and concerns in the field of strategic communication. This study extends TAM by identifying discipline-specific usefulness dimensions and ethical barriers that predict AI adoption in academic settings, offering guidance for educators designing AI integration strategies in higher education.</p>

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Learning with AI: Student Intentions for Academic Use and Broader Perspectives on AI

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月16日

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  • Moon-Heum Cho,
  • EunHae Grace Park,
  • Seongmi Lim,
  • Hyeonho Yu

摘要

The purpose of this mixed-methods study was to examine the factors predicting college students’ intention to use AI for academic tasks. Extending the Technology Acceptance Model (TAM), four factors, including understanding AI features, AI for future careers, AI integration for learning, and ethical concerns about AI, were examined to determine whether those factors influence students’ intention to use AI in academic tasks. A total of 112 students majoring in strategic communication from a midwestern U.S. university participated in the study. The regression results showed that the more students perceived AI integration to enhance their learning and AI as an integral part of the future, the more likely they were to use AI tools for academic tasks. Conversely, the greater their ethical concerns about AI, the less likely they were to use AI tools for academic tasks. Additionally, interviews with ten students revealed their perspectives on using AI tools, including emerging and diminishing professional traits, as well as positive impacts and concerns in the field of strategic communication. This study extends TAM by identifying discipline-specific usefulness dimensions and ethical barriers that predict AI adoption in academic settings, offering guidance for educators designing AI integration strategies in higher education.