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May 18, 2026Technology Knowledge and Learning0 citationsOpen Access

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

MCMoon-Heum ChoEPEunHae Grace ParkSLSeongMi Lim

Key Points

  • This study aims to identify factors influencing college students' intentions to use AI for academic tasks.
  • Mixed-methods approach including regression analysis and interviews.
  • 112 students majoring in strategic communication participated.
  • Examined factors like understanding AI features, future careers, integration for learning, and ethical concerns.
  • Higher perceived AI integration for learning positively influenced intentions to use AI (p-value not specified).
  • Perceived importance of AI for future careers led to greater intention to use AI tools (specific metrics not provided).
  • Increased ethical concerns about AI significantly reduced students' likelihood of using AI tools for academics.

Abstract

Abstract 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.

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Cite This Study

Cho et al. (2026) studied this question.

synapsesocial.com/papers/6a0aad015ba8ef6d83b70656https://doi.org/10.1007/s10758-026-09982-7
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