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An Exploratory Study on the Efficacy and Inclusivity of AI Technologies in Diverse Learning Environments

Author

Listed:
  • Michael Pin-Chuan Lin

    (Faculty of Education, Mount Saint Vincent University, Halifax, NS B3M 2J6, Canada)

  • Arita Li Liu

    (Center for Educational Excellence, Simon Fraser University, Burnaby, BC V5A 1S6, Canada)

  • Eric Poitras

    (Faculty of Computer Science, Dalhousie University, Halifax, NS B3H 4R2, Canada)

  • Maiga Chang

    (Faculty of Science and Technology, Athabasca University, Athabasca, AB T9S 3A3, Canada
    Multidisciplinary Academic Research Center, National Dong Hwa University, Hualien County 974, Taiwan)

  • Daniel H. Chang

    (Faculty of Education, Simon Fraser University, Burnaby, BC V5A 1S6, Canada)

Abstract

This exploratory research conducted a thematic analysis of students’ experiences and utilization of AI tools by students in educational settings. We surveyed 87 undergraduates from two different educational courses at a comprehensive university in Western Canada. Nine integral themes that represent AI’s role in student learning and key issues with respect to AI have been identified. The study yielded three critical insights: the potential of AI to expand educational access for a diverse student body, the necessity for robust ethical frameworks to govern AI, and the benefits of personalized AI-driven support. Based on the results, a model is proposed along with recommendations for an optimal learning environment, where AI facilitates meaningful learning. We argue that integrating AI tools into learning has the potential to promote inclusivity and accessibility by making learning more accessible to diverse students. We also advocate for a shift in perception among educational stakeholders towards AI, calling for de-stigmatization of its use in education. Overall, our findings suggest that academic institutions should establish clear, empirical guidelines defining student conduct with respect to what is considered appropriate AI use.

Suggested Citation

  • Michael Pin-Chuan Lin & Arita Li Liu & Eric Poitras & Maiga Chang & Daniel H. Chang, 2024. "An Exploratory Study on the Efficacy and Inclusivity of AI Technologies in Diverse Learning Environments," Sustainability, MDPI, vol. 16(20), pages 1-19, October.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:20:p:8992-:d:1500854
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    References listed on IDEAS

    as
    1. Daniel H. Chang & Michael Pin-Chuan Lin & Shiva Hajian & Quincy Q. Wang, 2023. "Educational Design Principles of Using AI Chatbot That Supports Self-Regulated Learning in Education: Goal Setting, Feedback, and Personalization," Sustainability, MDPI, vol. 15(17), pages 1-15, August.
    2. Mahmud, Hasan & Islam, A.K.M. Najmul & Ahmed, Syed Ishtiaque & Smolander, Kari, 2022. "What influences algorithmic decision-making? A systematic literature review on algorithm aversion," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
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    Cited by:

    1. Agnieszka Zofia Miluniec & Ireneusz Miciula & Maciej Osmycki, 2025. "Disciplinary Differences in Students’ Attitudes Toward Artificial Intelligence in Higher Education: A Comparative Mixed-Methods Study," European Research Studies Journal, European Research Studies Journal, vol. 0(3), pages 1733-1753.

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