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Artificial Intelligence in Education: Can AI bring the full potential of personalized learning to education?

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  • van der Vorst, Tommy
  • Jelicic, Nick

Abstract

In this study we explore the potential impact of educational AI applications in personalized learning. According to Bloom (1984) students that are tutored one-to-one perform two standard deviations better than students who learn via traditional educational methods. Due to the limited amount of teachers and costs associated, personalized one-to-one learning is not generally feasible from a societal point of view. Breakthroughs in the field of machine learning offer promising avenues to aid in personalized learning. AI may hence be the 'holy grail' in unlocking the potential of one-to-one learning, by enabling applications to offer personalized teaching to each individual student. We assess the potential impact of AI in personalized learning from a socio-technical perspective. Therefore, we investigate the technological possibilities, as well as any aspects that may impact adoption, e.g. legal, societal and ethical. To conclude we formulate policy options that can stimulate the adoption of AI-driven personalized learning applications.

Suggested Citation

  • van der Vorst, Tommy & Jelicic, Nick, 2019. "Artificial Intelligence in Education: Can AI bring the full potential of personalized learning to education?," 30th European Regional ITS Conference, Helsinki 2019 205222, International Telecommunications Society (ITS).
    • Handle: RePEc:zbw:itse19:205222
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    Cited by:

    1. Daina Gudoniene & Evelina Staneviciene & Vytautas Buksnaitis & Nicola Daley, 2023. "The Scenarios of Artificial Intelligence and Wireframes Implementation in Engineering Education," Sustainability, MDPI, vol. 15(8), pages 1-18, April.

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