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Artificial Intelligence in Adaptive and Intelligent Educational System: A Review

Author

Listed:
  • Jingwen Dong

    (Faculty of Computer Science & Information Technology, Universiti Putra Malaysia, Serdang 43400, Malaysia)

  • Siti Nurulain Mohd Rum

    (Faculty of Computer Science & Information Technology, Universiti Putra Malaysia, Serdang 43400, Malaysia)

  • Khairul Azhar Kasmiran

    (Faculty of Computer Science & Information Technology, Universiti Putra Malaysia, Serdang 43400, Malaysia)

  • Teh Noranis Mohd Aris

    (Faculty of Computer Science & Information Technology, Universiti Putra Malaysia, Serdang 43400, Malaysia)

  • Raihani Mohamed

    (Faculty of Computer Science & Information Technology, Universiti Putra Malaysia, Serdang 43400, Malaysia)

Abstract

There has been much discussion among academics on how pupils may be taught online while yet maintaining a high degree of learning efficiency, in part because of the worldwide COVID-19 pandemic in the previous two years. Students may have trouble focusing due to a lack of teacher–student interaction, yet online learning has some advantages that are unavailable in traditional classrooms. The architecture of online courses for students is integrated into a system called the Adaptive and Intelligent Education System (AIES). In AIESs, reinforcement learning is often used in conjunction with the development of teaching strategies, and this reinforcement-learning-based system is known as RLATES. As a prerequisite to conducting research in this field, this paper undertakes the consolidation and analysis of existing research, design approaches, and model categories for adaptive and intelligent educational systems, with the hope of serving as a reference for scholars in the same field to help them gain access to the relevant information quickly and easily.

Suggested Citation

  • Jingwen Dong & Siti Nurulain Mohd Rum & Khairul Azhar Kasmiran & Teh Noranis Mohd Aris & Raihani Mohamed, 2022. "Artificial Intelligence in Adaptive and Intelligent Educational System: A Review," Future Internet, MDPI, vol. 14(9), pages 1-11, August.
    • Handle: RePEc:gam:jftint:v:14:y:2022:i:9:p:245-:d:895425
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    References listed on IDEAS

    as
    1. Canhoto, Ana Isabel & Clear, Fintan, 2020. "Artificial intelligence and machine learning as business tools: A framework for diagnosing value destruction potential," Business Horizons, Elsevier, vol. 63(2), pages 183-193.
    2. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
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