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Artificial Intelligence-Enabled Game-Based Learning and Quality of Experience: A Novel and Secure Framework (B-AIQoE)

Author

Listed:
  • Asif Ali Wagan

    (Department of Computer Science, Sindh Madressatul Islam University, Karachi 74000, Pakistan)

  • Abdullah Ayub Khan

    (Department of Computer Science, Sindh Madressatul Islam University, Karachi 74000, Pakistan
    Department of Computer Science and Information Technology, Bhutto Shaheed University Lyari, Karachi 75660, Pakistan)

  • Yen-Lin Chen

    (Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei 106344, Taiwan)

  • Por Lip Yee

    (Department of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur 50603, Malaysia)

  • Jing Yang

    (Department of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur 50603, Malaysia)

  • Asif Ali Laghari

    (Software Collage, Shenyang Normal University, Shenyang 110034, China)

Abstract

Game-based learning in schools and colleges, with the help of AI-enabled augmented intelligence techniques, is reported to improve children’s neurodevelopment, intellectual sensing, and specific learning abilities, according to US officials. There is currently a huge transformation from traditional assisted learning to augmented reality-enabled computer-based learning. Globally, there has been a dramatic increase in the use of game-based augmented learning in most schools and colleges. A few problems arise that create concern, such as the emerging effects of gaming on institutional premises, the disordering of children’s involvement after game-learning, the rate of learning and attendance, adaptation, and teachers’ experience. To address these individual aspects, we proposed a blockchain Ethereum-enabled, secure AI-based augmented game learning environment, called B-AIQoE, in which protected on-chain and off-chain channels are designed to handle all the transactions and exchanges among students before analysis in terms of color transition, redundancy, unethical transmission, and related vulnerabilities. On the other hand, the proposed system examines and analyzes the Quality of experience (QoE) and improves accessibility as it receives feedback from the students and teachers. For the purpose of automating game-based transactions, three different aspects are discussed, such as verifying and validating student-teacher registration, creating content for game-based learning and privacy, and updating students’ records and exchanges. Finally, this paper separates, analyzes, and discusses a list of emerging challenges and limitations and their possible solutions involved in creating the proposed system.

Suggested Citation

  • Asif Ali Wagan & Abdullah Ayub Khan & Yen-Lin Chen & Por Lip Yee & Jing Yang & Asif Ali Laghari, 2023. "Artificial Intelligence-Enabled Game-Based Learning and Quality of Experience: A Novel and Secure Framework (B-AIQoE)," Sustainability, MDPI, vol. 15(6), pages 1-12, March.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:6:p:5362-:d:1100261
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    References listed on IDEAS

    as
    1. Kavisha Duggal & Lovi Raj Gupta & Parminder Singh, 2021. "Gamification and Machine Learning Inspired Approach for Classroom Engagement and Learning," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-18, May.
    2. Xuesong Zhai & Xiaoyan Chu & Ching Sing Chai & Morris Siu Yung Jong & Andreja Istenic & Michael Spector & Jia-Bao Liu & Jing Yuan & Yan Li & Ning Cai, 2021. "A Review of Artificial Intelligence (AI) in Education from 2010 to 2020," Complexity, Hindawi, vol. 2021, pages 1-18, April.
    3. Özge Kelleci & Nuri Can Aksoy, 2021. "Using Game-Based Virtual Classroom Simulation in Teacher Training: User Experience Research," Simulation & Gaming, , vol. 52(2), pages 204-225, April.
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