Game Mechanics and Artificial Intelligence Personalization: A Framework for Adaptive Learning Systems
The phenomenal growth of digital learning platforms has brought new learner engagement and retention challenges to higher education. This study proposes a framework that integrates game mechanics—leveling systems, badges, and timely feedback—with artificial intelligence (AI)-driven personalization t...
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| Vydáno v: | Education sciences Ročník 15; číslo 3; s. 301 |
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| Médium: | Journal Article |
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01.03.2025
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| ISSN: | 2227-7102, 2227-7102 |
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| Abstract | The phenomenal growth of digital learning platforms has brought new learner engagement and retention challenges to higher education. This study proposes a framework that integrates game mechanics—leveling systems, badges, and timely feedback—with artificial intelligence (AI)-driven personalization to meet the challenges of enhanced adaptability, motivation, and learning outcomes in online environments. Key design elements were identified through literature reviews and consultations with instructional design experts, leading to the development an adaptive learning platform prototype. The prototype underwent an eight-week pilot study with 250 Prince Sattam Bin Abdulaziz University (PSAU) students randomly assigned to a control group (non-adaptive system) or an experimental group (adaptive system). Data sources included pre- and post-tests, platform engagement analytics, and learner perception surveys. The results showed that the adaptive group outperformed the control group in the post-test scores (M = 85.2, SD = 6.4 vs. M = 78.5, SD = 7.2) and motivation levels (M = 4.2, SD = 0.7 vs. M = 3.6, SD = 0.8). Additionally, 82% of the adaptive group achieved mastery-level performance compared to 64% in the control group. These findings demonstrate the potential of integrating game mechanics and AI-driven personalization to transform digital learning, offering a roadmap for scalable, data-driven adaptive platforms. Future research will address long-term retention and diverse subject applications. |
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| AbstractList | The phenomenal growth of digital learning platforms has brought new learner engagement and retention challenges to higher education. This study proposes a framework that integrates game mechanics—leveling systems, badges, and timely feedback—with artificial intelligence (AI)-driven personalization to meet the challenges of enhanced adaptability, motivation, and learning outcomes in online environments. Key design elements were identified through literature reviews and consultations with instructional design experts, leading to the development an adaptive learning platform prototype. The prototype underwent an eight-week pilot study with 250 Prince Sattam Bin Abdulaziz University (PSAU) students randomly assigned to a control group (non-adaptive system) or an experimental group (adaptive system). Data sources included pre- and post-tests, platform engagement analytics, and learner perception surveys. The results showed that the adaptive group outperformed the control group in the post-test scores (M = 85.2, SD = 6.4 vs. M = 78.5, SD = 7.2) and motivation levels (M = 4.2, SD = 0.7 vs. M = 3.6, SD = 0.8). Additionally, 82% of the adaptive group achieved mastery-level performance compared to 64% in the control group. These findings demonstrate the potential of integrating game mechanics and AI-driven personalization to transform digital learning, offering a roadmap for scalable, data-driven adaptive platforms. Future research will address long-term retention and diverse subject applications. |
| Audience | Academic |
| Author | Awais, Qasim Ayub, Nafees Naseer, Fawad Khan, Muhammad Nasir Addas, Abdullah |
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| Cites_doi | 10.3390/educsci14080839 10.3390/informatics10030074 10.51473/rcmos.v1i1.2024.486 10.37134/ajelp.vol12.1.7.2024 10.3390/app12083982 10.3390/educsci14121324 10.4324/9781003246206-6 10.1002/cae.22477 10.3390/systems11010031 10.34069/AI/2024.79.07.9 10.4018/979-8-3693-0868-4.ch005 10.1145/3434780.3436665 10.4324/9781003413301-4 10.14445/22492593/IJCOT-V12I1P304 10.61707/v7498z68 10.1007/s10639-023-11585-z 10.4018/979-8-3693-1351-0.ch021 10.1016/j.heliyon.2024.e32628 10.1016/j.heliyon.2024.e39630 10.4018/979-8-3693-2165-2.ch009 10.30534/ijatcse/2021/171052021 10.1186/s41239-021-00289-4 10.3390/computers13120324 10.1155/2021/9922775 10.3390/buildings14092769 10.31098/icmrsi.v1i.786 10.3390/educsci13121216 10.3390/su14095428 10.1016/j.heliyon.2019.e01993 10.3390/educsci14060639 10.1109/ACCESS.2020.3036336 10.3390/electronics13183762 10.1108/JWL-11-2022-0160 10.1016/j.compedu.2024.105000 10.3389/feduc.2022.851019 10.22214/ijraset.2024.65088 10.3389/fcomp.2024.1480404 10.54941/ahfe1004068 |
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| SubjectTerms | Adaptive learning adaptive learning systems Adult Learning Algorithms Artificial intelligence At Risk Students Cognitive Psychology College dropouts Control Groups Customization Designers Difficulty Level Distance Education Distance learning Education Educational Environment Educational objectives Educational Psychology Electronic Learning Equipment and supplies Feedback (Response) Formative Evaluation framework development game mechanics Games Gamification Higher education Holistic Approach Individualized Instruction Influence of Technology Instructional design Instructional Materials Integrated approach Intelligent tutoring systems Learner Engagement Learning Management Systems Learning Modules Learning Motivation Lifelong Learning Literature reviews Mechanics Motivation Online instruction online learning platforms personalized learning Student Motivation Teachers Teaching |
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