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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Bibliographic Details
Published in:Education sciences Vol. 15; no. 3; p. 301
Main Authors: Naseer, Fawad, Khan, Muhammad Nasir, Addas, Abdullah, Awais, Qasim, Ayub, Nafees
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.03.2025
Subjects:
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
Pakistan
ISSN:2227-7102, 2227-7102
Online Access:Get full text
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Summary: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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ISSN:2227-7102
2227-7102
DOI:10.3390/educsci15030301