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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.

Details

1009240
Business indexing term
Title
Game Mechanics and Artificial Intelligence Personalization: A Framework for Adaptive Learning Systems
Author
Naseer, Fawad 1   VIAFID ORCID Logo  ; Muhammad Nasir Khan 2   VIAFID ORCID Logo  ; Addas, Abdullah 3   VIAFID ORCID Logo  ; Qasim Awais 4 ; Ayub, Nafees 5   VIAFID ORCID Logo 

 Computer Science and Software Engineering Department, Beaconhouse International College, Faisalabad 38000, Pakistan 
 Electrical Engineering Department, Government College University Lahore (GCUL), Lahore 54000, Pakistan; [email protected] 
 Department of Civil Engineering, College of Engineering, Prince Sattam Bin Abdulaziz University, Alkharj 11492, Saudi Arabia; Landscape Architecture Department, Faculty of Architecture and Planning, King Abdulaziz University, P.O. Box 80210, Jeddah 21589, Saudi Arabia 
 Department of Electronics and Computer Science, Fatima Jinnah Women University, Old Presidency, Rawalpindi 46000, Pakistan; [email protected] 
 Computer Science Department, Government College University Faisalabad (GCUF), Faisalabad 38000, Pakistan; [email protected] 
Publication title
Volume
15
Issue
3
First page
301
Publication year
2025
Publication date
2025
Publisher
MDPI AG
Place of publication
Basel
Country of publication
Switzerland
Publication subject
e-ISSN
22277102
Source type
Scholarly Journal
Language of publication
English
Document type
Journal Article
Publication history
 
 
Online publication date
2025-02-27
Milestone dates
2024-12-30 (Received); 2025-02-24 (Accepted)
Publication history
 
 
   First posting date
27 Feb 2025
ProQuest document ID
3181430884
Document URL
https://www.proquest.com/scholarly-journals/game-mechanics-artificial-intelligence/docview/3181430884/se-2?accountid=208611
Copyright
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Last updated
2025-03-28
Database
2 databases
  • Coronavirus Research Database
  • ProQuest One Academic