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Title
AI-enabled Gamification
Subtitle (en)
A New Paradigm for Gamified Digital Learning Environments
Language
English
Description (en)
The integration of Artificial Intelligence (AI) into gamified digital learning environments represents a significant shift in educational technology. AI-enabled gamification enhances traditional gamification by introducing dynamic, adaptive elements that cater to individual learning needs, thereby addressing long-standing challenges in student engagement and motivation. This paper explores the theoretical under pinnings of gamification, emphasising the importance of intrinsic motivation as described by the Self-Determination Theory, and critiques traditional methods for their reliance on extrinsic rewards. AI’s characteristics of autonomy and adaptivity allow for real-time data analytics and learner behavior prediction, creating person alised educational experiences that evolve with the learner. This approach fosters deeper engagement and sustained motivation, potentially leading to improved academic outcomes. However, the adoption of AI in education raises ethical concerns regarding data privacy and algorithmic bias, necessitating careful consideration and transparent practices. Ultimately, AI-enabled gamification offers a more nuanced and effective methodology for teaching, learning, and assessment, marking a paradigm shift from static, one-size-fits-all approaches to dynamic, learner-centered education.
Keywords (en)
personalised learning
Keywords (en)
engagement
Keywords (en)
motivation
Keywords (en)
gamification
Keywords (en)
artificial intelligence
DOI
Author of the digital object
Stephen  Bezzina  (University of Malta)
Alexiei  Dingli  (University of Malta)
20.08.2024
Format
application/pdf
Size
113.1 kB
Licence Selected
CC BY-NC 4.0 International
Type of publication
Article
Name of Publication (en)
DAC - DIGITAL JOURNAL FOR ARTS & CULTURAL STUDIES
Name of Publication (de)
DAC - DIGITALES JOURNAL FÜR KUNST- & KULTURWISSENSCHAFTEN
Series Title
Variations on how to play
Volume
01
Publication Date
2024
Citable links
Restricted access
Details
Uploader
Object type
PDFDocument
Format
application/pdf
Created
20.08.2024 07:26:19
Metadata
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