Gamification and Deep Learning-Driven Transformer Feedback Mechanism for Adaptive Language Learning Assessment

The integration of gamification in education has emerged as a transformative approach to enhance student engagement and academic achievement. Traditional language learning methods often lack personalization, real-time feedback, and adaptive assessment, leading to disengagement and ineffective learning. Existing AI-driven language learning approaches, while promising, primarily focus on rule-based assessments or simple feedback mechanisms, failing to provide context-aware, interactive, and adaptive learning experiences. To address these limitations, this study proposes a gamification-enhanced, Deep learning-driven Transformer framework that offers intelligent feedback and adaptive assessment to enhance language learning outcomes. The gamification elements such as leaderboards, achievement badges, adaptive difficulty levels, and interactive challenges enhance engagement and motivation. The adaptive assessment mechanism dynamically adjusts task complexity based on the learner’s progress, ensuring a customized learning experience. The proposed system integrates pre-trained Transformer models to analyze learners' responses in real-time, providing personalized feedback on fluency, grammar, vocabulary, and pronunciation. Cronbach's Alpha was employed to ensure the reliability of the developed questionnaires and achievement tests, confirming their internal consistency. The proposed system achieves 99.9% adaptive feedback accuracy, ensuring precise, real-time assessments that enhance language learning effectiveness. The findings indicate that gamification and advanced Transformer based feedback mechanisms significantly enhance students' comprehension and achievement in education, providing educators with innovative strategies to improve instructional methods and foster a more engaging learning experience. This research highlights the potential of integrating gamification and intelligent assessment technologies to transform education, promoting effective learning outcomes and student success.

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Institutions

Publication Details

Journal
Journal of Machine and Computing
Published
2025-04-05
DOI
https://doi.org/10.53759/7669/jmc202505062
Citations
1
Primary Topic
Educational Games and Gamification
Type
article
Field-Weighted Citation Impact
3.44
Controls
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article

Gamification and Deep Learning-Driven Transformer Feedback Mechanism for Adaptive Language Learning Assessment

Ahmed I. Taloba, Abdullah Alanzi
1 citations
Journal of Machine and Computing
Educational Games and Gamification
3.44
article

Gamification and Deep Learning-Driven Transformer Feedback Mechanism for Adaptive Language Learning Assessment

Ahmed I. Taloba, Abdullah Alanzi
article en
1 citations

Abstract

The integration of gamification in education has emerged as a transformative approach to enhance student engagement and academic achievement. Traditional language learning methods often lack personalization, real-time feedback, and adaptive assessment, leading to disengagement and ineffective learning. Existing AI-driven language learning approaches, while promising, primarily focus on rule-based assessments or simple feedback mechanisms, failing to provide context-aware, interactive, and adaptive learning experiences. To address these limitations, this study proposes a gamification-enhanced, Deep learning-driven Transformer framework that offers intelligent feedback and adaptive assessment to enhance language learning outcomes. The gamification elements such as leaderboards, achievement badges, adaptive difficulty levels, and interactive challenges enhance engagement and motivation. The adaptive assessment mechanism dynamically adjusts task complexity based on the learner’s progress, ensuring a customized learning experience. The proposed system integrates pre-trained Transformer models to analyze learners' responses in real-time, providing personalized feedback on fluency, grammar, vocabulary, and pronunciation. Cronbach's Alpha was employed to ensure the reliability of the developed questionnaires and achievement tests, confirming their internal consistency. The proposed system achieves 99.9% adaptive feedback accuracy, ensuring precise, real-time assessments that enhance language learning effectiveness. The findings indicate that gamification and advanced Transformer based feedback mechanisms significantly enhance students' comprehension and achievement in education, providing educators with innovative strategies to improve instructional methods and foster a more engaging learning experience. This research highlights the potential of integrating gamification and intelligent assessment technologies to transform education, promoting effective learning outcomes and student success.

Journal of Machine and Computing
Jouf University (SA)
Openalex Percentile: Top 11%
Educational Games and Gamification
3.44
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