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.
Keywords
Gamification, Transformer Models, AI Feedback, Adaptive Assessment, Language Learning, NLP, Personalized Learning.
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CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: Abdullah A Alanzi and Ahmed I Taloba;
Methodology: Abdullah A Alanzi;
Software: Ahmed I Taloba;
Data Curation: Ahmed I Taloba;
Writing- Original Draft Preparation: Abdullah A Alanzi and Ahmed I Taloba;
Visualization: Abdullah A Alanzi and Ahmed I Taloba;
Investigation: Abdullah A Alanzi;
Supervision: Ahmed I Taloba;
Validation: Abdullah A Alanzi;
Writing- Reviewing and Editing: Abdullah A Alanzi and Ahmed I Taloba;
All authors reviewed the results and approved the final version of the manuscript.
Acknowledgements
Author(s) thanks to Deanship of Graduate Studies and Scientific Research at Jouf University for this research support.
Funding
This work was funded by the Deanship of Graduate Studies and Scientific Research at Jouf University under grant No.
(DGSSR-2023-03-02287)
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Abdullah A Alanzi
Department of Educational Psychology, College of Education, Jouf University, Sakaka, Saudi Arabia.
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Cite this article
Abdullah A Alanzi and Ahmed I Taloba, “Gamification and Deep Learning-Driven Transformer Feedback Mechanism for Adaptive Language Learning Assessment”, Journal of Machine and Computing, pp. 789-803, April 2025, doi: 10.53759/7669/jmc202505062.
