GAMIFIED LEARNING OF VISUAL CONCEPTS USING AI
DOI:
https://doi.org/10.29121/shodhkosh.v6.i1s.2025.6652Keywords:
Artificial Intelligence, Gamified Learning, Visual Concept Acquisition, Adaptive Learning Systems, Deep Learning, Knowledge Tracing, Reinforcement Learning, Educational Technology, User Engagement, Learning AnalyticsAbstract [English]
This paper introduces a gamified learning model based on AI and aimed at increasing the visual concept mastery with the use of adaptive instructions, real-time feedback, and motivational elements of a game. The classical methods of visual learning usually presuppose the unrestricted use of a fixed material and less personalization that leads to lower levels of involvement and poor mastering. The proposed system will overcome these shortcomings by incorporating a visual recognition module trained on deep learning, a mastery-related knowledge-tracing model, and a task selection policy that is based on reinforcement learning to establish an efficient challenge-skill balance. The framework is accompanied by a gamification engine which includes points, levels, streaks, and adaptive rewards to keep the learners motivated. The model was operationalised in a multi-layer system architecture and its performance measured using quantitative measures of performance and interaction data of actual learners. Findings indicate that recognition is high (94.7%), mastery gains are significant +0.37), learning uncertainty is low -0.92 entropy and engagement is high (91% task completion rate). There is high acceptance and usability with user experience ratings comprising of a System Usability Scale (SUS) score of 87.5. In general, the results indicate that AI-based gamification is a significant method of increasing learning efficiency, engagement and adaptability and it provides a scalable and smart intervention to educate visual concepts.
References
Alenezi, A. (2023, October). Teacher Perspectives on AI-Driven Gamification: Impact on Student Motivation, Engagement, and Learning Outcomes. Information Technologies and Learning Tools, 97(5), 138–148. https://doi.org/10.33407/itlt.v97i5.5437 DOI: https://doi.org/10.33407/itlt.v97i5.5437
Ali, Y. K., Mammadova, M., Geyis, A. J., and Mehdiyeva, G. B. (2024, May). Enhancing ESL Education with AI: Innovations in Teaching Methods and Authentic Materials. International Journal of Membrane Science and Technology, 11(1), 497–505. https://doi.org/10.15379/ijmst.v11i1.3687 DOI: https://doi.org/10.15379/ijmst.v11i1.3687
Dehankar, A., Ghugare, K., Shinde, S., Rathod, P., Gadekar, S., and Chakole, P. (2025, May). Implementation of Autonomous Robot for Efficient Multitasking Operations. International Journal of Advanced Electronics and Computer Engineering (IJAECE), 14(1), 97–112.
Farhi, F., Jeljeli, R., Aburezeq, I., Dweikat, F. F., Al-Shami, S. A., and Slamene, R. (2023). Analyzing Students’ Views, Concerns, and Perceived Ethics About ChatGPT Usage. Computers and Education: Artificial Intelligence, 5, Article 100180. https://doi.org/10.1016/j.caeai.2023.100180 DOI: https://doi.org/10.1016/j.caeai.2023.100180
Himawan, R., Kusmiatun, A., and Suyata, P. (2024). Developing Project-Based Learning EBook “Critical and Creative Reading” to Improve Students’ Critical Thinking Skills. Jurnal Kependidikan, 10(1), Article 392404. https://doi.org/10.33394/jk.v10i1.10316 DOI: https://doi.org/10.33394/jk.v10i1.10316
Hutson, J., and Plate, D. (2023). Human–AI Collaboration for Smart Education: Reframing Applied Learning to Support Metacognition. IntechOpen. https://doi.org/10.5772/intechopen.1001832 DOI: https://doi.org/10.5772/intechopen.1001832
Kenedy, R. A. (2024, December). The Challenges of Critical Thinking in the Era of Artificial Intelligence. European Journal of Multidisciplinary Studies, 9(2), 19–36. https://doi.org/10.26417/hfdngd20 DOI: https://doi.org/10.26417/hfdngd20
Khobragade, P., Dhankar, P. K., Titarmare, A., Dhone, M., Thakur, S., and Saraf, P. (2024). Quantum-Enhanced AI Robotics for Sustainable Agriculture: Pioneering Autonomous Systems in Precision Farming. In 2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA) (1–7). IEEE. https://doi.org/10.1109/ICAIQSA64000.2024.10882412 DOI: https://doi.org/10.1109/ICAIQSA64000.2024.10882412
Knox, J., Wang, Y., and Gallagher, M. (2019). Introduction: AI, Inclusion, and “Everyone Learning Everything.” In Perspectives on Rethinking and Reforming Education (1–13). https://doi.org/10.1007/978-981-13-8161-4_1 DOI: https://doi.org/10.1007/978-981-13-8161-4_1
Manzano-León, A., et al. (2021). Between Level Up and Game Over: A Systematic Literature Review of Gamification in Education. Sustainability, 13(4), Article 2247. https://doi.org/10.3390/su13042247 DOI: https://doi.org/10.3390/su13042247
McGonigal, J. (2011). Reality is Broken: Why Games Make us Better and How They Can Change the World. Penguin Press.
Memarian, B., and Doleck, T. (2024). Teaching and Learning Artificial Intelligence: Insights from the Literature. Education and Information Technologies, 29(16), 21523–21546. https://doi.org/10.1007/s10639-024-12679-y DOI: https://doi.org/10.1007/s10639-024-12679-y
Meng, N., Dhimolea, T. K., and Ali, Z. (2022). AI-Enhanced Education: Teaching and Learning Reimagined. In M. V. Albert, L. Lin, M. J. Spector, and S. Dunn (Eds.), Educational Communications and Technology: Issues and Innovations (xx–xx). Springer. https://doi.org/10.1007/978-3-030-84729-6_7 DOI: https://doi.org/10.1007/978-3-030-84729-6_7
Narvaez-Teran, and Martínez Elizalde, L. (2024). A Card Game Proposal for Approaching Regular Languages. Revista Emprendimiento Científico Tecnológico, 5(1). https://doi.org/10.54798/CJOK6586
Squalli Houssaini, M., Aboutajeddine, A., Toughrai, I., and Ibrahimi, A. (2024). Development of a Design Course for Medical Curriculum: Using Design Thinking as an Instructional Design Method Empowered by Constructive Alignment and Generative AI. Thinking Skills and Creativity, 52, Article 101491. https://doi.org/10.1016/j.tsc.2024.101491 DOI: https://doi.org/10.1016/j.tsc.2024.101491
Sun, L., and Zhou, L. (2024, December). Does Generative Artificial Intelligence Improve the Academic Achievement of College Students? A Meta-Analysis. Journal of Educational Computing Research, 62(7), 1676–1713. https://doi.org/10.1177/07356331241277937 DOI: https://doi.org/10.1177/07356331241277937
Wahid, A., Kamin, Y., Syamsuri, A. S., Paida, A., Latief, S. A., and Muhsin, A. (2024). The Effect of Critical Literacy-Based Literature Reading on Critical Thinking Skills: A Study on First-Year University Students. International Journal of Literacies, 32(1), 53–73. https://doi.org/10.18848/2327-0136/CGP/v32i01/53-73 DOI: https://doi.org/10.18848/2327-0136/CGP/v32i01/53-73
Welbers, K., et al. (2019, March). Gamification as a Tool for Engaging Student Learning: A Field Experiment with a Gamified Ae-Learning and Digital Media, 16(2), 92–109. https://doi.org/10.1177/2042753018818342 DOI: https://doi.org/10.1177/2042753018818342
Wu, C. H., Chen, C. C., Wang, S. M., and Hou, H. T. (2018, July). The Design and Evaluation of a Gamification Teaching Activity Using Board Game and QR Code for Organic Chemical Structure and Functional Groups Learning. In Proceedings of the IIAI International Conference on Learning Technologies and Learning Environments (IIAI-LTLE 2018). DOI: https://doi.org/10.1109/IIAI-AAI.2018.00190
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Copyright (c) 2025 Anu Alex, Pooja Srishti, P Thilagavathi, Dr. Praveen Priyaranjan Nayak ; Sorabh Sharma; Minal Keote
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