Published October 20, 2026
| Version v2
Patent
Open
UK Design Patent: Computer Display Screen with Adaptive Learning Interface, Eman Zaki Mousa Mohamed Elsherif
Authors/Creators
- 1. Specific Education Faculty, Minia University
- 2. Educational Technology Professor, Dean of the Specific Education Faculty, Minia University, Minia 61519, Egypt,
Description
The present invention relates generally to the field of artificial intelligence in education, and more specifically to neuro-symbolic AI systems, cognitive digital twins, federated learning architectures, and multi-agent systems for adaptive personalized learning. The invention finds application in intelligent tutoring systems, learning management systems, educational data mining, and privacy-preserving learning analytics.
Files
PATENT, UK Design Patent Government Patent Office, Dr Eman, Egypt.pdf
Additional details
Additional titles
- Alternative title
- NS-CDT-FMA via Display Screen with Adaptive Learning Interface
- Alternative title
- NEURO-SYMBOLIC COGNITIVE DIGITAL TWIN WITH FEDERATED MULTI-AGENT ORCHESTRATION FOR ADAPTIVE EDUCATION (NS-CDT-FMA)
Software
- Programming language
- Python , MATLAB , Altium Designer , AIDL , Smarty , 4D , DirectX 3D File
References
- [1] R. S. Baker and K. Yacef, "The state of educational data mining in 2009: A review and future visions," J. Educ. Data Mining, vol. 1, no. 1, pp. 3-17, 2009. [2] A. T. Corbett and J. R. Anderson, "Knowledge tracing: Modeling the acquisition of procedural knowledge," User Model. User-Adapt. Interact., vol. 4, no. 4, pp. 253-278, 1995. [3] C. Piech et al., "Deep knowledge tracing," in Proc. Adv. Neural Inf. Process. Syst., 2015, pp. 505-513. [4] J. Zhang, X. Shi, I. King, and D. Y. Yeung, "Dynamic key-value memory networks for knowledge tracing," in Proc. Web Conf., 2017, pp. 765-774. [5] S. Pandey and J. Srivastava, "RKT: Relation-aware self-attention for knowledge tracing," in Proc. ACM Conf. Inf. Knowl. Manag., 2020, pp. 1205-1214. [6] A. Ghosh, N. Heffernan, and A. S. Lan, "Context-aware attentive knowledge tracing," in Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Mining, 2020, pp. 2330-2339. [7] A. Singla et al., "Towards automated assessment and feedback in programming education," IEEE Trans. Learn. Technol., vol. 15, no. 4, pp. 456-469, 2022. [8] Z. Liu et al., "Large language model-based multi-agent systems in education," Lecture Notes Educ. Technol., Springer, 2026. [9] N. N. P. et al., "Examining intelligent tutoring systems and their AI underpinnings in the design of the future of learning," IEEE Trans. Learn. Technol., 2025. [10] M. M. et al., "A systematic review of literature on the effectiveness of intelligent tutoring systems in STEM," in Proc. IEEE Conf. Educ., 2025. [11] S. D. et al., "Educational data mining for personalized learning: A systematic literature review," J. Educ. Data Mining, 2025. [12] Z. Papanikolaou and A. Economides, "Learning analytics and educational data mining in practice," Educ. Technol. Soc., vol. 17, no. 4, pp. 4-15, 2014. [13] T. Singh, S. Dutta, S. Vyas, and A. Rocha, "Explainable AI for education: Recent trends and challenges," Springer, 2024. [14] "Explainable AI in education: Integrating educational domain knowledge into the deep learning model for improved student performance prediction," Sci. Rep., 2026. [15] Q. Ai et al., "Digital twin technology for building immersive learning environment for English education," Int. J. Continuing Eng. Educ. Life-Long Learn., vol. 36, no. 9, pp. 174-193, 2026. [16] "Precision cognitive digital twins in personalized education," BBN Times, 2024. [17] "Neuro-symbolic artificial intelligence-based assessment system," US Patent Application, 2026. [18] "Proficiency assessment and instructional feedback using explainable artificial intelligence," US20220164713A1, 2022. [19] "Federated personalized learning method and system based on knowledge-free distillation," CN Patent, 2024. [20] "Privacy-preserving learning and analytics of a shared embedding space," US20240346367, 2024. [21] H. Khosravi et al., "Explainable artificial intelligence in education," Comput. Educ. Artif. Intell., vol. 3, 100074, 2022. [22] Bhuyan, B. P., Ramdane-Cherif, A., Tomar, R., & Singh, T. P. (2024). Neuro-symbolic artificial intelligence: a survey. Neural Computing and Applications, 36(21), 12809–12844. https://doi.org/10.1007/s00521-024-09960-z [23] B. McMahan et al., "Communication-efficient learning of deep networks from decentralized data," in Proc. AISTATS, 2017, pp. 1273-1282. [24] M. Abadi et al., "Deep learning with differential privacy," in Proc. ACM SIGSAC Conf. Comput. Commun. Security, 2016, pp. 308-318. [25] F. Oliehoek and C. Amato, A Concise Introduction to Decentralized POMDPs. Springer, 2016. [26] Gligorea, I., Cioca, M., Oancea, R., Gorski, A., Gorski, H., & Tudorache, P. (2023). Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature review. Education Sciences, 13(12), 1216. https://doi.org/10.3390/educsci13121216 [27] Wen, J., Zhang, Z., Lan, Y., Cui, Z., Cai, J., & Zhang, W. (2022). A survey on federated learning: challenges and applications. International Journal of Machine Learning and Cybernetics, 14(2), 513–535. https://doi.org/10.1007/s13042-022-01647-y [28] A. N. et al., "Intelligent tutoring systems from symbolic AI to LLMs: A critical review," IEEE Trans. Learn. Technol., 2026. [29] A. M. et al., "Adaptive Multi-Agent Learning Systems," in Proc. ACM Conf. Learn.@Scale, 2026.