Published October 20, 2026 | Version v2

UK Design Patent: Computer Display Screen with Adaptive Learning Interface, Eman Zaki Mousa Mohamed Elsherif

  • 1. Specific Education Faculty, Minia University
  • 2. Educational Technology Professor, Dean of the Specific Education Faculty, Minia University, Minia 61519, Egypt,

Contributors

Hosting institution:

  • 1. 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

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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.