Intelligent Educational Environments: Recent Trends, Modeling, and Applications
Source abstract
Abstract
This paper aims to summarize recent trends in the field of intelligent education and build on the research findings to propose a conceptual model of an intelligent educational environment (IEE) along with possible applications. A bibliometric analysis explores the recent trends to obtain insights into the current research directions. The concept of an IEE is introduced and correlated to other popular concepts in the educational field. Relevant user requirements for the IEE are defined and discussed. They serve as the basis for creating the conceptual model as a UML class diagram. The proposed model gives an overall picture of the relationships among the essential elements of the IEE and provides insights into its future development. Two possible applications are outlined: personalized teaching and learning and the assessment process. Any particular application of the model to build an IEE must consider all specific user requirements and add relevant details. The proposed conceptual model can be used to enhance the teaching–learning process and improve learners’ performance.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Online Learning and Analytics · Engineering Education and Technology · Robotics and Automated Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, 1113 Sofia, Bulgaria
Technical College of Sofia, Technical University of Sofia, 1756 Sofia, Bulgaria
Department of Informatics, Faculty of Applied Mathematics and Informatics, Technical University of Sofia, 1756 Sofia, Bulgaria
Funding
Bulgarian National Science Fund
References (57)
Show source-provided references
1. The ontology of next generation smart classrooms10.1007/978-3-319-19875-0_1 ↗
2. Learning analytics tasks as services in smart classrooms10.1007/s10209-017-0525-0 ↗
3. Teaching and learning with technology: Effectiveness of ICT integration in schools10.21890/ijres.23596 ↗
4. Research on the English teaching and autonomous learning based on multimedia platform and smart classroom system
5. Internet of Things for smart classrooms
6. Ashwin, M., Kumar, E.S., Naidu, R.C.A., and Ramamoorthy, R. (2023, January 23–25). IoT based Innovative Teaching Learning Using Smart Class Rooms. Proceedings of the International Conference on Sustainable Computing and Data Communication Systems (ICSCDS), Erode, India.10.1109/ICSCDS56580.2023.10104589 ↗
7. Huang, L.-S., Su, J.-Y., and Pao, T.-L. (2019). A context aware smart classroom architecture for smart campuses. Appl. Sci., 9.10.3390/app9091837 ↗
8. Smart educational tools and learning management systems: Supportive framework10.1007/s12008-020-00695-4 ↗
9. A research framework of smart education10.1186/s40561-016-0026-2 ↗
10. Terzieva, V., Todorova, K., and Ivanova, T. (2021, January 28–29). Conceptual Model of Intelligent Educational System and the Need of Big Data Analytics. Proceedings of the International Conference Big Data, Knowledge and Control Systems Engineering (BdKCSE), Sofia, Bulgaria.10.1109/BdKCSE53180.2021.9627252 ↗
11. Liu, Y., Xie, Z., and Chen, J. (2016, January 13–15). An Intelligent Learning System for Supporting Interactive Learning Through Student Engagement Study. Proceedings of the 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), Changsha, China.10.1109/FSKD.2016.7603245 ↗
12. Technologies and tools for creating adaptive e-learning content
13. Personalized adaptive learning: An emerging pedagogical approach enabled by a smart learning environment10.1186/s40561-019-0089-y ↗
14. A Systematic review of process modelling methods and its application for personalised adaptive learning systems
15. Ontology based e-learning framework: A personalized, adaptive and context aware model10.1007/s11042-019-08125-8 ↗
16. Intelligent tutoring systems10.1007/978-3-319-49625-2_9 ↗
17. Ambient intelligence-based smart classroom model10.1080/10494820.2019.1652836 ↗
18. Standards for smart education—Towards a development framework10.1186/s40561-018-0052-3 ↗
19. Definition, framework and research issues of smart learning environments—A context-aware ubiquitous learning perspective10.1186/s40561-014-0004-5 ↗
20. Evolution is not enough: Revolutionizing current learning environments to smart learning environments10.1007/s40593-016-0108-x ↗
21. The role of Internet of Things in smart education10.1016/j.ifacol.2022.08.057 ↗
22. Reidenberg, J.R. (2015). The transparent citizen. Loyola Univ. Chic. Law J., 47, Available online: https://heinonline.org/HOL/LandingPage?handle=hein.journals/luclj47&div=14&id=&page=.
23. Joy, J., and Raj, N.S. (2019, January 2–5). An ontology model for content recommendation in personalized learning environment. Proceedings of the Second International Conference on Data Science, E-learning and Information Systems, Dubai, United Arab Emirates.10.1145/3368691.3368700 ↗
24. Menolli, A.L.A., Reinehr, S., and Malucelli, A. (2012, January 1–5). Ontology for Organizational Learning Objects Based on LOM Standard. Proceedings of the 2012 XXXVIII Conferencia Latinoamericana En Informatica (CLEI), Medellin, Colombia.10.1109/CLEI.2012.6427194 ↗
25. A new competency ontology for learning environments personalization10.1186/s40561-021-00160-z ↗
26. A systematic review of ontology use in E-Learning recommender system10.1016/j.caeai.2022.100047 ↗
27. Pleskach, V., Tkachenko, K., Tkachenko, O., and Tkachenko, O. (2023, January 20–21). Using Ontologies and Knowledge Graphs to Individualize in E-Learning System. Proceedings of the Information Technology and Implementation Workshops 2023, Kyiv, Ukraine. Available online: https://ceur-ws.org/Vol-3646/Paper_11.pdf.
28. A framework of an intelligent education system for higher education based on deep learning10.3991/ijet.v16i07.22123 ↗
29. Machine learning-based personalized learning path decision-making method on intelligent education platforms10.3991/ijim.v18i16.51009 ↗
30. Design and Optimization of Personalized Education System Based on Intelligent Algorithms10.1016/j.procs.2024.09.063 ↗
31. Personalized education resource recommendation method based on deep learning in intelligent educational robot environments
32. Design of resource matching model of intelligent education system based on machine learning
33. Artificial intelligence driving innovation in higher education management and student training mechanisms
34. Chen, Y. (2021, January 21–23). Research on data mining of intelligent education evaluation from the perspective of the knowledge graph. Proceedings of the 4th International Symposium on Big Data and Applied Statistics (ISBDAS 2021), Dali, China.
35. A novel Internet of Things-supported intelligent education management system implemented via collaboration of knowledge and data10.3934/mbe.2023600 ↗
36. Application of IoT Android voice assistant based on sensor networks in higher education network mode10.1016/j.measen.2024.101091 ↗
37. Pense, C., Tektaş, M., Kanj, H., and Ali, N. (2023). The use of virtual reality technology in intelligent transportation systems education. Sustainability, 15.10.20944/preprints202212.0322.v1 ↗
38. Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., and Gerjets, P. (2023). ChatGPT in education: Global reactions to AI innovations. Sci. Rep., 13.10.1038/s41598-023-42227-6 ↗
39. Artificial intelligence in intelligent tutoring systems toward sustainable education: A systematic review10.1186/s40561-023-00260-y ↗
40. Educational models for cognition: Methodology of modeling intellectual skills for intelligent tutoring systems10.1016/j.cogsys.2024.101261 ↗
41. Fernández-Herrero, J. (2024). Evaluating recent advances in affective intelligent tutoring systems: A scoping review of educational impacts and future prospects. Educ. Sci., 14.10.3390/educsci14080839 ↗
42. Smart MOOC integrated with intelligent tutoring: A system architecture and framework model proposal10.1016/j.caeai.2022.100092 ↗
43. An intelligent tutoring system architecture based on fuzzy neural network (FNN) for special education of learning disabled learners10.1007/s10639-021-10713-x ↗
44. An ontology-driven architecture for intelligent tutoring systems with an application to learning object recommendation
45. Fundamentals of the design and the operation of an intelligent tutoring system for the learning of the arithmetical and algebraic way of solving word problems10.1016/j.compedu.2012.11.020 ↗
46. Towards automatic tutoring of Math Word Problems in Intelligent Tutoring Systems10.1109/ACCESS.2023.3290478 ↗
47. Towards a design of an intelligent educational system10.1016/j.ifacol.2021.10.474 ↗
48. Basic concepts and taxonomy of dependable and secure computing10.1109/TDSC.2004.2 ↗
49. Randell, B., Laprie, J.C., Kopetz, H., and Littlewood, B. (1995). Dependability—Its attributes, impairments and means. Predictably Dependable Computing Systems, Springer.10.1007/978-3-642-79789-7 ↗
50. Dubrova, E. (2013). Fault-Tolerant Design, Springer Science+Business Media.10.1007/978-1-4614-2113-9 ↗
51. Rhodes-Ousley, M. (2013). Information Security: The Complete Reference, McGraw-Hill. [2nd ed.].
52. (2025, February 20). EU General Data Protection Regulation. Available online: https://gdpr.eu.
53. (2025, February 20). Directive on Measures for a High Common Level of Cybersecurity Across the Union (NIS2 Directive). Available online: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32022L2555.
54. Djambazova, E., Terzieva, V., Ilchev, S., and Gaidarski, I. (2024, January 29–31). Aspects of Dependability and Security in Integrated Intelligent Educational Environments. Proceedings of the 12th IEEE International Conference on Intelligent Systems—IS’24, Varna, Bulgaria.10.1109/IS61756.2024.10705223 ↗
55. Graph-based cognitive diagnosis for intelligent tutoring systems10.1016/j.knosys.2022.109547 ↗
56. Soto-Forero, D., Ackermann, S., Betbeder, M.L., and Henriet, J. (2024, January 1–4). The Intelligent Tutoring System AI-VT with Case-Based Reasoning and Real Time Recommender Models. Proceedings of the International Conference on Case-Based Reasoning Research and Development, Merida, Mexico.10.1007/978-3-031-63646-2_13 ↗
57. Freedman, R., Zhou, Y., Glass, M., Kim, J.H., and Evens, M.W. (1998, January 1–4). Using Rule Induction to Assist in Rule Construction for a Natural-Language Based Intelligent Tutoring System. Proceedings of the Twentieth Annual Conference of the Cognitive Science Society, Madison, WI, USA. Available online: http://cs.iit.edu/~circsim/documents/rfcog98.pdf.