Relevance of the topic
Modern education has become increasingly diverse, and students differ in their needs, interests, and abilities. This creates a need for an individualized approach to teaching, which enhances its effectiveness. In recent years, artificial intelligence has been increasingly integrated into the field of education, providing new opportunities for personalizing the educational process. Therefore, studying and utilizing artificial intelligence for the personalization of education is a relevant and in-demand topic.
Research goal and objectives
The aim of this research is to develop a model for personalizing the learning process using artificial intelligence. To achieve this goal, the following objectives were set:
1. Present the role of artificial intelligence in education.
2. Study existing approaches to personalizing the learning process using artificial intelligence.
3. Consider machine learning algorithms used for personalizing the learning process.
4. Develop a model for personalizing the learning process based on artificial intelligence, taking into account the individual needs and characteristics of each student.
The role of artificial intelligence in education
Artificial intelligence (AI) has become an indispensable tool in many areas of human activity, including education. The use of AI in education allows for the creation of personalized educational programs, the analysis of student data, and the provision of individualized assignments and approaches to learning. As Siemens and Siemens (2010) note, the use of artificial intelligence in education allows for “personalizing education based on the different needs and abilities of each student” [6].
Personalization of the learning process
Personalization of the learning process is an important approach that allows for the individualization of each student’s learning process, taking into account their needs, interests, abilities, and level of preparation. Personalized education contributes to increased student motivation, improved learning outcomes, and better adaptation to the demands of the modern job market. According to Schwartz and Goldman-Segall (2008), “personalized education allows students to develop at their own pace, focus on their strengths, and develop missing skills” [5].
Existing approaches to personalizing education using artificial intelligence
The use of artificial intelligence in personalizing education has tremendous potential for optimizing the educational process and improving student performance.
One successful example of a model for personalizing the educational process using artificial intelligence is the Personalized Adaptive Digital Interface (PADI) model. In the work of Mitrovic, Lawrance, and Vinnikov (2007), a model is presented that uses machine learning algorithms to create personalized educational programs [4]. PADI takes into account the individual needs and abilities of each student and adapts the learning process by offering suitable assignments and materials.
Another successful model is the Intelligent Tutoring System (ITS) model. In the work of Brusilovsky, Eklund, and Schwarz (1998), a model is presented that uses artificial intelligence to analyze data and provide real-time personalized support and feedback. ITS helps each student achieve better results by adapting educational materials and learning methods to their needs and learning style [1].
Machine learning algorithms used for educational personalization
Machine learning algorithms allow the system to adapt the educational process to the individual needs and abilities of each student. The use of machine learning algorithms, as Hooshiar, Stanujkic, and Warick (2020) suggest, enables the education system to delve into the individual needs of each learner and provide a personalized approach to learning.
Here are some of the most common machine learning algorithms:
1) Collaborative filtering: This algorithm, according to Verbert (2012), is based on the analysis of student preferences and performance indicators, and then provides recommendations based on the similarity between students and their preferences. For example, it can be used to suggest suitable tasks or materials for study, based on the successes of other students with similar interests and abilities [7].
2) Classification: Classification algorithms, as suggested by Zhang, Huang, and Chen (2020), can be used to determine the level of knowledge and abilities of students based on their responses to assignments or tests. This allows for the provision of personalized content and tasks that are appropriate to their level and needs. For example, if a student shows good results in mathematics, the system can suggest more challenging tasks or additional materials on that topic [8].
3) Clustering: Clustering algorithms, as emphasized by Zhou, Oliver, and Steyn (2013), allow for grouping students with similar characteristics for a more precise personalization of the educational process. For example, students with similar interests or learning styles can be grouped together to be offered the same tasks or materials [9].
4) Regression: Regression algorithms can be used to predict future student success based on their previous progress. The predictions from these algorithms can be useful in optimizing personalized educational plans and offering optimal tasks and materials for each learner.
Machine learning algorithms allow for the analysis of student data, the discovery of hidden patterns, and the prediction of their needs and achievements in learning. As Kotsiantis, Zaharakis, and Pintelas (2007) point out, machine learning algorithms can be used to develop systems that can offer individual tasks, materials, and recommendations based on each student’s data [3].
Methodology for personalizing the educational process using artificial intelligence
To achieve the goal of developing a model for personalizing the educational process using artificial intelligence, the following step-by-step methodology can be used:
1) Data collection and analysis: First, it is necessary to collect student data, including information about their progress, preferences, task responses, and characteristics. Then, analyze this data to uncover correlations and patterns that can be used for personalizing the educational process.
2) Choice and application of machine learning algorithms: Based on the data analysis, suitable machine learning algorithms are selected for personalizing the educational process. Depending on the goals and available data, collaborative filtering, classification, clustering, and regression algorithms can be used.
3) Development of personalized modules and materials: Based on the results of the machine learning algorithms, personalized educational modules and materials are developed. These materials can be offered to students for further learning according to their needs and abilities.
4) Evaluation of effectiveness and model improvement: After implementing the model, it is necessary to evaluate its effectiveness. To do this, compare the learning outcomes and performance of students using the personalized approach with those using traditional methods.
Conclusions
This article has examined the current topic of personalizing the educational process using artificial intelligence. The modern education system faces challenges in individualizing learning, and artificial intelligence offers numerous possibilities and tools to address these problems.
As a result of the research, it has been found that the use of artificial intelligence significantly improves the learning process and achieves higher results. Personalization of education becomes a reality.
References
- Brusilovsky, P., Eklund, J., & Schwarz, E. (1998). Web-based education for all: A tool for developing adaptive courseware. Computer Networks and ISDN Systems, 30(1-7), 291-300.
- Hooshiar, K., Stanujkic, D., & Warick, B. (2020). Personalized Education System Using Artificial Intelligence. In Proceedings of the 5th International Scientific Conference on Lifelong Education and Leadership for All (LELA 2020) (pp. 261-269). Atlantis Press.
- Kotsiantis, S., Zaharakis, I., & Pintelas, P. (2007). Machine learning: A review of classification and combining techniques. Artificial Intelligence Review, 26(3), 159-190.
- Mitrovic, A., Lawrance, N., & Vinnikov, L. (2007). Rapid development of ITSs with a user-oriented authoring tool. User Modeling and User-Adapted Interaction, 17(1-2), 33-68.
- Schwartz, D., Goldman-Segall, B. «Master minds: Expanding the frontiers of mind science». Teachers College Press. 2008.
- Siemens, N., Siemens, G. «Personal Learning Environments: Challenging the dominant design of educational systems». Journal of e-Learning and Knowledge Society. 2010.
- Verbert, K. (2012). Learning analytics for personalized e-learning. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (LAK’12) (pp. 100-101). ACM.
- Zhang, R., Huang, Y., & Chen, N. S. (2020). Personalized E-Learning Recommendation Systems: A Review. IEEE Transactions on Education, 63(3), 224-235.
- Zhou, W., Oliver, R., & Steyn, N. P. (2013). Cluster Analysis: An approach to ensure the personalized e-learning. In Proceedings of the 2013 International Conference on E-Learning and E-Technologies in Education (pp. 150-156). IEEE.
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