Analysis of the Effectiveness of Artificial Intelligence Methods for Data Processing and Interpretation in Educational Research

Abstract

The rapid development of artificial intelligence (AI) technologies has significantly impacted various sectors, including education. In educational research, AI methods are increasingly used for data processing and interpretation, unlocking the potential to transform how data is analyzed and insights are derived. However, the effectiveness of these methods in accurately processing and interpreting educational data remains a critical area of investigation. The relevance of this study lies in the growing dependence on AI methods in education, necessitating a thorough examination of their effectiveness in producing reliable and valid results. The purpose of the study is to analyze the effectiveness of different AI methods for processing and interpreting educational data. Methods: analysis of scientific literature, scientific abstraction, comparison, generalization, and systematization. The study’s results indicate that AI methods exhibit varying effectiveness across different metrics. Machine learning algorithms demonstrate high accuracy and scalability, making them suitable for working with large datasets, yet they may encounter challenges with interpretability. Deep learning methods, while powerful in processing complex data patterns, require substantial computational resources and may face issues with reproducibility. Natural Language Processing (NLP) is promising for interpreting qualitative data but is limited by the quality of training data and the need for complex models. The findings underscore the importance of selecting the appropriate AI method based on specific research objectives and data characteristics. Conclusions. Thus, while AI methods offer significant advantages in processing and interpreting educational data, their effectiveness depends on various factors, including data type, research goals, and resource availability. Future research should focus on refining these methods to overcome existing limitations and exploring their potential to transform educational research. Continuous evaluation and improvement of AI methods will be crucial to maximizing their impact on the field of education.