Artificial Intelligence Fuzzy Approaches in History Education: A Detailed Analysis of Digital Archives, Decision Making System and Machine Learning Algorithms

This paper will be an empirical examination of artificial intelligent fuzzy solutions in history education based on using machine learning, deep learning, and processing the digital archive methodology. The study suggests a cohesive framework that combines fuzzy inferences systems with ML and DL algorithms to overcome uncertainty in the historical data, automate the process of classifying the archives, and improve decision-support systems in the learning process of students. A large dataset of textual documents, digitized images, and student performance indicators were assessed with the help of such models as Random Forest, Gradient Boosting, CNNs, BiLSTM, and Transformer designs. Also, Mamdani, Sugeno and neuro-fuzzy models of fuzzy logic were evaluated based on interpretability and ambiguity. The findings indicate that hybrid Fuzzy-DL models have a better level of accuracy with the transparency needed in an educational setup. The results show a great enhancement in the personalized learning recommendation, metadata quality prediction, and the ability to retrieve an archive. Altogether, this paper shows that the integration of AI-based fuzzy systems can be used to transform history education and enhance the data-driven pedagogical decision-making.

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