An AI-driven tools assessment framework for english teachers using the Fuzzy Delphi algorithm and deep learning

English literature and linguistics have long served as foundational disciplines in humanities education, cultivating critical analysis, linguistic proficiency, and cultural interpretation. Conventional teaching methods struggle to meet diverse learner needs, ensure consistent engagement, and provide personalized academic feedback. To improve learning with the help of modern techniques, this study proposes a comprehensive, multi-technique Artificial Intelligence (AI)-driven tools assessment framework aimed at enhancing English pedagogy through the integration of advanced artificial intelligence tools. The research work includes adaptation of a mixed-methods research design incorporating classroom case studies, in-depth interviews, and analysis of students' documents to evaluate their learnings. The framework employed statistical techniques to validate significant relationships among engagement, tool usage, and learning clarity. Key evaluation criteria is captured using the Fuzzy Delphi Technique which identifies high-importance attributes such as AI usage, usability, and analytical quality. Moreover, eXplainable AI (XAI) techniques including LIME and SHAP applied to enhance model transparency, offering both global and local interpretability of outcomes. To predict pedagogical effectiveness, a deep learning Bi-LSTM model was trained, achieving 90% accuracy, 92% precision, 93% recall, and 92% F1-score across key performance metrics for the usage analysis of AI-based tools.