AI-Driven Pedagogical Word Recommendation Systems Transforming English as a Second Language Vocabulary Learning Effectiveness

AI-powered word recommendation systems have revolutionized AI-Driven Pedagogical Word Recommendation Systems Transforming English as a Second Language Vocabulary Learning Effectiveness (ESL) vocabulary acquisition. Traditional approaches limit customized learning with static word sets and generic training. AI-driven instructional systems assess student interactions, competency, and vocabulary retention to recommend smart words. The objective is an AI-powered, context-aware system that optimizes vocabulary learning via real-time feedback, varied challenge levels, and interactive interaction. Improved retention, contextual word usage, and personalized learning are aims. The objective is a dynamic, data-driven learning approach that refines vocabulary recommendations depending on student performance and participation. AI-driven solutions improve word selection, engagement, and vocabulary growth in ESL teaching, closing linguistic gaps and improving long-term language capacity. The AI-Driven ESL Vocabulary Learning Effectiveness dataset was analyzed three times. The first sample included 5 students and 5 tests. On exams 1-5, Student 1 averages 57-92. Student 2 averages 60-88. Student 3 averages 60-89, 4 51-93. Student 5 averages 51-87. Different models sampled 5 steps in the second occurrence. Model 1 stage value: 77.28-85.3. Model 2 stages are 73.49-89.63. Model 3 phases are 71.16-85.19. Model 4 stages are 71.63-94.14. The Model 5 phases are 72.44-87.11. The third sample included 5 students from 5 weeks. From week 1 to 5, Student 1 averages 67-94. Student 2 averages 61-99. Student 3 averages 69-93. Student 4 averages 67-95. Student 5 averages 73-99.