This work presents TOP, a practical stepwise framework for implementing AI tools based on learning preferences to enhance metacognition, motivation, and effective study practices. It integrates VARK assessment, reflective review of learning strategies, and classification of AI technologies according to learner preferences. TOP structures AI prompting through Task Verbs, Output specifications, and purposeful prompting aligned with Bloom’s taxonomy. By combining adaptive task design and personalized content delivery, TOP supports differentiated cognitive engagement, self-regulation, sustained motivation, and deeper learning. The TOP framework provides students and educators with actionable guidance for responsible, pedagogically grounded, and effective AI-supported instruction. #AI-in-education #Adaptive-Learning #Learning-preferences-and-study-skills
