The rapid rise of Artificial Intelligence (AI) tools prompts educators to revisit how learning theories inform instruction and assessment. While AI supports personalization, adaptive feedback, and automation, its alignment with established learning theories remains unclear. This study examines how AI tools engage cognitive, metacognitive, and affective dimensions using an open dataset of AI-powered educational tools. Using topic modeling techniques and cluster analysis, we identify thematic categories and their alignment with Bloom’s Taxonomy, the Two-Level Model of Metacognition, and Control-Value Theory. Findings show a strong emphasis on lower-order processes (e.g., Remembering and Understanding), with fewer designed for higher-order thinking. Some tools enable self-monitoring and reflection, but few support strategic learning. In the affective domain, AI enhance motivation via personalization but lacks emotional adaptation. These results highlight the need for theory-informed AI design to promote deeper, self-regulated, and emotionally responsive learning.