Artificial intelligence is rapidly becoming a default academic tool for students, yet most large language models were not designed with pedagogical clarity, grade-level appropriateness, or cognitive simplicity in mind. As a result, students often receive explanations that are verbose, poorly structured, or mismatched to their reading level—paradoxically increasing confusion rather than improving understanding. My project addresses this gap by designing and evaluating SimplrAI, an AI-based academic explanation system that integrates structured prompt templates, grade-level controls, and readability scoring tools. This project tests the hypothesis that explanations engineered with intentional structure—definitions, stepwise reasoning, examples, and applications—will significantly improve perceived clarity and learning outcomes compared to default AI responses. By combining computer science, cognitive load theory, and user-experience design, SimplrAI contributes a replicable methodology for evaluating AI explanations in real educational settings.