Engaging students with effective learning materials continues to be a significant challenge in programming education. Analogies are commonly used to simplify complex topics, enabling learners to relate unfamiliar concepts to familiar ones. Additionally, visual representations of these analogies can enhance engagement and improve the overall learning experience. This work presents a prototype of a novel AI tool that generates analogy-based explanations and corresponding video animations for programming education. The tool leverages Large Language Models (LLMs) for analogy generation and a structured animation workflow for visualization. This poster invites discussion on the effectiveness of AI-generated educational content and its implications for programming education.

The rise of AI tools is reshaping computer science education, shifting the focus from coding skills to teaching students how to effectively use these technologies. Understanding students' mental models and fostering computational and metacognitive skills are now essential, as over-reliance on AI can weaken critical thinking. This panel explores how a human-centered approach can balance these challenges, sharing strategies to optimize learning while addressing the risks of cognitive offloading in an AI-driven world.