VEGA: Adaptive Learning in Astronomy through Symbiotic Artificial Intelligence

The increasing availability of astronomical data and public interest in Near-Earth Objects (NEOs) has highlightedthe need for accessible, adaptive, and educational tools that support exploration and learning in this complexscientific domain. This paper presents VEGA (Visual Exploration and Graphical Analysis of Asteroids), aninteractive system that leverages Symbiotic Artificial Intelligence (SAI) to enable a personalized and transparentlearning experience in astronomy. Grounded in SAI principles—such as explainability, fairness, and human-inthe-loop interaction—the proposed system integrates generative AI, reinforcement learning, and user modelingto deliver tailored educational content related to asteroids and NEOs. VEGA allows non-expert and expert usersto explore real-time NASA data, generate customized learning materials and quizzes via Large Language Models(LLMs), and iteratively improve content through a dynamic versioning mechanism. The paper introduces amodular framework for adaptive learning based on five co-evolving components, illustrating how SAI can fostertrust and cognitive augmentation in science education. This approach not only enhances public awareness ofastronomical phenomena but also serves as a testbed for the application of human-centered AI in low-risk yethigh-impact domains.