JDS Journal of Data Science 1683-86021680-743X1680-743X School of Statistics, Renmin University of China JDS1214 10.6339/25-JDS1214 Data Science Reviews AI for Science: Opportunities, Challenges, and Future Directions FuValerievalerie.r.fu@gmail.com1∗ Independent Researcher, Carmel, IN 46032, U.S.A. Email: valerie.r.fu@gmail.com. 2026212026241106124158202512122025 2026 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.2026 Open access article under the CC BY license.

Artificial intelligence (AI) has lately emerged as a transformative force in scientific discovery, with skills in accelerating knowledge synthesis, automating experimentation, and enhancing interdisciplinary collaboration. As research challenges—ranging from climate change to rare disease treatments—grow more and more complex, the rapid evolution of AI calls for a comprehensive examination of its current and future roles. Despite recent breakthroughs, the field remains fragmented, due to the lack of a unified framework to understand AI’s progression in science and its implications for data science, in particular. To address this gap, this review provides an analysis on AI for science, and also introduces a novel three-phase framework—Keplerian (data-driven pattern recognition), Edisonian (autonomous experimentation), and Einsteinian (foundational innovation)—to conceptualize AI’s evolving role in science. Additionally, we discuss the ethical, environmental, and data privacy challenges that go alongside AI’s integration in science, emphasizing the need for sustainable and responsible development. This review outlines how AI may transform the scientific methods and to help researchers harness AI’s potential to drive scientific innovation.

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