Enhancing researcher competence in AI for Science paradigm shift and corresponding strategies

Against the backdrop of the paradigm shift triggered by AI for Science (AI4S) and intensifying global technological competition, systematically enhancing researchers’ AI4S competence is essential for developing new quality productive forces and achieving scientific and technological self-reliance. In practice, AI4S manifests in both specialized and general forms, imposing universal, multi-dimensional competence requirements on researchers. AI4S competence comprises four core dimensions: computational thinking for scientific problems, human-AI interaction and verification, interdisciplinary collaboration, and ethical awareness and responsibility. Future talent development should transform fragmented self-learning into organized competence training, shifting from teaching technical tools to cultivating computational thinking, building a tiered support system for both general and specialized needs, incentivizing the production of reusable expert knowledge, and strengthening ethics education and governance.