AI for Science (AI4S) is reshaping the paradigm of scientific inquiry by integrating scientific priors with data−driven methods. Within this broader shift, AI−driven scientific discovery has become a question of national strategic importance. This paper examines its conceptual boundaries, methodological landscape, and human−AI collaboration mechanisms, and considers its potential implications for the research ecosystem. We first distinguish the two complementary pathways of AI4S—knowledge embedding and knowledge discovery—and the closed loop they form, and argue that the core contributions of knowledge discovery lie in automating inductive reasoning and explicit knowledge formalization. This contribution, however, is concentrated at the level of phenomena and empirical laws; the leap to constructive theories still depends on human mechanistic interpretation and validation. We then propose the Representation–Search–Evaluation (RSE) framework, showing that knowledge−discovery methods differ in the openness of their representation space, with search and evaluation co−evolving around that openness. On this basis, we characterize the core responsibilities that remain with humans in human−AI collaboration: defining epistemic goals, providing semantic constraints, and leading value−based adjudication. Finally, we discuss how knowledge discovery may reshape the research ecosystem. In fields where data density, automation, and validation conditions are relatively mature, the role of scientists may shift from task executors toward a kind of “metacognitive hub” and research organizations may become flatter within groups while growing more differentiated across groups. Such differentiation can be turned into a higher−quality basis for cross−group collaboration through platformized assets, standardized interfaces, and effective validation governance. We emphasize that these ecosystem−level claims are conditional tendencies rather than established facts. The paper’s innovation is to articulate, within the RSE framework, both the methodological trajectory of knowledge discovery and its associated human−AI collaboration mechanism, and on that basis to offer an outlook on the possible changes in research collaboration and organization.