At the Intelligent Manufacturing Research Institute of Hefei University of Technology, staff are debugging an AI chemical management robot.

The Chinese Academy of Sciences has released the “Rock 100” model system, which aims to create a cluster of large models across eight disciplines.

Currently, AI is deeply involved in scientific research, from predicting protein structures to discovering new materials, showcasing its immense potential as a “universal engine” for accelerating science.

How is the Path of Scientific Discovery Changing?

Traditional research starts with “hypothesis-validation,” but now the path is shifting to “data-discovery of patterns-intelligent generation-closed-loop iteration.”

Wang Xijun, a distinguished professor at the University of Science and Technology of China, explains that in traditional research, questions are often based on experience and intuition. Now, for some disciplines, AI can actively discover patterns in vast amounts of data, leading to a new paradigm in scientific discovery. AI can even design desired materials according to specific goals.

For instance, in my research on framework materials, these can be created through combinations of different metal nodes, organic ligands, and connection methods, resulting in an astronomical number of structures. Here, AI provides a breakthrough. Machine learning can quickly predict material performance, saving significant trial-and-error costs. Additionally, AI can extract patterns from data, transforming past intuitive approaches into computable, transferable models, making material design more rational.

On this basis, generative AI can further push research from “filtering the known” to “creating the unknown”—directly generating new material structures beyond the training data, achieving “reverse design” around target performance. This means AI is not only accelerating problem-solving but also expanding the boundaries of the questions themselves.

Thus, the role of AI in research is evolving: from a computational tool to an analytical assistant, and now to a “research partner” that can participate in and even drive autonomous exploration.

Of course, AI will not replace scientists. Understanding key scientific issues and mechanisms still relies on human judgment and insight. Humans are responsible for posing questions and guiding direction, while AI searches for possible answers within vast data and complex spaces. This collaboration will provide a more solid and expansive space for future research innovation.

Is Research Innovation Efficiency Improving?

AI excels at handling tasks with clear answers that require extensive repetitive calculations.

Mo Bofeng, a professor at the Oracle Bone Research Center of Capital Normal University, states that AI has significantly improved research efficiency in literature review, experimental design, and data analysis. Even when dealing with oracle bones from over 3,000 years ago, AI can play a substantial role. Tasks like oracle bone splicing and restoration, which relied heavily on the experience of a few experts, now have new solutions through AI.

To truly leverage AI, it is crucial to identify the right points of integration. As oracle bones are archaeological texts, the core research goal is to restore the material and information of the characters. AI is particularly adept at tasks requiring clear answers and extensive repetitive calculations. It can identify subtle features that humans might miss, such as the curvature of breaks and the angles of brush strokes, providing key clues for splicing and restoration.

However, AI is not omnipotent. The total number of oracle bones exceeds 160,000, with over a million characters, which seems substantial but is still insufficient for training large AI models. Therefore, human experts are still needed for deep semantic judgments. A more effective approach is human-machine collaboration: using AI as a speed-up tool while experts review and correct its results.

Currently, splicing and restoration are just the beginning of AI’s assistance in oracle bone research. As technology advances, tasks like classification, aggregation, and translation of oracle bones will gradually break through. Future researchers will need not only professional knowledge but also enhanced data processing skills and the ability to leverage technology to amplify their research advantages.

Will AI Affect Research Judgment?

While lowering some research barriers, risks such as false citations and erroneous reasoning deserve attention.

Yang Yaodong, a researcher at Peking University’s Institute of Artificial Intelligence, notes that AI is not just helping researchers write code, review literature, and create charts; it is changing the entire research process. The linear flow from hypothesis to experiment to result analysis is gradually evolving into a closed-loop system of human-machine collaboration, model prediction, automated experimentation, and feedback iteration.

This change brings several benefits. First, efficiency is greatly enhanced, especially in fields like materials, pharmaceuticals, and energy, where there are numerous candidate solutions that traditional methods struggle to exhaustively explore. AI can quickly filter options, freeing researchers from repetitive trial and error to focus on key issues. Second, it promotes interdisciplinary integration, as a scientific problem often involves physics, chemistry, biology, engineering, and computation, with AI establishing connections between multi-source data. Third, it lowers some research barriers; with open-source models and tool platforms, small teams can tackle large projects.

However, it is important to note that AI does not equate to true scientific understanding. Scientific research must not only be accurate in predictions but also answer “why.” If the model is a black box, the data sources unclear, and the experimental processes non-reproducible, AI’s conclusions could introduce new risks. Particularly, generative AI raises concerns about false citations, erroneous reasoning, low-quality papers, data leaks, and unclear academic responsibilities, all of which could impact research norms.

A deeper issue is that research judgment cannot be replaced by tool logic. AI excels at finding optimal solutions within existing data, but determining what problems are worth investigating and which results hold scientific significance still requires human oversight.

How to Achieve Effective Resource Integration?

Connecting scientists, AI engineers, and industry forces to shift innovation from isolated breakthroughs to systematic acceleration.

Wu Libo, assistant president of Fudan University and chairman of the Shanghai Institute of Science Intelligence, states that scientific intelligence is transitioning from a “technology-centric” 1.0 era to a “scientist-centric” 2.0 era. This new era aims to make more scientists the protagonists, allowing AI to truly permeate the entire research process. The Shanghai Institute of Science Intelligence and Fudan University have jointly created the Xinghe Qizhi Scientific Intelligence Open Platform to respond to this shift.

The primary role of the platform is to lower the barriers for scientists to use AI. It has built a comprehensive infrastructure covering data, models, computing power, experiments, intelligent agents, and collaborative communities around real research paths. Currently, the Xinghe Qizhi Scientific Intelligence Open Platform has gathered over 400 scientific models and tools, 22PB of high-value data, and 500 million pieces of literature and patents, allowing scientists to utilize cutting-edge models for research without delving into technical details.

We have also launched a research intelligent agent system based on “Dasheng,” which can understand scientific problems and assist in completing the entire process from literature analysis to hypothesis generation and experimental validation. Recently, “Dasheng” introduced a custom laboratory function, enabling scientists to build their own toolchains based on their research directions.

The second role of the platform is to promote interdisciplinary, cross-regional, and cross-field integration. In traditional research, data, models, and methods from different disciplines often do not communicate, making collaboration difficult. The Xinghe Qizhi Scientific Intelligence Open Platform facilitates the sharing, reuse, and combination of results from different fields through a unified model repository and data infrastructure.

More fundamentally, the platform serves as a hub for the scientific intelligence ecosystem. It connects scientists, AI engineers, and industry forces, allowing data and methods to flow and be reused within the system, shifting innovation from isolated breakthroughs to systematic acceleration, providing sustainable institutional support for AI-driven research paradigm transformation.

How to Build and Utilize Intelligent Platforms Effectively?

Encouraging open sharing to bridge the gap between industry and research.

Liu Tieyan, president of Beijing Zhongguancun College and chairman of the Zhongguancun Artificial Intelligence Research Institute, emphasizes that having many platforms does not equate to them being usable, effective, or genuinely useful. Last year, Zhongguancun College surveyed over 30 materials enterprises in Beijing and identified 100 “bottleneck” issues. The research found that with current mainstream scientific intelligence technologies, only 20% of these problems are likely to be solved. The remaining issues are temporarily unsolvable due to low levels of digitalization in enterprises, data deficiencies, and insufficient algorithm accuracy. This realization highlights that “AI empowering research” cannot be merely a slogan or a platform; infrastructure shortcomings, technical limitations, and the industry-research gap are real challenges.

Regarding the open sharing of scientific intelligent agents and tools, this may seem like a technical issue on the surface, but it is fundamentally about the lack of motivation to bridge the gap. Why would an organization open its data and platform? If this question lacks a systematic answer, “open sharing” will remain at the level of advocacy.

To break the deadlock, it is suggested to approach from three aspects: first, vigorously promote industrial digitalization, allowing true industry demands to guide scientific research directions. Research should not remain in a “research first, then transform” model; industry feedback should be integrated into the research cycle to fill the “last mile.” Second, establish incentive mechanisms for open sharing, recognizing sharing as a form of research contribution, such as making it a condition for project initiation and completion, and creating a citation-like measurement system. Third, public entities should take the lead in building foundational infrastructure for interdisciplinary collaboration. Users of scientific intelligent agents and tools are highly specialized and dispersed across various disciplines. Due to insufficient market size, national strategic investments could be considered first, gradually introducing market mechanisms.

In summary, bridging data and intelligent agent interfaces is a surface issue, restructuring incentive mechanisms is a mid-level issue, and ensuring that research genuinely addresses national needs and real industry problems is fundamental.

A Controversial Call for AI as the First Author

The call for papers stating “the first author must be AI” has sparked heated discussions in academia.

In 2025, East China Normal University issued a call for papers that has stirred significant debate in the academic community. This social experiment, which requires AI to be the primary author of research papers, serves as a near “extreme test” to confront the question: as AI deeply engages in knowledge production, what are the ethical boundaries of AI-assisted writing, and where should the bottom line of academic research be drawn?

“We hope to use this approach to study the public’s acceptance of AI writing, its technical feasibility, scientific quality, and academic norms,” said Yuan Zhenguo, a lifelong professor at East China Normal University and the initiator of the experiment.

Following the announcement, controversy ensued. Supporters view it as a “breaking the ice” experiment for academic norms in the AI era, while opponents worry it represents a “proactive retreat” of humans in research. Zhang Zhi, director of the Intelligent Education Laboratory at East China Normal University, stated, “Currently, the penetration rate of AI in papers is high, and many students use AI to assist in writing but do not dare to disclose it. This ‘underground state’ is a greater violation of academic norms. Rather than turning a blind eye, we should respond directly.”

The experiment collected 820 research papers with “AI as the first author.” Reviewers found that AI demonstrated good capabilities in topic planning, outline generation, data analysis, literature skimming, and logical structuring. However, limitations are equally significant: large models excel at “fragment reorganization and cross-domain transfer” within existing data but lack genuine creativity and value judgment.

“Based on this underlying logic, the reasonable application scenarios for AI in research writing should still focus on non-core aspects,” Zhang Zhi stated. In paper writing, humans should take on the roles of problem posers, tool selectors, instruction designers, and quality reviewers.

“The bottom line for AI usage essentially revolves around academic integrity and responsibility attribution. The originality threshold cannot be breached, and the transparency threshold must be upheld—all AI usage must be fully disclosed, specifying the tool’s name, application scope, and human review process in the paper. Furthermore, the responsibility attribution threshold must remain clear; regardless of the extent of AI involvement, human authors must bear full responsibility for the final results,” Zhang Zhi emphasized.

The significance of this experiment may not lie in reaching conclusions but in fostering a consensus: as human-AI collaboration in paper writing becomes a new phenomenon, only by effectively utilizing AI and upholding academic integrity can we preserve the true value of academic research.

“Using AI to assist in paper writing does not mean relinquishing subjectivity but rather exploring a new division of labor in research, allowing AI to handle the breadth of data while humans maintain the depth of thought and the warmth of values,” said Chu Xiaobo, vice president of Peking University.