The use of AI agents not only promises greater research efficiency, but also signals a transformation in how social science knowledge production is organized. Image generated by AI 

With the development of artificial intelligence (AI), generative tools have made conversational interfaces a gateway to a growing range of research assistance. They can help researchers organize ideas, review academic papers, complete code, and catch typographical errors. AI agents now take this capability a step further. Powered by foundation models, they can autonomously break a research objective into a sequence of tasks, moving dynamically among literature databases, webpages, spreadsheets, statistical software, coding environments, and writing tools. Processes that once required researchers or research assistants to complete one by one—from initial literature screening, data organization, interview transcription, and data cleaning to preliminary coding, programming, results presentation, and manuscript drafting—can therefore be reorganized. This not only promises greater research efficiency, but also signals a transformation in how social science knowledge production is organized.

‘Students performing tasks’ or ‘hands-on mentors’

AI agents can support social science research in two main ways. The first is through “acceleration.” Well-trained scholars can delegate repetitive operational tasks—such as retrieving information, cleaning data, running programs, and generating tables—to AI agents, freeing themselves to concentrate on setting the research direction and ensuring quality rather than personally handling every tedious step.

In this division of labor, AI agents resemble “students who do the work.” The underlying mechanism is the delegation of repetitive labor: Humans set the direction and ensure quality, while AI agents carry out the work. The longer the research cycle and the more complex the workflow, the more pronounced the efficiency gains become.

The second is through “empowerment.” In this scenario, users may be relatively unfamiliar with the task at hand. With the assistance of AI agents, disciplinary knowledge, research paradigms, and programming languages that once demanded prolonged study can be learned and applied more quickly. In this role, AI agents function as “hands-on mentors,” helping users complete tasks while also providing guidance and instruction.

Put simply, “acceleration” resembles a mentor directing an AI student, whereas “empowerment” resembles a student learning from an AI mentor. The decisive distinction is whether researchers can independently evaluate the agents’ outputs and adjust the direction of their research accordingly.

Toward high-quality disciplinary development

Intelligent driving technology offers a useful analogy. Despite continuing advances, the industry’s mainstream approach remains Level 2 (L2) driver assistance, under which human drivers must continuously monitor the system and be ready to take control. AI agents in scientific research can be understood in much the same way. They can assist with literature searches, drafting, and language refinement, but they do not relieve researchers of their responsibilities. Scholars must still verify facts, check citations, and remain accountable for the authenticity, accuracy, and completeness of their findings.

Social science research should harness AI agents to enhance efficiency without neglecting research safeguards. If scholars use these systems uncritically to produce papers, the field could be flooded with AI-generated work, potentially accelerating the spread of misinformation. Accordingly, core processes—including formulating research questions, developing concepts, and structuring arguments—cannot be outsourced. Every link connecting literature, facts, data, models, and inference must be carefully verified. Tasks such as preliminary information screening, formatting, code development, and drafting can, however, be undertaken collaboratively with AI. Fluency and apparent completeness are not guarantees of correctness. Only when the boundaries among human leadership, verification, and AI-assisted collaboration are clearly defined can human–AI division of labor enhance rather than undermine research quality.

The researchers of the future will therefore need more than technical proficiency. They must be able to formulate questions, deploy AI agents, audit research processes, recognize methodological boundaries and social contexts, and address concerns such as privacy protection and academic integrity. Academic communities should likewise establish clear norms governing the disclosure of AI use, documentation of research processes, reproducibility of results, and attribution of responsibility.

Ultimately, AI agents should do more than accelerate the production of academic papers. They should help researchers understand society more deeply and respond more seriously to real-world issues. The true promise of the AI-agent era lies not in replacing social scientists with machines, but in freeing researchers from repetitive work so they can devote more time and intellectual energy to engaging with reality, understanding others, and answering more consequential questions.

Shen Minghong is a research fellow from the Center for Applied Social and Economic Research at NYU Shanghai.