Task Distribution and Engineering Practices for AI Assistants in Scientific Research

I. Identifying the Real Landing Points of AI in Research Through Session Tags

Recent aggregated research focusing on scientific scenarios analyzed approximately 207,000 AI research sessions. It offers an observation that engineering teams should note: the frequent use of general AI assistants in research does not replace the entire research workflow. Instead, it focuses on text-based, iterative, and verifiable tasks such as data analysis, writing and communication, and code assistance. The study covered a two-month window, comprising about 129,000 conversational sessions and 78,000 code assistance sessions. To protect privacy, the system retained only categorical tags like discipline, sub-field, and task type. It did not extract or store specific content such as research data, hypotheses, experimental methods, preliminary findings, or unpublished papers. Results were presented in an aggregated format.

This methodology itself is valuable: observing AI usage structures through task and discipline tags without touching sensitive research content allows for identifying macro trends while reducing data leakage and compliance risks. For enterprises, universities, and research institutions, this means that when building research AI capabilities, priority should be given to establishing an observation system characterized by “minimal collection, categorical aggregation, and auditability,” rather than initially collecting vast amounts of raw content.

II. Key Findings: Data Analysis and Writing as the Main Battlefield, Code Tools Favoring Quantitative Fields

Results indicate a distinct “dumbbell-shaped” characteristic in task distribution. Data analysis accounted for 33.7% of general AI assistant research sessions and 48.1% of code assistance research sessions. Writing and communication tasks made up 32% of general AI assistant research sessions. Correspondingly, researchers typically spend about 27.6% of their time on data analysis. This suggests that the frequent landing points of AI are highly correlated with actual time consumption: tasks that occupy the most time and can be structured in expression easily become the first entry point for AI assistance.

Disciplinary distribution also showed differences. Researchers in computer science, mathematics, and statistics use AI assistants more frequently, while medical health and physical sciences relying on lab work use them relatively less. In research sessions, computer science accounted for 11.9%, higher than engineering’s 8.4% and medicine’s 7.7%. Even when adjusting for researcher population, the usage proportion in computer science, mathematics, and statistics still exceeded their respective share of researchers. The use of code assistance tools is more skewed towards quantitative research fields: computer science researchers make up 4.3% of global researchers but generated 32.7% of code assistance research sessions. AI and machine learning researchers account for 1.2% globally but represented 5% of research sessions and 15.2% of code assistance research sessions. Usage rates in arts, humanities, and some social sciences were lower.

These data should not be simply interpreted as “certain disciplines do not need AI.” A more reasonable explanation is that inter-disciplinary volume differences are simultaneously influenced by researcher base, task-AI adaptability, tool form, and verification costs. Tasks in computer science, math, and statistics are more easily expressed via code, formulas, and structured data. Their outputs are easier to close the loop through testing, reproduction, and numerical verification. Experimental sciences, however, require on-site equipment, samples, ethical approvals, and causal verification, leaving AI mostly at the periphery for literature, draft proposals, and data processing.

III. Behind Disciplinary Differences: Researcher Base × Task Adaptability × Tool Form

First, the researcher base is the foundational variable. The more researchers a discipline has, the higher the absolute session volume naturally is. Therefore, when observing usage intensity, the researcher population must be factored in rather than just looking at total volume. Even after adjusting for population, the study showed higher usage proportions in computer science, math, and statistics, confirming the existence of task adaptability factors.

Second, task adaptability determines the penetration speed. Data analysis and writing have characteristics of text-based input/output, segmentable iteration, and manual reviewability. Researchers can break tasks into small steps like “cleaning instructions, statistical criteria, chart interpretations, paragraph rewrites, and reviewer responses,” where AI provides drafts or checks at each step. Conversely, collecting data often involves instruments, sensors, samples, and field processes, while generating research hypotheses requires domain judgment, causal reasoning, and subsequent experimental validation. AI’s direct replacement space is limited here.

Third, tool form affects usage depth. General conversation suits open-ended analysis and writing, while code assistance tools suit quantitative modeling, data processing scripts, statistical analysis, and testable code generation. The study showed that code assistance sessions account for a higher proportion in quantitative fields, aligning with the principle of “matching tool capabilities with task structure.” In engineering implementation, a general entry point should not be forced upon all disciplines; routing should be based on task type and risk level.

IV. Engineering Implementation Framework: Task Layering, Entry Routing, Data Governance, and Evaluation Loop

1. Task Layering: Whitelist First, Then Greylist

It is recommended to categorize research AI tasks into three types: low-risk tasks (e.g., writing polishing, formatting, drafting literature summaries, code commenting); medium-risk tasks (e.g., data analysis script generation, statistical method suggestions, chart interpretation); and high-risk tasks (e.g., research hypothesis generation, experimental protocol design, clinical or ethics-related judgments). Low-risk tasks can be opened first. Medium-risk tasks require manual review and traceable records. High-risk tasks should be human-led, with AI providing only auxiliary information.

2. Entry Routing: Matching Tool Forms by Task and Discipline

The routing strategy can be described with schematic pseudocode, with the core being “sensitivity first, task matching, human fallback”:

profile = {task_type, domain, sensitivity, reproducibility}
if sensitivity == "high":
    route = "human_review_first"
elif task_type in {"data_analysis", "writing"}:
    route = "assistant_draft_with_review"
elif task_type in {"code", "quant_modeling"} and domain in {"cs", "math", "stats"}:
    route = "code_assist"
else:
    route = "human_led"

This strategy does not depend on specific product APIs but expresses general engineering principles: high-risk tasks get manual gatekeeping first; data analysis and writing follow “draft + review”; code and quantitative modeling use code assistance in adapted disciplines; other tasks remain human-led. Teams can add prompt template versions, output citation requirements, and reviewer fields on this basis.

3. Data Governance: Keep Only Tags, No Sensitive Original Text

The study’s practice of “retaining only discipline, sub-field, and task tags, without saving research data, hypotheses, experimental methods, and unpublished papers” is worth emulating. Engineering should implement minimal collection, desensitization, aggregation, access control, and auditing. Logs should only record metadata like task type, discipline, duration, adoption status, and rework status; sensitive content should not enter long-term logs. On general cloud platforms like Baidu AI Cloud, observation pipelines can be built using object storage, logging services, and access control features, but it is essential to ensure proper permission isolation and key management.

4. Evaluation Loop: Look Beyond Call Volumes

Evaluation metrics should cover efficiency, quality, and risk: task adoption rate, manual rework rate, average time saved, error discovery rate, output traceability ratio, sensitive data incidents, and user satisfaction. For data analysis tasks, focus should also be on statistical criteria consistency and result reproducibility; for writing tasks, on factual consistency and citation integrity; for code assistance, on test pass rates and security scan results.

V. Practical Recommendations and Risk Control

First, pilot by discipline and task rather than rolling out a one-size-fits-all approach. Computer science and math/statistics can prioritize code assistance and quantitative analysis pilots; experimental sciences can start with literature review, data cleaning scripts, and writing communication. Second, establish a manual review system where medium and high-risk outputs must be confirmed by domain personnel. Third, versionize prompt templates and evaluation rules to avoid untraceable “different answers to the same question” issues. Fourth, audit logs and permissions regularly to ensure only aggregated tags and necessary metadata are retained. Fifth, position AI as an “accelerator in the research workflow” rather than the ultimate responsible party for research conclusions.

Overall, the high-value areas for AI assistants in scientific scenarios are clear: data analysis and writing communication are the current main battlefields, while code assistance penetrates deeper in quantitative fields. What truly determines the implementation effect is not a single demo, but an engineering system comprising task layering, data governance, manual review, and an evaluation loop. Teams should design entry points based on real task time consumption and risk levels, allowing AI to create stable returns in verifiable and auditable processes before gradually expanding to more complex research workflows.