China Leads in Effective AI Use for Research, Elsevier Finds

China Leads in Effective AI Use for Research, Elsevier Report Finds

A new joint report from Elsevier and the Chinese Association of Science of Science and Science & Technology Policy Research shows Chinese scientists are ahead in confidence and practical use of AI across the research workflow.

Based on a survey of 3,200+ researchers in 113 countries, AI adoption is rising fast: 58 percent of researchers now use AI tools, up from 37 percent last year. The standout insight: China isn't just using AI more-it's using it more effectively in areas that matter day to day, like data analysis, manuscript drafting, and proposal writing.

Key findings from the survey

• Choice and flexibility: 68% of Chinese respondents say AI tools give them more options, versus 29% in the US and 26% in the UK.
• Confidence in impact: 64% in China say AI can empower scientific research, compared with 25% in the US and 24% in the UK.
• Time savings: 79% in China say AI saves research time, versus 54% in the US and 57% in the UK.
• Quality gains: 60% in China say AI enhances research quality, compared with 22% in the US and 17% in the UK.
• Faster discovery: 49% in China believe AI can accelerate discoveries, versus 30% in the US and 26% in the UK.

Chinese researchers also show a pragmatic mindset. They focus on concrete applications and measurable social impact, with deeper use of AI in analysis, writing, and planning work-where time and clarity usually bottleneck progress.

Why this matters for your lab

AI is shifting from optional tool to standard practice. If your team isn't using it with intention-clear tasks, documented workflows, quality checks-you're leaving speed and precision on the table.

The report also echoes a reality many labs feel: time is tight and funding is uncertain. That's exactly where structured AI use pays off.

Practical moves to close the gap

• Map AI to bottlenecks: data cleaning, figure generation, literature synthesis, R&R responses, proposal sections (needs, aims, methods, budget notes).
• Standardize workflows: define which tools are approved, prompts/templates to use, and when a human review is required. Write it down.
• Tighten quality control: require provenance notes, link claims to sources, track model versions, and keep a human in the loop for interpretation.
• Upskill your team: give short, targeted training for analysts, authors, and PIs. If you need a structured path, see AI courses by job role.
• Protect ethics and IP: set rules on data sensitivity, disclosure in manuscripts, and what content can or can't be model-generated.
• Measure ROI: log hours saved, error rates, reviewer feedback, and acceptance outcomes. Keep what works, drop what doesn't.

Headwinds to plan around

• Only 45% of researchers say they have enough time for research-protect focused blocks and automate routine tasks.
• Just 33% expect funding to grow in the next 2-3 years-use AI to sharpen proposals and reduce admin overhead.
• 68% report higher pressure to publish-use AI to compress drafting cycles, but keep method and result integrity non-negotiable.
• Peer review still anchors quality: 74% value it. Treat AI as an assistant, not an author, and document its role.

Bottom line

The labs making the fastest progress aren't doing more-they're removing friction. The data shows Chinese researchers are doing this with AI at a deeper, more practical level. If you systematize how your team uses these tools, you'll gain time, clarity, and stronger outputs without adding headcount.