AI in scientific research: Are machines the future of discovery?
AI and ML have significantly improved research capabilities by automating routine tasks and enhancing data analysis. Scientists are increasingly using AI in areas such as laboratory automation, computational modeling, automated experiment planning, and data interpretation. AI enables more efficient experimentation, reduces errors, and enhances the reproducibility of research findings.

The integration of artificial intelligence (AI) and machine learning (ML) into scientific research is revolutionizing the way experiments are conducted, data is analyzed, and discoveries are made. Automation technologies are improving efficiency and reproducibility, but they also challenge traditional research methodologies. A critical question arises: how do researchers perceive the impact of AI-driven automation on their work?
A recent study titled “Researchers’ Perceptions of Automating Scientific Research” by David M. Douglas, published in AI & Soc (2025), investigates the perspectives of researchers from diverse fields, including automation engineering, computational design, conservation decision-making, materials science, and synthetic biology, regarding AI/ML integration into their work. The study highlights the benefits and limitations of laboratory automation, AI-powered experiment design, and data analysis, while also addressing concerns about explainability, reliability, and the importance of maintaining human oversight in scientific discovery.
Automating science: The benefits and challenges
The Expanding Role of AI in Research
AI and ML have significantly improved research capabilities by automating routine tasks and enhancing data analysis. Scientists are increasingly using AI in areas such as laboratory automation, computational modeling, automated experiment planning, and data interpretation. AI enables more efficient experimentation, reduces errors, and enhances the reproducibility of research findings. In laboratory settings, robotic automation allows for continuous experiments without human intervention, while computational modeling and simulations help predict outcomes and test hypotheses before real-world implementation. AI-powered data analysis accelerates pattern recognition, classification, and anomaly detection, providing insights that might otherwise be overlooked.
Despite these advancements, the integration of AI into scientific research presents challenges. Researchers recognize AI's ability to speed up processes and optimize workflows, but many are concerned about potential risks such as loss of control, explainability issues, and over-reliance on automation. The accessibility of AI-driven research is also a concern, as adopting these technologies requires significant investment in infrastructure and training, making them less feasible for smaller institutions.
How different research fields view automation
Laboratory Automation: Increasing Efficiency and Safety
Researchers in automation engineering and synthetic biology see laboratory automation as an effective means of improving efficiency and reducing human error. Robotic lab assistants can operate continuously, enabling parallel experimentation while reducing scientists’ exposure to hazardous materials. AI-driven automation enhances reproducibility, ensuring that experiments are conducted under consistent conditions.
However, despite these advantages, researchers emphasize the need for human oversight in laboratory automation. While robots excel at handling repetitive tasks, human intervention remains necessary for interpreting unexpected results, troubleshooting system errors, and making creative adjustments to experiments. Scientists also stress that automation should be seen as an enhancement rather than a replacement for human expertise.
Computational Design and Simulations: Bridging Theory and Experimentation
Scientists in computational design leverage AI to generate and refine models of physical structures, such as wind turbine blades, molecular compounds, and complex materials. AI simulations allow researchers to test multiple designs under virtual conditions, reducing the need for costly real-world experiments. These computational tools enable faster innovation and a more data-driven approach to research and development.
Despite these benefits, computational researchers highlight the limitations of AI models, particularly in representing real-world complexities. Many AI-based simulations simplify intricate systems, which can lead to discrepancies between predicted and actual performance. To mitigate this issue, scientists emphasize the necessity of experimental verification, ensuring that AI-generated models are validated against real-world data before implementation.
AI in Conservation Decision-Making: Balancing Performance and Explainability
AI is playing a growing role in environmental science, where it is used to predict ecosystem changes, evaluate conservation strategies, and optimize resource management. Researchers in this field acknowledge AI's ability to process large-scale environmental data, providing valuable insights into climate change, wildlife populations, and land use patterns. However, they emphasize the importance of explainability in AI models, particularly when making policy or conservation decisions that impact ecosystems and communities.
The study highlights a trade-off between model performance and interpretability—while complex AI models often yield highly accurate predictions, their black-box nature makes them difficult to interpret and justify. Scientists stress that for AI-driven conservation efforts to be widely accepted and implemented, models must be transparent, explainable, and accessible to policymakers and stakeholders.
Materials Science: Enhancing Research Through Automation
Materials scientists are increasingly adopting AI-driven automation for experiment design, material discovery, and process optimization. AI helps in predicting material properties, accelerating the development of new compounds, and improving experimental reproducibility. Automated workflows allow scientists to rapidly test material compositions, significantly reducing time and resource costs compared to traditional trial-and-error methods.
Despite these advantages, researchers highlight the challenge of integrating AI tools into existing workflows. Many AI-driven platforms require specialized knowledge, meaning researchers must acquire technical skills in programming, data science, and algorithm design. As AI becomes more embedded in materials science, interdisciplinary collaboration between domain experts and AI specialists will be crucial for maximizing its potential.
Explainability and the need for human oversight
Explainability Matters - But Not in Every Case
The study reveals that the importance of AI explainability varies depending on its application. In fields like laboratory automation, where AI performs predefined tasks, explainability is less critical. However, in fields like computational modeling and conservation decision-making, where AI is used to generate insights and guide decision-making, explainability is essential. Researchers stress that AI-generated findings must be interpretable and verifiable, ensuring that scientific conclusions remain credible and reproducible.
Keeping a Human-in-the-Loop: A Non-Negotiable Factor
Across all disciplines, researchers emphasize the importance of maintaining human involvement in AI-driven research. While automation enhances efficiency, it cannot replace human intuition, creativity, or scientific judgment. Scientists stress that human oversight is essential for risk management, ensuring that AI-generated findings are accurate, ethical, and scientifically valid. Additionally, researchers highlight that AI lacks the ability to formulate new scientific questions, meaning that fundamental scientific inquiry must remain human-led.
- FIRST PUBLISHED IN:
- Devdiscourse