Today, artificial intelligence has become a core element in research; it is now an inseparable component of the modern research process; ChatGPT has even become a co-author for many scholars. Despite the glamour with which the involvement of AI in research is portrayed, this involvement is not risk-free, since reliance on automated systems represents a threat to human intellectual abilities and skills, with long-term negative consequences for the human mind’s ability to seek deep knowledge and creativity.

The gradual increase in AI use in research may lead to the replacement of human intelligence with artificial intelligence in conducting research projects over time.

Relying on AI in research could gradually cause the next generation to lose awareness of all the stages and requirements of the scientific research process. When AI platforms automate every task needed for research, researchers won’t need to actively engage with the entire process; instead, they can simply ask AI to handle it. Thus, the risk for the next generation is a likely loss of our cognitive skills and memory over time. As AI takes on more components of the research process, it could decline in critical thinking and problem-solving abilities. The simplicity of the AI-powered research process, therefore, might lead to a new generation of researchers who are capable of delivering quick results but lack the intellectual depth, independence, and awareness needed to maintain meaningful research.

When researchers rely on AI’s automated responses, they risk becoming passive users and failing to develop analytical and questioning skills that high-quality research demands. Nor’ain Abdul Rashid states in his article: “Over-dependence on automation may undermine the reflective dimensions of the qualitative research process.” This highlights a key risk: research isn’t just about generating results but about understanding and thoughtfully engaging with the process, learning new insights from all its stages. It is also important to note that AI, which provides fake references and information, is likely to mislead researchers about the stages and components of research, leading to fake results.

If AI automates all research tasks that used to foster our intellectual growth, the human mind could become weaker, and research skills might gradually fade away. We are likely to lose those abilities and skills that differentiate us from all other creatures, including the angels, since we come to life with a purpose and a mission. Maintaining the essence of effective research requires human intellect and reflection to guide the process. The human mind should never be replaced by a machine; otherwise, research will lose its value.

Hajer al Balushi

The writer is a student of Sultan Qaboos University

The Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) is at the forefront of a global shift in how science gets done—one driven by artificial intelligence, automation, and powerful data systems. By integrating these tools, researchers are transforming the speed and scale of discovery across disciplines, from energy to materials science to particle physics.

This integrated approach is not just advancing research at Berkeley Lab—it’s strengthening the nation’s scientific enterprise. By pioneering AI-enabled discovery platforms and sharing them across the research community, Berkeley Lab is helping the U.S. compete in the global race for innovation, delivering the tools and insights needed to solve some of the world’s most pressing challenges.

From accelerating materials discovery to optimizing beamlines and more, here are four ways Berkeley Lab is using AI to make research faster, smarter, and more impactful.

Automating Discovery: AI and Robotics for Materials Innovation

At the heart of materials science is a time-consuming process: formulating, synthesizing, and testing thousands of potential compounds. AI is helping Berkeley Lab speed that up—dramatically.

A-Lab
At Berkeley Lab’s automated materials facility, A-Lab, AI algorithms propose new compounds, and robots prepare and test them. This tight loop between machine intelligence and automation drastically shortens the time it takes to validate materials for use in technologies like batteries and electronics.

Autobot
Exploratory tools like Autobot, a robotic system at the Molecular Foundry, are being used to investigate new materials for applications ranging from energy to quantum computing, making lab work faster and more flexible.