Artificial intelligence (AI) is growing rapidly, with applications spread across industries such as healthcare, finance, education and entertainment. One of the most exciting areas of AI is scientific research. The ability of artificial intelligence to process large amounts of data, identify complex patterns and make predictions is accelerating the speed of scientific discovery. This raises the interesting question: Can AI think outside the box and produce truly novel ideas like human scientists? To explore this, we must look at how AI is currently used in scientific discovery and whether it can truly produce original ideas.
AI’s role in scientific discovery continues to grow
AI has made great progress in various scientific fields, including drug discovery, genomics, materials science, climate research and astronomy. By processing large-scale data sets that humans cannot handle, AI plays an important role in identifying potential drug candidates, modeling climate change and even proposing new theories about the universe.
For example, researchers at MIT used AI to discover a new antibiotic within a few days, targeting resistant bacteria to existing drugs. In biology, DeepMind’s Alphafold solves the problem of protein folding, predicting that 3D protein structure is crucial for drug development. In materials science, AI models such as GNOME predict millions of new crystals that can redefine technologies such as cells and solar cells. AI also helps physics by proposing new methods to model physical phenomena and astronomy, by discovering exoplanets and gravity lenses. In climate science, AI enhances climate predictions and helps model extreme weather events.
Can artificial intelligence think outside the box?
While AI’s contribution to scientific discovery is undeniable, the question remains: Can it really think out of the box? Human scientific advances often rely on intuition, creativity, and courage to challenge existing paradigms. These breakthroughs usually come from scientists willing to transcend traditional wisdom.
However, AI is data-driven. It analyzes patterns and predicts results based on the information provided, but it does not have the imagination of human abstract ideas. In this sense, AI’s creativity is different from human creativity. AI runs within constraints of its data and algorithms, which limits its ability to perform truly creative, out-of-the-box thinking.
That is to say, the situation is more complicated. AI shows that it can generate new assumptions, propose innovative solutions, and even challenge knowledge in certain fields. For example, machine learning models have been used to create novel chemical compounds and design materials that humans have never considered before. In some cases, these findings have led to breakthroughs, which are difficult for human researchers to achieve on their own.
The arguments that support AI creativity
Proponents believe that AI demonstrates creativity by producing ideas that are not obvious to human researchers. Alphafold, for example, uses a novel deep learning structure to solve the protein folding challenge that has eschewed scientists for decades. Similarly, Google’s Gemini 2.0-driven AI is also used to create original assumptions and research proposals, allowing scientists to bridge the gap between different scientific fields. A study from the University of Chicago shows that AI can produce the “alien” hypothesis—innovative ideas that humans may not think of, thus expanding the boundaries of scientific exploration. These examples show that AI has the potential to think about frames by proposing new ideas.
Disputes against AI’s creativity
Critics argue that AI is fundamentally limited because it relies on existing knowledge and data sets. Its work is more like filling gaps in the data than questioning existing assumptions. According to critics, AI’s creativity is limited by trained data to prevent its truly groundbreaking discoveries.
Thomas Wolf, a well-known AI expert, asserted that, like Einstein’s thoughts, true innovation requires new questions and challenging traditional wisdom. Large Language Models (LLM) and other AI systems, despite extensive training, have not been proven to produce truly novel insights. Therefore, AI is more regarded as an effective tool for learning than a true thinker who can destroy the established scientific paradigm.
Furthermore, AI lacks human qualities of intuition, emotion and contingency that often drive creative breakthroughs. AI relies on predetermined algorithms in logic and system processes. According to entrepreneurs, this algorithm approach is very different from the unpredictable spontaneous nature of human creativity. A ScienceDirect research paper also argues that AI-generated creativity may appear innovative, but does not provide the same insights as human creativity.
Comprehensive and meaning
While AI can certainly think about some aspects (especially when identifying patterns and proposing new solutions), it is different from human creativity because it relies on data-driven analytics rather than intuition or life experience. The role of AI in scientific discovery is better understood as a partner rather than a replacement for human scientists.
Research from Imperial University’s School of Business shows that AI complements traditional scientific methods, helps discover new principles and address declines in research productivity. Similarly, Kellogg researchers found that AI can have a positive impact in the field of science, but stressing training and interdisciplinary collaboration is crucial to leveraging the full potential of AI.
The biggest advancement in science may come from combining human creativity with AI’s analytical capabilities. Together they can accelerate the breakthrough and lead to discoveries that are beyond what we can imagine at the moment.
Bottom line
AI changes scientific research by accelerating discovery and introducing new ways of thinking. Although AI has proven the ability to generate hypotheses and identify new patterns, it is not able to think outside the box like humans. As of 2025, continued developments show that its impact on science will continue to grow. However, it is crucial to ensure that AI supports human efforts rather than replace them, and to focus carefully on transparency, validation and moral convergence. By working with human creativity, AI can enhance scientific progress and open up new ways of exploration.
