AI can transform hard sciences but may struggle in soft sciences

Artificial intelligence is revolutionizing many areas of life, including academia. However, its influence is not the same across all academic disciplines. In hard sciences such as physics, chemistry and biology, AI is making groundbreaking progress. On the other hand, soft sciences like sociology, psychology and history present unique challenges that make AI integration more complex.
Hard sciences thrive on precision, consistency and quantitative data. These disciplines aim to understand natural phenomena through structured methods like controlled experiments and replicable measurements. AI is particularly effective in these environments because it excels at analyzing vast datasets, spotting patterns and making accurate predictions.
For example, in astronomy, AI has been used to identify exoplanets and analyze massive amounts of cosmic data. In genomics, it processes billions of DNA sequences to detect patterns that help researchers understand genetic disorders and develop treatments. Similarly, in drug discovery and material sciences, AI can simulate experiments and identify promising solutions in a fraction of the time traditional methods require.
What makes hard sciences an ideal playground for AI is the stability of their subjects. Physical laws and chemical reactions remain constant, enabling AI to optimize processes, refine simulations and conduct experiments without the risk of human error. AI-driven tools like automated labs further enhance efficiency by performing tasks faster and more precisely than humans.
Soft sciences deal with the complexities of human behavior, culture and social dynamics — areas that are often unpredictable and deeply influenced by context. Unlike the constants in hard sciences, human actions and societal patterns change depending on emotions, environments and cultural contexts. This makes it hard for AI to create accurate models or draw reliable conclusions.
Soft sciences rely heavily on qualitative data, such as interviews, case studies and ethnographies. This data is rich in meaning but difficult for AI to interpret. For instance, while natural language processing tools can analyze text, they often fail to capture the emotional depth or cultural nuances of a personal narrative or historical event. Understanding the complexities of human interactions requires empathy and intuition — traits AI lacks.

Understanding the complexities of human interactions requires empathy and intuition — traits AI lacks.

Dr. Munassir Alhamami

Additionally, soft sciences require interpretive analysis. A historian studying the significance of a historical event or a sociologist examining social dynamics must consider cultural, historical and subjective factors that AI simply cannot replicate. Human researchers bring life experience, cultural awareness and empathy to their work, allowing them to interpret data in ways that go beyond what AI is capable of.
Human behavior and social practices are highly variable, making it nearly impossible for AI to produce consistent models. While AI can assist by processing large datasets and identifying trends, its role in soft sciences remains supplementary. It cannot replace the critical human abilities to empathize, contextualize and interpret complex social phenomena. For example, in psychology, AI can analyze patterns in mental health data but struggles to understand the nuanced emotions behind a person’s choices. Similarly, in history, AI can identify trends across timelines but cannot fully grasp the human significance of those events.
AI’s strengths lie in its ability to handle structured, quantitative data, making it a powerful tool in hard sciences. It can conduct experiments, run simulations and accelerate discoveries, making it indispensable in fields like physics, chemistry and biology. However, in soft sciences, AI’s role is more about supporting human researchers rather than replacing them.
In soft sciences, AI can organize data, identify patterns and suggest hypotheses. But the interpretation of findings — the core of these disciplines — will always require human expertise. The future of academia lies in a balanced approach, in which AI and humans work together. AI’s efficiency in data processing and pattern recognition can complement the human ability to empathize and understand context.
AI will undoubtedly reshape the academic world, but its impact will vary depending on the discipline. In hard sciences, AI is a game-changer, driving progress with speed and precision. In soft sciences, however, it will remain a valuable tool, supporting but not replacing human researchers. By combining AI’s strengths with human insight, academia can better address both natural and societal challenges. The question is not whether AI will replace us, but how we can collaborate with it to advance knowledge and improve understanding of the world around us.

• Dr. Munassir Alhamami is a professor at the Faculty of Languages and Translation at King Khalid University in Abha, Saudi Arabia.