By Lucy Wang
Advances in artificial intelligence are giving bioengineers new tools to pursue similarly ambitious ideas. AI could make projects once constrained by time, cost, and complexity increasingly practical, while also making the everyday work of bioengineering faster and more efficient.
For Esha Ananth, a 2026 UC Berkeley bioengineering graduate and recipient of the Bioengineering Chair’s Award, that shift was already visible in the classroom.
Ananth used AI primarily as a thought partner, helping her process ideas, learn unfamiliar skills, and execute projects more efficiently. Rather than replacing the technical work, she found that AI could remove some of the barriers between having an idea and figuring out how to pursue it.
“AI has mainly served as a brainstorming tool for me, helping me think through ideas, streamline formatting, and speed up project execution,” Ananth said. “It’s also opened up more room for creativity, since I can get support with learning new skills rather than getting stuck on the technical how-to to carry forward research approaches or ideas.”
One of AI’s most promising applications in bioengineering is its ability to analyze enormous amounts of biological data quickly. According to a review available through the National Library of Medicine, AI is already being used to analyze genomic data, interpret medical images, predict protein structures, and assist in drug discovery. In drug development, models can help predict toxicity and drug-target interactions, allowing researchers to narrow down candidates before investing in costly lab testing. Tools like AI can speed up the research process by helping bioengineers determine which possibilities are most worth pursuing.
But despite its robust capabilities, AI is not a substitute for scientific judgment. The accuracy of any AI model depends both on the quality of the data used to train it and the scientists who design and use it. The Convergence of AI and Synthetic Biology suggests that incomplete or biased datasets can produce unreliable and even dangerous predictions. Many AI systems operate as “black boxes,” which makes it difficult for researchers to understand how conclusions are reached. The authors emphasize that as AI becomes more integrated into automated bioengineering pipelines, human oversight becomes even more essential to ensure ethical decision-making and safe implementation.
For Ananth, that makes the skills AI cannot easily replicate even more important. Technical abilities in the lab or in data analysis still matter, she said, but AI has made it easier for students and researchers to acquire new skills or automate parts of technical work.
As AI makes it easier to learn or automate certain technical tasks, skills such as scientific judgment, communication, creativity, and interdisciplinary thinking are becoming increasingly valuable. Ananth has already seen that shift in her own education. “Specific skills, whether at the bench or in data analysis, still matter, but in the age of AI, you can teach yourself or automate almost any technical skill,” she said. “The most important thing is understanding the big picture: knowing why your work matters and being able to explain it to people outside the field.”
Ananth said studying at Berkeley changed her understanding of how broadly the field extends, from mechanical and computational engineering to materials science. “As someone who studied both bioengineering and business, I have learned how important it is to help people who control funding and investment understand and care about biological problems,” Ananth said.
For students entering a field changing this quickly, Ananth cautions against specializing too early. Bioengineering’s breadth, she said, gives students the opportunity to explore interests outside a single discipline and develop skills they may not initially expect to use.
“My advice is to take full advantage of that fluidity to explore tangential interests and skills rather than narrowing too soon,” Ananth said. “At the end of the day, it’s engineering: developing solutions to problems that happen to involve biology, not a deep study of biology itself.”
The tools available to bioengineers have changed dramatically since scientists set out to sequence the human genome in 1990. AI may make research faster and more efficient and allow scientists to pursue ideas that were once prohibitively complicated, but more powerful tools also make the questions bioengineers choose to ask more consequential. The future of the field will depend not only on what AI makes possible, but on the curiosity, judgment, and ambition of the people deciding what to do with it.