When we talk about artificial intelligence (AI) in the classroom, most conversations tend to focus on the negatives. Fears of cheating, plagiarism, reduced critical thinking skills, and more take the forefront, making us question whether or not these evolving technologies—including generative AI (GenAI)—have a productive place in learning environments.
As someone who has spent decades in higher education, I think these concerns are completely valid. I even share some of them with my peers. But I do not think this apprehension should stop academia from exploring the truly transformative potential of AI.
The data suggests that I’m not alone in this. According to Gurobi’s 2026 State of Mathematical Optimization in Academia report, 91% of faculty say GenAI specifically is having at least some impact on their work, including research, teaching, and report writing. When paired with optimization capabilities, GenAI has the potential to benefit coding, model generation, and the creation of detailed educational resources.
At this point, there’s no question that AI will affect academia. The challenge we face is determining how to embrace these tools without sacrificing the expertise and rigor that define academic excellence.
The Best Research Assistant We’ve Ever Had
One area ripe for enhancement is research. Academic projects are exhaustive, requiring interdisciplinary collaboration, extensive literature reviews, complex data analysis, and a seemingly never-ending stream of grant applications, progress reports, and publications. And all of this comes on top of professors’ lesson planning, teaching, and grading tasks.
This is where AI can provide some of its greatest value. For researchers who need to spend less time navigating information and more time producing actual insights, AI can help streamline a range of time-consuming tasks. From reviewing large volumes of literature to identifying connections between materials, summarizing findings, and helping outline early proposal drafts, GenAI tools can offload repetitive duties and empower researchers to work more efficiently and effectively—as long as outputs are regularly vetted for accuracy. By spending less time on these administrative tasks, academics can devote more attention to actual reasoning, thinking, and further advancement of their research.
Gurobi’s research highlights areas that AI models—and their increasingly frequent partner, mathematical optimization (MO)—are already making an impact on academic studies. In our survey, nearly a third (31%) of faculty said the combination of AI and MO could accelerate their research analysis and hypothesis generation.
In these scenarios, AI is not being used as a substitute for expertise. It enhances the work researchers are already doing, allowing them to explore ideas and advance their studies more quickly with less admin. It is, in many ways, the most efficient research assistant we’ve ever had. And the power of these research assistants continues to grow, expanding to coding and proof assistants, and even more general scientific co-workers. As one of my colleagues recently told me, these tools cannot be ignored anymore, so they need to be embraced.
Reclaiming Time for More Important Academic Tasks
Every professor knows that research is only one of the many responsibilities they must take on outside the classroom. Their day-to-day schedule is likely to include course preparation, grading, committee work, accreditation requirements, and other administrative tasks—all of which consume their valuable time.
One of AI’s most immediate academic benefits could be helping faculty manage this broad administrative workload. Faculty respondents reported that GenAI has had a “significant impact” on teaching (25%) and has the potential to help create educational tools and teaching interfaces for students (48%). Beyond these examples, AI could also help draft lecture materials, develop assessment rubrics, and summarize student feedback.
The potential use cases here are many, but their impact is shared. By helping faculty reduce the burden of routine administrative tasks, AI can create time for more meaningful teaching, mentorship, collaboration, and research.
Preparing Students for an AI-Augmented Future
Thus far, we’ve focused on how AI can make academics’ lives easier. But how does this translate to what is arguably their most important responsibility: preparing students for their post-graduate lives and careers?
It is near certain that today’s students will enter a workforce that involves AI in some capacity, fundamentally changing the nature of the work. This holds across numerous fields, where AI has become a productivity tool of unprecedented power. According to Gurobi’s research, many students already see significant opportunities to apply AI and MO to their work, identifying use cases such as coding (79%), model generation (53%), and research analysis (35%). With plans to enter information-heavy fields like operations research (29%), data science (16%), and other AI- or optimization-related businesses (15%), it’s essential that these students build a solid understanding of decision intelligence solutions before they graduate.
Institutions of higher learning have an opportunity to prepare students for this reality, teaching them to use AI tools both effectively and responsibly. This means demonstrating by example: showing students when AI can accelerate research, document and code generation, and communication materials, but also teaching them when they should instead rely on traditional human oversight. It means emphasizing verification, sound methodology, fundamental skills, and critical thinking when using these tools. And most importantly, it means emphasizing that accessibility should never come at the expense of academic rigor and integrity.
Amplifying Expertise Rather Than Superseding it
The future of higher education will not be defined by AI replacing faculty or core academic responsibilities. And it will not be defined by students acting with completely free and unchecked access to AI tools. The most successful academic models will be those that pair the inevitability of widespread accessibility with thoughtful adoption, targeted experimentation, and a continued emphasis on the indispensable nature of real, human expertise.
The next few years will bring enormous changes to both higher education and the broader marketplace. How we handle this transformation will determine how successful we will be as a society.
