Generative AI (GenAI) has quickly moved from being a futuristic concept to a practical tool reshaping the way we live, work, and learn. While much has been said about AI’s impact on business, its influence on academia is equally profound. At Gurobi, we are deeply engaged with the academic community, and we recently hosted a live round table discussion as part of our yearly Gurobi Days Digital Academic event — Generative AI in Teaching and Research: How Academia Is Adapting — to hear directly from educators on how teaching, learning, and research are evolving in this new era.
Moderated by our own Thomas Braam, Senior DevOps Engineer, the panel brought together three leading academics to share their perspectives:
Together, we explored how GenAI is influencing education, where the risks and opportunities lie, and what students and researchers need to know to thrive in this new landscape.
When discussing the skills students should double down on — or de-emphasize — now that AI can generate code, analyze data, and even write essays, the panelists highlighted several themes.
What stood out to us was Dr. Li’s emphasis on the importance of fundamentals such as math and physics, noting that these first principles remain essential. He also stressed critical thinking, since GenAI tools can sometimes produce answers that sound confident but are incorrect.
Dr. Albert noted that coding itself is evolving. Syntax and rote programming are less critical now that AI can assist with these tasks. Instead, she emphasized domain knowledge and the ability to integrate AI responsibly.
Dr. Nikandish added that communication skills remain essential. Even if AI generates code, students must understand what it does and be able to clearly explain results to stakeholders — otherwise the technology risks becoming more of a crutch than a tool.
Our discussion highlighted how student learning behaviors have shifted since tools like ChatGPT became widely available.
Dr. Albert observed that academia has not yet built a strong culture of responsible AI use. Students often shortcut their work by leaning on AI, making it difficult for professors to distinguish responsible use from over-reliance. In response, she and colleagues are placing greater emphasis on in-class exams and oral presentations, where students cannot simply outsource their work.
Dr. Nikandish expressed concern that students may lose valuable learning opportunities. Struggling through problems, making mistakes, and iterating are essential to building critical thinking skills. GenAI, he cautioned, risks removing that process. At the same time, AI can give students — particularly those without prior coding experience — greater confidence at the start.
Dr. Li suggested that future AI tools may work best if they guide students step by step, more like a teaching assistant, rather than providing full solutions immediately.
The panelists shared examples of how they have adapted their teaching practices in response to GenAI.
Dr. Li described changes in his statistics and optimization courses. After discovering that ChatGPT could score perfectly on take-home exams, he restructured assessments to emphasize in-class performance. AI can also help tailor explanations to students’ backgrounds, making advanced mathematical concepts more approachable.
Dr. Albert introduced “AI statements” into student projects, requiring students to disclose how they used generative AI. “Today’s students are tomorrow’s professionals,” she explained, “and I want to cultivate responsible usage of AI in this culture.” Students submitted written statements and presented slides on their AI use, fostering transparency and accountability.
Dr. Nikandish described transforming his Python for Data Analysis course with a flipped classroom model. Students review materials and experiment with AI tools outside class, then work in teams on case studies during class. At the end of the semester, they present projects on video — defending their code, results, and reasoning as if pitching to investors.
In research, the panelists discussed opportunities and risks.
Dr. Nikandish uses AI for literature reviews and prototyping supply chain analytics models. Tasks that once took weeks can now be completed much faster, but he stresses the need to understand every line of generated code.
Dr. Albert emphasizes the need to convey expectations for AI use in research mentoring. She added a section on GenAI use to her lab compact with principles of transparency, responsibility, critical thinking, and research integrity. She encourages her students to only use AI for brainstorming, coding support, or feedback on writing — with humans remaining ultimately accountable.
Dr. Li highlighted AI’s time-saving potential in coding. Tools can now generate documentation or unit tests for software projects, freeing students to focus on higher-level work.
Panelists agreed that universities are still grappling with formal AI policies. Most institutions discourage reliance on AI-detection tools due to high false-positive rates, instead encouraging disclosure and acknowledgement. At the publication level, journals typically require authors to state how AI assisted the research, but forbid listing AI systems as co-authors.
Dr. Albert noted that while strategy documents exist, culture often lags: “We have strong AI guidelines, but culture eats strategy for breakfast.”
Panelists envisioned both opportunities and challenges.
All three agreed that the greatest risk is “deskilling” — students relying too heavily on AI without first developing foundational knowledge and problem-solving skills.
The discussion closed on an optimistic note about collaboration. GenAI can bridge disciplinary language gaps, making it easier for researchers from different fields to work together. This could spark breakthroughs in domains where operations research expertise has not traditionally been applied.
Reflecting on the insights shared during our panel, it’s clear that AI can empower students and researchers — if guided thoughtfully. At Gurobi, we’re inspired by these conversations and committed to providing educators and students with the tools and support to use AI responsibly, strengthen critical thinking, and solve real-world problems.
Senior Director of Academic Programs
Senior Director of Academic Programs
Lindsay brings over 13 years of experience working at the intersection of technology and education. Prior to Gurobi, Lindsay worked as an Operations leader at Opex Analytics, a product and services firm dedicated to solving complex business problems using the power of Artificial Intelligence. While there, she focused on growth and business development, product launch, and marketing. Lindsay spent 10 years working in various leadership capacities at Universities including Columbia University, Northwestern University, and the University of Chicago. From 2013 to 2017, she worked to establish and grow the Master of Science in Analytics degree at Northwestern University’s School of Engineering, the program was one of the earliest MS degrees focused on an applied data science curriculum. During her time with Northwestern, she managed external relations and corporate relations, helped hire and onboard new faculty and subject-matter experts in various disciplines of analytics, directed recruiting efforts/admissions/student advising, and managed a team of administrative professionals. Prior to Northwestern, she spent over 5 years working in Advancement at Columbia University’s School of Engineering and Applied Science. She completed her Bachelor’s Degree in English and Fine Art at Sewanee: The University of the South and her Master’s Degree in Nonprofit Management at Columbia University.
Lindsay brings over 13 years of experience working at the intersection of technology and education. Prior to Gurobi, Lindsay worked as an Operations leader at Opex Analytics, a product and services firm dedicated to solving complex business problems using the power of Artificial Intelligence. While there, she focused on growth and business development, product launch, and marketing. Lindsay spent 10 years working in various leadership capacities at Universities including Columbia University, Northwestern University, and the University of Chicago. From 2013 to 2017, she worked to establish and grow the Master of Science in Analytics degree at Northwestern University’s School of Engineering, the program was one of the earliest MS degrees focused on an applied data science curriculum. During her time with Northwestern, she managed external relations and corporate relations, helped hire and onboard new faculty and subject-matter experts in various disciplines of analytics, directed recruiting efforts/admissions/student advising, and managed a team of administrative professionals. Prior to Northwestern, she spent over 5 years working in Advancement at Columbia University’s School of Engineering and Applied Science. She completed her Bachelor’s Degree in English and Fine Art at Sewanee: The University of the South and her Master’s Degree in Nonprofit Management at Columbia University.
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