Takeaways from the CESIM Elite Instructor Workshop 2026, featuring keynote speaker Dr. Tawnya Means.
At this year's CESIM Elite Instructor Workshop, keynote speaker Dr. Tawnya Means opened with a question that reframes almost every conversation happening in higher education right now. When we ask how to prepare students for a world of AI, we usually reach for the obvious answer: teach them the tools. Run a prompt-engineering workshop. Offer a certification. But according to Means — founder and Principal at Inspire Higher Ed, co-founder and Chief Academic Officer of AdvancAI Higher Ed, and author of The Collaboration Chronicle: Human+AI in Education — the students themselves are asking for something else.
What the data actually says
Means drew on three independent studies: a GMAC survey of more than 4,000 graduate management candidates across 145 countries, a Gallup study of 1,500 young people aged 14–29, and a California State University survey of over 80,000 students. Different populations, different methods — and a strikingly consistent signal.
Demand for AI in the curriculum has climbed fast, from roughly 17% of candidates wanting it in their ideal program in 2022 to 50% by 2025. That part isn't controversial. What's revealing is how they want it. Asked to rank the options, students put hands-on experience in realistic business simulations first (66%), followed by AI in business strategy (62%) and decision-making (58%). Training in prompt writing and everyday AI tasks ranked lowest, at 46%.
In other words, students want AI woven into the real intellectual work of their disciplines. They are less interested in being trained as proficient tool users — which is what many institutions have been building. As Means put it, we've nearly inverted what students told us they wanted.
The quieter worry
There's a second signal underneath the first. Among Gen Z — roughly half of whom use AI weekly — excitement has fallen 14 points in a single year, while anger has risen. Most striking of all: eight in ten say they believe AI is likely to make it harder for them to learn. It isn't the skeptics raising the alarm. It's the daily users.
Employers, meanwhile, are asking for problem solvers, strategic thinkers, communicators, people who show initiative, emotional intelligence, and coachability. None of these are technical skills. None can be fixed with a prompt-engineering course. They are dispositional capacities that develop through experience, feedback, and the productive struggle that good course design is meant to protect.
Students and employers, Means noted, are pointing at the same thing from opposite directions: develop the thinking. AI genuinely matters — but in service of that thinking, not as a substitute for it.
The good news for educators
If that feels daunting, Means offered a genuinely reassuring turn: educators already have what this moment requires. The prerequisite for using AI well isn't technical fluency — it's thinking well, something most educators have spent whole careers developing.
She told the story of an instructor of 40 years who arrived at a workshop skeptical and disengaged, and left invigorated — not by the novelty of the tools, but by the realization that four decades of knowing exactly where his students get stuck was the very thing that would make AI useful to him. Deep thinking, he discovered, could let a late adopter outpace people who had used AI superficially and stopped there.
That word — stopped — is the crux. Most people take one or two exchanges with AI, accept or reject the answer, and move on. Means argues the real value shows up through persistent iteration: pushing back, questioning, bringing your own voice and judgment to what comes back. The first response, she reminds us, is never the final response. Treating AI as a thinking partner rather than an answering machine is itself a skill — and one worth modeling for students.
Her practical starting point: pick a real problem you actually care about, one with genuine stakes and texture. Explain it the way you'd brief a thoughtful new colleague. Then, when the response comes back, don't simply accept it — ask what it got wrong, what claims it made without evidence, what's missing. You're not asking AI to solve the problem. You're asking it to help you think better.
From principle to practice
This is the philosophy CESIM has been building toward. Neuroscientist Dr. Vivienne Ming warns that too many technology products make people better only while they're using them, then leave them worse off once they switch off. In education, the aim has to be the opposite: students who are genuinely more capable afterward, not more dependent.
For more than 25 years, CESIM has built business simulations where students learn by making real decisions in teams. Its AI layer is designed to keep the cognitive work where it belongs. The Tireless Teaching Assistant handles the grunt work for the instructor — analyzing results, surfacing who did what, supporting feedback — so educators can focus on the human side of teaching. The newer AI Coach is deliberately Socratic: it's context-aware, knowing where a team sits in the simulation and how they compare with others, but it won't hand over answers. Ask it how to improve your sales and it helps you reason your way there. That friction is the point.
A favorite example came from Telecom Paris, where a cohort of 200-plus engineering students — more than capable of writing their own agents — were told to use AI freely during the simulation, then sat a pen-and-paper assessment with no tools at all, with faculty using the AI Assistant to generate team-specific questions from each team's simulation rounds, so every group faced a unique narrative. They used AI to deepen their thinking during the rounds, but couldn't offload the learning itself, because the assessment demanded real knowledge. The outcome: faculty who had feared AI would erode learning discovered how much it could add when the design assesses the process, not just the output.
Same work, new tools
The throughline of the whole session was simple: the tools are new, but the work isn't. Education has always been about developing judgment, evaluation, oversight, and human relationships — the kind of thinking no tool will replace. Used thoughtfully, AI can serve that goal. Used as a shortcut, it quietly undermines it.
Two ways to keep going. To go deeper on the ideas, Dr. Tawnya Means writes regularly about human + AI collaboration in The Collaboration Chronicle: Human+AI in Education. And to see what context-aware AI looks like in your own simulation classroom, book CESIM AI demo and we'll walk you through the AI Coach and the Tireless Teaching Assistant in action.
About the speaker
Tawnya Means, PhD, is founder & Principal at Inspire Higher Ed, co-founder and Chief Academic Officer of AdvancAI Higher Ed, and co-founder of AI Convergence — ventures collectively focused on helping higher education institutions lead AI transformation. She also serves as Strategic Innovation and AI Advisor at Bowling Green State University and Chief Learning Officer at AristAI. A long-standing AACSB seminar facilitator and published scholar, she holds a PhD in Information Science and Learning Technologies from the University of Missouri. With nearly two decades of experience across six continents, she is author of The Collaboration Chronicle: Human+AI in Education on Substack. Her work centers on evidence-based Human+AI partnerships that preserve what makes education irreplaceably human.
