New Teaching Methods for an AI-Driven Workplace

• When schools embed AI across disciplines, students understand how the technology reshapes work, affects decision-making, and acts as a partner to create value.
• AI simulations enable schools to offer rigorous and equitable experiential learning opportunities that focus student effort on interpretation, synthesis, and judgment.
• Instructors can turn to customized chatbots to provide students with 24/7 access to information and guidance—augmenting rather than replacing the teacher’s role.

 
Artificial intelligence has gone from being “the next big thing” to something that’s foundational to the workflows of professionals around the world. As a consequence, corporate recruiters now expect business school graduates to be capable of using and critically evaluating the outputs of AI tools.

Importantly, recruiters are not asking graduates to display deep technical expertise, but judgment—to know when AI outputs make sense, when they do not, and how to adapt them to context. Recruiters report that today’s students are often more fluent with AI tools than employers themselves; however, there is a concern that students over-rely on AI to outsource thinking rather than treating it as a partner.

Interestingly, hiring itself is also becoming more human again. Because AI has made it easier for students to send out mass applications, recruiters are leaning harder on behavioral interviews, networking, and storytelling to vet candidates. Generic, overly polished AI-generated résumés are increasingly easy to spot and dismiss.

The message for business schools is clear: AI fluency matters, but it has to be paired with communication skills, self-awareness, and professional judgment. At many institutions, this means quietly redesigning the everyday mechanics of teaching and learning to transform how and what we teach, strengthen how students practice critical thinking, and update how we assess learning.

Schools can take five steps to ensure graduates master both technical and human competencies.

1. Embed AI Across the Curriculum

It’s not enough for schools to simply add a major in AI or treat it as a discrete topic. They must teach AI across disciplines and show students how it reshapes work itself, how it affects decision-making and project execution, and how value is created when humans work alongside intelligent systems.

AI is a foundational part of an undergraduate marketing research course at my school, Gies College of Business at the University of Illinois Urbana-Champaign. Professor Nathan Yang has woven AI into the logic of how research gets designed, not just how it gets analyzed.

In his class, students write their own research questions, then use large language models (LLMs) to pressure-test them. They ask the AI to play roles such as skeptic or “method cop” to uncover hidden assumptions, vague constructs, causal overreach, or feasibility issues. In qualitative projects, AI helps expand the design space but is deliberately kept away from interpreting participant meaning. The consistent message is that AI can sharpen thinking and uncover blind spots, but it can’t decide what’s strategically important or rescue a poorly designed study.

Schools must show students how AI reshapes work, how it affects decision-making and project execution, and how value is created when humans work alongside intelligent systems.

In our iMBA Digital Marketing Strategy course, students use AI as a decision-support tool inside realistic, uncertainty-filled business scenarios. During live class sessions, students upload simulated data sets and use LLMs to identify patterns; then, they critically compare how the same model behaves under strong signals versus weak ones. They analyze influencer transcripts for trust cues, evaluate AI-generated customer service responses, and test how recommendation systems might be optimized. At one point, they even encounter data poisoning, which forces them to reconsider earlier conclusions and build safeguards.

These experiences mirror the kinds of situations graduates will face in workplaces where intelligent systems are powerful, accessible, and imperfect. Students learn that they can treat AI as a useful analytical partner, but they must exercise managerial judgment to determine the quality and accuracy of its data.

2. Rethink Experiential Learning

AI is providing business schools with an alternative to traditional experiential learning models that require students to interact with executives at real companies. For instance, Gies professor Doug Laney historically had students in his MBA course interview industry professionals about data monetization and analytics. But he found that companies were increasingly reluctant to disclose sensitive or proprietary information.

So, Laney created a chatbot called Cleo to simulate a corporate communications director. By asking Cleo questions about data strategy, governance, and monetization, students can get the answers they need without compromising an organization’s privacy.

In simulations, the organizations being studied are not real, but the learning conditions are rigorous and equitable. Every student has access to the same information, assessment criteria are clearer, and instructors can analyze interaction logs to improve course design. They can even refine chatbots to make them more useful for future cohorts.

AI simulations can increase authenticity by focusing student effort on interpretation, synthesis, and judgment—the qualities that matter most in an AI-enabled workplace. Simulations also reflect a broader reality where digital representations and models increasingly shape real-world business decisions before physical actions are taken.

3. Emphasize the Human Value

As AI takes over more routine cognitive tasks in the workplace, employees are spending less time drafting or summarizing and more time synthesizing, navigating ambiguity, and making decisions with incomplete information. In some fields, such as public accounting, organizations consider it nonnegotiable to have a human in the loop.

For these reasons, professor Cassie Mongold added a soft skills module to her master’s-level financial reporting standards course last semester. The module specifically emphasized how students could communicate accounting information effectively to clients. Mongold tasked students with using AI to research an issue requiring significant judgment and present that information to a client.

Then, she met with each student team and played the role of an unfriendly client who did not like the news the students were delivering. She asked hard questions such as, “Why is this even the standard? If we started doing X instead, would that change your conclusion?” The feedback she received on the project was overwhelmingly positive—possibly influenced by the costumes she wore to fully get into character.

Business graduates will be most effective on their jobs when they better understand where they—as humans—bring the most value.

Jokes aside, while AI often can answer technical accounting questions, it cannot sit across the table from a client, explain what the answers mean, respond in real time to the inevitable “why?” and “what if?” follow-ups, and deal with big personalities of people who might not like the conclusions a professional has reached. Business graduates will be most effective on their jobs when they better understand where they—as humans—bring the most value.

4. Embrace the ‘Always-On’ Learning Environment

Student expectations for flexibility have accelerated adoption of course-specific AI support tools. Custom AI chatbots trained exclusively on instructor-approved materials are increasingly used to provide 24/7 access to course information and instructor guidance.

These tools free teachers from having to answer the same questions from different students every term. When instructor Mandi Alt piloted a chatbot in her tax course, she reported a 75 percent drop in repetitive questions. She reinvested that time in feedback, mentoring, and deeper teaching.

Always-on support also promotes equity. Students who can’t attend office hours, who are juggling jobs, or who need repeated explanations benefit from consistent access. When an AI tool is designed thoughtfully, with clear boundaries and academic integrity guardrails, it doesn’t replace instructors. It extends them.

5. Use AI to Redesign Assessment

In a profound shift to assessment design, some faculty are revising assignments to require interaction with AI, instead of attempting to detect or ban its use.

This often means they are relying less on traditional problem sets and essays, and more on simulations, interviews, presentations, and synthesis-heavy work. In these settings, AI is part of the environment, not the answer key. Students still have to explain their reasoning, make judgments, and defend their choices.

When students understand why AI is being used and what kind of thinking is being credited, shortcuts become less appealing. At the same time, faculty understand that academic integrity is easier to support through good assessment design than through surveillance.

Implications for Business Education

At Gies Business, we are on a deliberate journey to integrate AI into our courses both organically and through formalized curriculum innovation.

Organically, faculty with strong AI skill sets are responding quickly to transformations in their disciplines, bringing new tools and approaches into their courses and research. Formally, we have embedded AI activities into our four-year experiential learning sequence at the undergraduate level, as well as into our practicum and capstone courses at the graduate level. Across our programs, we are on a trajectory where all undergraduate and graduate students will take at least one course in which they use AI as a partner to solve real client problems, undertake industry research, prepare for interviews, analyze data, or conduct decision modeling.

In a profound shift to assessment design, some faculty are revising assignments to require interaction with AI, including simulations, interviews, presentations, and synthesis-heavy work.

To build consistency across the college, we have launched two formal mechanisms. The Gies AI Innovation Fellows Program will bring together a cohort of faculty who produce shareable deliverables: redesigned courses, academic integrity guidance for the AI era, and models others can adopt. Alongside this, we have established a centralized Gies AI resource hub that provides dedicated faculty training opportunities and brings in internal and external faculty experts to discuss effective classroom examples. Both initiatives will be formalized and expanded in the coming academic year.

While we are building toward a goal of consistency, we do not want to mandate uniformity. Faculty context and disciplinary judgment remain essential to integrating AI well.

A Focus on Competence

Our hope is that our efforts will alleviate the most common concern that students express: not that they will be displaced by AI, but that they will not be able to demonstrate their AI competencies to recruiters and employers. We have responded in two ways.

First, we have proposed new coursework centered on human judgment and critical thinking. For example, a Human Intelligence Lab course is in the works for students in our MBA and specialized master’s programs.

Second, our career services team has developed an AI readiness framework that prompts students to organize work by three categories: tasks that AI can handle independently, tasks that should be done in partnership with AI but require human direction, and tasks that remain distinctly human. As students meet with interviewers, they can discuss how they have handled these types of tasks and where they have added value to projects.

We know that our experiential learning courses are essential for helping students understand when they can partner with AI and when they must rely on human judgment. In these courses, if students defer uncritically to AI outputs, real clients provide an immediate reality check. That experience builds professional skepticism and follow-through more effectively than any standalone module could. We reinforce that skepticism by taking the Five Whys approach, encouraging students to keep questioning AI-generated outputs rather than simply accepting them.

Our faculty are adopting two models to integrate AI into the classroom. In the “build to learn” model, students build capabilities by exploring and experimenting with agentic AI tools.

In the “human manager” model, faculty emphasize that AI does not face real-world consequences for errors. Unlike a professional whose reputation is at stake, AI is wholly indifferent to whether its output is accurate. Students who cannot critically evaluate AI outputs are effectively signing off on work they cannot assess. When faculty teach from this perspective, they cover foundational knowledge first, discussing the underlying logic, assumptions, and trade-offs of their disciplines. Only then do they allow students to engage in “hyperscaling,” or using AI to amplify the work they already understand how to do.

A Broad Shift

As AI becomes essential to the professional work environment, AI tools become core parts of academic structure rather than optional add-ons. In response, faculty roles must expand from delivering content to developing learning architecture, becoming stewards of AI, and redesigning assessment protocols.

That raises key questions for all business schools. How consistent is the learning experience across courses? How are teaching values and learning science encoded into AI systems? How is trust maintained when intelligent systems are visible parts of learning?

Today’s business schools need to redesign learning around judgment, adaptability, and human contribution. Our greatest opportunities lie in shared learning—exchanging insights about what works, what does not, and how teaching methods can evolve responsibly in an AI-driven world.