Having a fundamental knowledge of how AI works provides an edge to students entering the workforce, which is why Bryant has made an Introduction to AI course a core requirement for all business students.
“The impact of AI on businesses and industries has been quick and dramatic,” says Todd Alessandri, Ph.D., dean of the Bryant College of Business. “It’s transforming the way companies operate, disrupting virtually every industry and altering most career paths."
Alessandri says the course — launched during the 2025-26 academic year — “provides a strong foundational understanding of AI” to make students “‘day one ready’ in this AI-enabled world.”
The course "provides a strong foundational understanding of AI” to make students “‘day one ready’ in this AI-enabled world.”
As one of the first individuals to teach the course, Michelle Munoz, associate director for educational technologies within Bryant’s Center for Teaching Excellence, says the course teaches AI literacy to people who don't have a programming, coding, or machine learning background.
“A lot of Finance majors, for example, are not going to go out and code a new system, but they need to understand the tools, know how to use them productively, and be able to have a coherent conversation about them,” says Munoz.
She notes that many schools embed AI education strictly into their engineering, computer science, or physics curricula.
“That isn't what we're trying to do here. My number one goal is to explore the historic trajectory of AI, how we got to generative AI, define it, and show what makes it different from what's come before,” Munoz says.
Rather than teaching math or programming, Munoz devotes much of her classroom time to explaining the basics of AI and how it can be applied in a business environment. “I also want students to cast a critical eye on the ethical, social, economic, and environmental implications of AI,” she says.
For example, undergrads spend a week learning about the data centers that power AI and the amount of resource intensity they require. Munoz runs a town hall where students role play around bringing a data center to their community. “Students take on the role of different stakeholders so they can debate it and look at all the implications,” she says.
The course’s most technical portion occurs when the discussion turns to applied AI.
“We talk about specific AI tools that they probably will find themselves using in the world, and about vibe coding [using AI to write software using natural language prompts],” Munoz says.
Throughout the semester, students work in groups to complete an AI innovation project where they identify a problem and then vibe code an AI solution using Bryant-supported tools like BryantGPT, Microsoft Copilot, and Gemini. “One of the important things students need to understand — at Bryant or wherever they end up working — is that they need to work within a safe AI environment,” Munoz stresses.
Many students come into the course having used AI chatbots and as a tool for research, says Munoz, but there’s still ample opportunity for learning. For example, one classroom exercise involves using AI to engage in a conversation with students where, instead of generating answers, the bot asks a series of questions to probe their understanding of AI.
“I've had several students say that they never thought to use AI in that way — to flip the script and have it question them,” she says. “With the final project, I ask every group what they’ve learned that surprised them, and one thing that came out quite a lot was the power of Claude, which runs BryantGPT, to vibe code and build apps.”
“Regardless of what field you're going into, whether it's highly technical or not, you need to be able to talk about AI and its use cases in a competent way."
As for Munoz, teaching the class has taught her how accessible machine learning is, even to non-technical people.
“Regardless of what field you're going into, whether it's highly technical or not, you need to be able to talk about AI and its use cases in a competent way,” she says. “I don't want students to go into a job interview or an internship and be like, ‘I know how to prompt.’ I want them to understand large language models and natural language processing — what’s going on under the hood — and be able to talk about these things even if they're not the ones building them.”