Evolving curriculum: Learn about 5 CWRU courses integrating AI

As artificial intelligence (AI) disrupts the status quo across industries and challenges us to adapt to a rapidly changing world, Case Western Reserve University students will be poised to respond.

Instructors for courses across campus—from economics to nursing to biomedical engineering—are increasingly drawing from real-world scenarios to equip students with the tools to confront and leverage AI head-on. CWRU’s AI offerings go beyond the surface of their subjects, preparing participants to understand, evaluate and navigate these rapidly changing technologies.

“Machine Learning for Predictive Analytics in Economics”

In Wang’s course, students are tasked with using powerful analytic tools available through artificial intelligence—with the caveat that AI is just a tool and not a solution. Wang encourages his students to explore real-world data sets and use Python-based tools to clean data, explore patterns, build predictive models and evaluate their performance. 

In the process, students learn a variety of analytical methods, including regression, decision trees, ensemble methods, feature engineering and model validation. While AI can often run their analyses, he encourages students to understand the processes intimately so they can ensure they are feeding the programs the best data.

“The emphasis is not on learning a particular platform,” said Wang, who puts a focus on “durable” skills and not trending popular platforms. “Tools will continue to change. I want students to understand the analytical process well enough that they can evaluate and use whatever tools emerge next.”

A critical skill students must develop, he said, is the ability to communicate the results of their analyses, regardless of whether they used a tool to complete them or ran them on their own. They also must be critical of the results they receive.

“With AI making technical implementation increasingly accessible, I think these judgment skills become even more important,” Wang said. “I want students to leave the course comfortable using AI and machine learning, but also able to question their outputs and understand when a model is—or is not—useful for a particular decision.”

His students are already reporting the benefits of Wang’s approach—one sharing they used the experience to rapidly prototype different analyses in a job interview, which was met with positive feedback from their interviewer. 

“Management 395: AI as Your Ally”

In his classroom at the Weatherhead School, Sam Gerace, faculty director of the Master of Business Analytics and Intelligence program,  finds many of his students have expansive experience with large-language models (LLMs) and chatbots. The familiarity to the platforms, however, is often where their understanding stops. 

To encourage students to gain context on how AI technologies function, including their capabilities, limits and risks, Gerace prepares activities and up-to-date readings. For his course “Management 395: AI as Your Ally,” a half-semester offering starting in October, he’ll select course materials just before the first class session—an indicator of just how quickly AI is evolving.

After beginning with a broad look at topics such as the ethics, governance and applications surrounding AI, the course will transition to explore specific ways students can use this technology to their benefits.

“I especially want them to know how and when to apply those technologies, and how to use that knowledge to maximize their leverage in their careers and non-work lives,” said Gerace, who’s serving as a visiting associate professor. 

Given the fast-paced nature of the advances in the industry, Gerace guides students in creating a continuous learning plan to help them keep up with the developments even after the course concludes.

He’s also learning alongside his students. Though he admits he was well-versed in how implicit bias can inadvertently cause harm in a corporate risk-management framework, Gerace’s students have expanded his awareness of the topic to include AI.

“I find myself approaching new developments in predictive and generative machine learning with a much sharper focus on implicit individual, academic and community impacts,” he said. “I owe that in large part to students' attention to it.”

“Health Promotion” & “Family Health Nursing”

Students in the health sciences are responsible for more than just learning about the functions of the human body. They must also learn to work with patients and interpret complex scenarios at a rapid pace. 

In Marie Grosh’s NUNP 410 and 419 courses at Frances Payne Bolton School of Nursing, students are using AI to better prepare for interactions with human patients, 

Grosh, an assistant professor of nursing, worked with Tron Compton-Engle, assistant vice president of client experience in University Technology, to develop an AI-driven chatbot to simulate telehealth patient conversations. 

Students engage in the chatbot to “manage” patients with conditions relevant to what was discussed recently in class. In doing so, they’re charged with providing evidence-based, patient-centered recommendations in an email format using language that can easily be understood by a non-medical audience.

While previous iterations of the course simulated patient interactions in a discussion-board model, the chatbot has proven more time effective and allows students to more deeply hone their communication skills in a low-stakes environment and carry these over into clinicals they may be participating in simultaneously. 

“I’m very passionate about experiential learning and using simulation in formative learning activities in the classroom and with virtual distance-learning courses,” Grosh said. “I’m a primary care clinician who does this work every single day so I want the students to experience what I experience when I am managing my patients via telehealth.”

Students speak highly of their experiences working with the chatbot, with many listing it in course evaluations as the activity that best helped them apply course concepts.

“Biomedical Engineering: AI in Medical Imaging”

A subset of AI designed to make predictions based on raw data, machine learning holds great power to diagnose and treat patients using medical imaging. In Shuo Li’s “Biomedical Engineering: AI in Medical Imaging” course at Case School of Engineering, students are gaining insights into the process by examining topics such as image registration, segmentation, visualization and deep learning–based prediction of disease and disease stage.

In this project-based course, Li, the Leonard Case, Jr. Professor in Engineering, tasks students to develop their own deep learning models to apply to open-source medical imaging datasets. Then, they are tasked with evaluating their model’s performance through visual assessment and quantitative metrics.

Later in the course, students team up to evaluate work published in leading journals or presented at prestigious conferences, assessing the limitations, reproducing the analyses and offering possible improvements to the work they studied. 

To best promote career readiness, Li incorporates the clinical areas in which AI is making the biggest impacts, such as cardiovascular imaging, musculoskeletal imaging and ultrasound, and introduces students to emerging methods.

What is most important, in his view, is not that students learn how to use AI, but that they play an active role in shaping it—critically evaluating it, identifying where it falls short and building what comes next.

“[This] course reflects the distinctive CWRU and Cleveland ecosystem, where computer science, biomedical engineering, medical research, and clinical practice are closely connected,” said Li. “Students examine AI problems and research examples motivated by real clinical needs, helping them understand how computational advances can move beyond the laboratory toward meaningful healthcare applications.”

“Computer and Data Sciences: Current Issues in Artificial Intelligence, for Better or Worse”

AI has become an influential, and frequently debated technology prompting ongoing discussion about both its potential benefits and its limitations. No matter what side of this debate students stand on, they can benefit from taking Ronald Loui’s “Computer and Data Sciences: Current Issues in Artificial Intelligence, For Better or Worse” course at Case School of Engineering. 

In this course, Loui challenges his students to go beyond the oft-parroted benefits and drawbacks of AI technology and dig deeper into the limitations, risks, uses and real-world consequences of autonomous systems, decision-making software and cognitive surrender.

Driven by discussion of peer-selected readings, and framed by the Loui’s four decades of AI papers and research, participants examine how AI is shaping policy on work, education and society, all the while interacting with large language models to test their weaknesses and creative capabilities.

Students are also free to leverage AI in their coursework, but Loui issues a warning.

“They can use AI to do their work but I tell them chat is invariably ‘B’ work, not ‘A’ level,” said Loui, adjunct professor of computer and data sciences. “Don't let it ruin your chance to impress—just like real-life use/misuse.”

Ultimately, Loui wants students to walk away from the course with confidence in their abilities to think thoughtfully and critically about AI—becoming mature voices and leaders in the conversations around the technology.