Designing for What's Next

Teaching for Tomorrow

UMSI faculty leaders Tanya Rosenblat, Tawanna Dillahunt, and Chris Brooks share how they are evolving their teaching to meet today’s learners and prepare students for an AI-driven, collaborative, and rapidly changing world.

Bachelor of Science in Information (BSI) - Tanya Rosenblat, Academic Program Director 

One of the most exciting aspects of teaching at UMSI is that our curriculum is very dynamic to account for rapid technological change and prepare students for the jobs of the future. One of the courses that I developed for our undergraduate program is SI 425: Introduction to User Modeling which uses insights from economics, psychology, behavioral economics, and computer science to model the behavior of users online. SI 425 is a required course for the Information Analysis path and an elective for students in User Experience and Social Media minors. My teaching in SI 425 is evolving to meet the demands of a future shaped by agentic AI. We're moving beyond traditional user behavior analysis as personal AI assistants, with their "infinite attention" and decision-making capabilities, will fundamentally change how companies will build and market products and services.

Recent research indicates that rather than focusing on informative advertising—especially through search engines—to capture scarce human attention, companies with high-quality products may benefit more from lowering prices to generate positive user reviews and drive AI agent-led purchases. Additionally, common behavioral biases tied to limited consumer attention, such as context effects, may be significantly reduced as AI agents can make more consistent comparisons on behalf of consumers.

If advertising becomes less important than today’s “freemium” digital services supported by advertising might need to find a new business model. One possibility is greater differentiation through customization: generative AI can unbundle digital content such as news into individual “photos”, “quotes” and “facts” and repackage them into personalized articles that address exactly the information gaps of the user. This would mirror what happened to digital music: 30 years ago, music was sold as albums which were a bundle of songs - however, modern streaming services can account for the replay of every individual track which has changed how artists are being paid. 

In my classes, I use an active learning approach where students engage in in-class experiments and surveys. These exercises let students experience new concepts firsthand through simulated decision-making, keeping them engaged and helping them “discover” ideas independently. Beginning in Fall 2023, I started tracking students' attitudes toward AI in the classroom. Self-reported AI use at the start of the semester rose from 28% in 2023 to 38% in 2024, and by the end of Fall 2024, 70% of students reported using AI to support their learning.

To better understand student perceptions, I conducted incentivized experiments to measure both descriptive and injunctive norms around AI usage. In 2023, students estimated that 60% of their peers used AI and 59% believed it was appropriate for coursework. By late 2024, both numbers had increased to 70%. These shifts show that AI tools are becoming an integral part of student's academic habits—even if they are sometimes hesitant to acknowledge it. While I encourage students to use AI to enhance their learning, I also expect them to demonstrate their understanding independently during assessments and discussions. Balancing these expectations raises important questions about the evolving ethics of AI in education.

Master of Science in Information (MSI) - Tawanna Dillahunt, Academic Program Director

Although I gave countless lectures and guest lectures while on sabbatical and scholarly leave, the 2024-2025 academic school year was my first time back in the classroom after two years. I teach primarily in our MSI program and since returning, I've recognized that many of these students' undergraduate experiences were affected by the pandemic, which limited their social interaction and opportunities to collaborate. I've seen how this shapes learning, a sense of connection, resilience, and students' willingness to take risks. 

As a result, I introduced a case study from Mary Gentile's Giving Voice to Values framework, which I learned about while on sabbatical. The case presented a complex, real-world ethical dilemma around an AI startup, and students responded positively to the content. Students valued the opportunity to exercise their values independently and then collaboratively with their peers. They all shared how they appreciated learning from one another and hearing each other's perspectives. I also took a low-tech approach to this exercise. I printed paper copies of the case and asked students to put away their devices. I've found that learning without devices forces us to focus more deeply and can lead to richer discussions. Devices can be distracting for the user and those around us in learning spaces.

Beyond the classroom, I see community-building as key to professional development and preparation. I'm interested in modeling community-building and collaboration, perhaps via teaching circles among faculty. I think sharing what's working in the classroom, particularly around AI will be helpful going forward. Modeling our collaboration could be beneficial in developing the interpersonal and community-oriented skills our students will need to lead and thrive in an ever-changing world.

Master of Applied Data Science (MADS) - Chris Brooks, Academic Program Director

I primarily teach data science in our online Master of Applied Data Science degree, and the applied nature of the degree means that students need to do a lot of programming -- even if they don't have an undergraduate degree in Computer Science! To keep momentum going when students run into bugs we've embedded novel generative AI hinting scaffolds directly in the assignment notebook, allowing learners to ask for a nudge when they don't understand the errors they come across. While this "bug solving" mode is helpful at getting the assignment done, the real value comes when going beyond the assignment, as learners who finish early can begin to explore "optimization" opportunities for their code with the AI. And throughout the use of this tool, we build in mechanisms for reflection, aiming to strengthen student metacognitive and self-regulated learning skills while helping them gain knowledge of the field of applied data science.