Katie Furniss and Max Kramer — both teaching associate professors in the Department of Biology Teaching and Learning — are among the University’s 2025-26 Emerging Technologies Faculty Fellows. The 18-month program brings together 16 faculty from all five UMN campuses to define the role of new technologies in higher education and to move beyond the "AI detection" mindset toward deeper engagement with artificial intelligence in an education context.
For Furniss, the motivation to join the cohort was rooted in the program's robust support system of mentors and experts. "The program caught my attention because its major goal is to help fellows develop a project using generative AI," Furniss says. "I decided to apply because the program doesn’t leave the fellow to figure it out on their own.” Ultimately, she wants to gain a better understanding of how AI works in order to better determine when it would or wouldn’t be useful for her students.
Furniss is piloting her project in the Foundation of Biology course, asking students to integrate the technology into a project focused on developing concept maps to organize biological information.
“Educational research as well as studies by UMN show that students are using AI for help in the classroom,” says Furniss. “There are benefits to this use as it allows students to get help whenever they might be working on that course and to ask questions they may not feel comfortable asking a TA or instructor. However, AI is known to provide confident responses that are incorrect. This project will work to leverage the benefits while also building the critical thinking skills necessary for students to become AI collaborators."
Kramer’s work centers on the Foundations of Biology labs, where students investigate the human microbiome using massive datasets and complex coding. Kramer acknowledges the "undeniably steep" learning curve of using tools like the R statistical language and supercomputers.
"I want students to learn how to use AI tools while also building up their own understanding and critical thinking in experimental design and analysis," he says. Kramer plans to use AI to guide students through writing code and selecting statistical tests, replacing static lab manuals with more dynamic, responsive support.
Kramer is particularly concerned with the "paradox" of AI: the need for expertise to evaluate its output, while simultaneously relying on it to provide that very expertise.
"It’s hard to definitively assess the impact of AI tools on student work, but it’s certainly very significant," he says. "Given that reality ... I think it’s important that we address it head-on and help students learn to use AI effectively. And when not to use it."
Furniss and Kramer are excited to be part of a community of practice that extends to the entire University. "It is energizing to engage with folks involved in impactful and cutting-edge work happening across the University," says Furniss. "I will bring what I learn ... to benefit my department, which will further benefit the thousands of students we teach and mentor every year." –Stephanie Xenos