By Prof. Tom Yeh
This portfolio offers a comprehensive account of my experience in designing and implementing the course on Advances in Artificial Intelligence (AI) and Neuroscience (CSCI 7000). This documented experience can provide a roadmap for other educators who are interested in designing advanced level graduate courses in an interdisciplinary area.
The goal of CSCI 7000, Advances in Artificial Intelligence (AI) and Neuroscience is three-fold. First, in terms of AI, the course seeks to lead students to explore the recent advances intersection of artificial intelligence and neuroscience, including algorithms, tools, and theories. Second, in terms of data, the course aims to let students engage in hand-on learning activities based on real, large, publicly available neuroimage datasets. Third, in terms of ethics, the course helps students identify ethical issues that may arise in the use of AI in brain imaging studies.
To serve the goal stated above, the course uses three methods to create a productive learning environment: flipped classroom, active learning, and peer teaching exercise. First, the course implements a flipped classroom by providing students with readings, video lectures, and coding exercises to work on at their own pace before coming to class. Second, during the class time, the majority of time is devoted to active learning activities, including retrieval practice, fishbowl, and role-playing.
Outside of the classroom, students work on a semester long project aimed to answer a research question using a real world brain imaging dataset and artificial intelligence techniques. The components of the project include: proposing a research question, selecting a dataset, exploring the dataset, designing a data analysis plan, executing the plan, identifying key findings, understanding the limitations, reporting the findings to an academic audience, and presenting the findings to the general public.
A sample of student work is included to illustrate how students responded to the learning activities this course provide. At the conceptual level, excerpts of students' responses to prompt questions for assigned scholarly publications are presented. Those excerpts demonstrate how students, mostly with a computer science background, received a scaffold to navigate through new and unfamiliar ideas and concepts they encountered in neuroscience papers. At the technical level, examples of code fragments and visualizations are included to demonstrate how students learned to implement algorithms and develop tools for analyzing neuroimaging data using AI.
As I reflect upon the semester, I observed students’ gain in knowledge of and interests in the intersection of artificial intelligence and neuroscience. I offer informal evidence that students are developing a richer vocabulary to engage in a productive scholarly discourse on deep topics concerning neuroscience and AI. Some students are motivated to pursue further interdisciplinary research opportunities at this intersection. Lastly, the unexpected shift to online learning caused by the COVID-19 pandemic posed a significant challenge to this course. I offer an account of how the class worked together to adapt the course to meet this challenge.