This blog post is part of a series of posts covering community members’ experiments with AI in the classroom and the workplace.
Al Montero, Frank B. Kellogg Professor of Political Science, has been experimenting with GenAI to create interactive simulations of real historical events. These simulations allow students to act out political and economic decision-making at pivotal moments in world history.
In Democracy and Dictatorship (POSC 120), for instance, students play out the leadership of a democracy in decline. First, students choose which country to play as: either the Weimar Republic or the Second Spanish Republic during their respective declines in the 1930s. Then, students must pilot their country through a four-year period of political downturn, applying what they have learned in class and from readings to keep the state from complete democratic breakdown. “Not everyone succeeds,” Montero notes, “and it’s always interesting to go back and see what went wrong in each case.”
Similarly, in Public Policy and Global Capitalism (POSC 265), students test their knowledge of fiscal policy by playing as a US administration during a time of economic crisis. Students can play as the Nixon administration, for example, during stagflation in the 1970s, or as the Obama administration during the 2008 recession.
Montero has also found GenAI useful in his own research. “It can’t replace expertise, he notes, “but it’s strong with pattern recognition.” He gives the example of sifting through long, technical reports for references to specific topics. GenAI can quickly process long documents and direct you to specified points of interest. It also has the added benefit of natural language processing, allowing you to locate this information without having to know document-specific keywords ahead of time.
Montero also encourages students to use GenAI in this way, while cautioning them against using it to uncritically replicate “expert” opinions. Rather, as he puts it, “students must apply their own emerging expertise to identify deeper expertise.” “It’s when AI becomes an impediment to this process that we ought to be concerned.” For this reason, Montero stresses the importance of extending familiar lessons from the liberal arts to AI use, thereby teaching students how to use these tools effectively and responsibly.
Far from eliminating the need for individual expertise and expression, Montero sees in the increasing popularity of AI an increasing need for teamworking and rhetorical skills. He notes that the liberal arts’ focus on writing and clear argumentation has not always extended to oral communication. As it becomes easier to automate written communication, these abilities—speaking in public, responding to critique in real time, and improvising moment to moment—become even more valuable.