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DTSTART:20070311T020000
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DTSTAMP:20260908T105827
DTSTART;TZID=America/Detroit:20260915T113000
DTEND;TZID=America/Detroit:20260915T130000
SUMMARY:Workshop / Seminar:Generative AI Availability\, Grades\, and Student Satisfaction at a Large University
DESCRIPTION:The spread of generative AI (GenAI) in higher education has raised concerns that students offload cognitive effort to AI\, earning high grades without learning. If this \"GenAI substitution hypothesis\" is true\, grades should rise disproportionately in GenAI-susceptible courses -- those relying more on assessments like take-home problem sets and essays rather than in-class exams. Substitution could also affect student satisfaction\, measured here as self-reported understanding and interest in the subject\, which prior research links to assessments. We test the substitution hypothesis using syllabus and administrative data from a large U.S. university (2016-2025\; 138\,386 students\; 72\,730 course offerings). We measure courses' GenAI susceptibility using a human-validated LLM pipeline to extract assessment types from syllabi\, and use a differences-in-differences design comparing outcomes across courses before and after ChatGPT's release\, while modeling COVID-19 pandemic effects as either persistent or transient. We find no significant differential effect of GenAI availability on grades overall or among previously lower-performing students. Effects on self-reported understanding are likewise insignificant\; effects on interest are significant only assuming transient pandemic effects. Our findings temper concerns that GenAI inflates grades and reduces students' satisfaction.\n\nAuthors: James M. Zumel Dumlao\, Meng Wang\, Zhonghan Xie\, Junyao Hu\,\nIvan Bar1 George Chaney III\, Henry Gold\, Misha Teplitskiy
UID:150774-21910155@events.umich.edu
URL:https://events.umich.edu/event/150774
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Complex Systems,Generative Ai
LOCATION:Weiser Hall - 747
CONTACT:
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DTSTAMP:20260907T234737
DTSTART;TZID=America/Detroit:20260915T113000
DTEND;TZID=America/Detroit:20260915T125000
SUMMARY:Workshop / Seminar:Why Didn't the U.S. Unemployment Rate Rise at the End of WWII? (with Shigergu Fujita)
DESCRIPTION:This paper investigates why the U.S. unemployment rate rose only a few percentage points despite the dramatic decline in government spending and other upheaval at the end of World War II. Using a new longitudinal data set based on archival sources and government surveys\, we study the many facets of this question. Our findings suggest the following answers. First\, the dramatic decline in government spending led to a significantly smaller decrease in GDP than predicted by standard Keynesian models. Instead\, private job creation surged as private activity was “crowded in” by the fall in government spending. Second\, even accounting for the smaller fall in GDP\, the unemployment rate rose much less than predicted by Okun’s Law. We develop a new decomposition method that reveals how unusual movements in labor force participation rates\, hours worked\, and productivity led to a breakdown in Okun’s law during the 1940s. Third\, the U.S. labor market worked with astounding efficiency: despite large sectoral shifts at the end of the war\, most of the workers who separated from their jobs moved directly into new jobs without experiencing unemployment. All of these factors lined up to create a post-war boom despite the largest fall in government spending in U.S. history.
UID:149558-21906702@events.umich.edu
URL:https://events.umich.edu/event/149558
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Economics,seminar,Macroeconomics
LOCATION:North Quad - 4325
CONTACT:
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