Page Gehlbach AERA Open 2017
Principal Investigator(s): View help for Principal Investigator(s) Lindsay C. Page, University of Pittsburgh; Hunter Gehlbach, Johns Hopkins University
Version: View help for Version V1
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text/x-stata-syntax | 14.3 KB | 12/21/2020 01:39:PM |
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application/x-stata | 1.4 MB | 12/22/2020 07:25:AM |
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Project Citation:
Page, Lindsay C., and Gehlbach, Hunter. Page Gehlbach AERA Open 2017. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2020-12-22. https://doi.org/10.3886/E129643V1
Project Description
Summary:
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Deep reinforcement learning using convolutional neural networks is the technology behind autonomous vehicles. Could this same technology facilitate the road to college? During the summer between high school and college, college-related tasks that students must navigate can hinder successful matriculation. We employ conversational artificial intelligence (AI) to efficiently support thousands of would-be college freshmen by providing personalized, text message–based outreach and guidance for each task where they needed support. We implemented and tested this system through a field experiment with Georgia State University (GSU). GSU-committed students assigned to treatment exhibited greater success with pre-enrollment requirements and were 3.3 percentage points more likely to enroll on time. Enrollment impacts are comparable to those in prior interventions but with substantially reduced burden on university staff. Given the capacity for AI to learn over time, this intervention has promise for scaling personalized college transition guidance.
Scope of Project
Subject Terms:
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summer melt;
chatbot;
experiment
Geographic Coverage:
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Georgia State University (Atlanta, GA)
Time Period(s):
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5/1/2016 – 9/1/2016 (Summer 2016)
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