Page Gehlbach AERA Open 2017

Version
V0
Resource Type
Dataset
Creator
  • Page, Lindsay C. (University of Pittsburgh)
  • Gehlbach, Hunter (Johns Hopkins University)
Publication Date
2020-12-22
Free Keywords
summer melt; chatbot; experiment
Description
  • Abstract

    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.
Temporal Coverage
  • 2016-05-01 / 2016-09-01
    Time Period: Sun May 01 00:00:00 EDT 2016--Thu Sep 01 00:00:00 EDT 2016 (Summer 2016)
Geographic Coverage
  • Georgia State University (Atlanta, GA)
Availability
Download
This study is freely available to the general public via web download.
Relations
  • Has version
    DOI: 10.3886/E129643V1
Publications
  • Page, Lindsay C., and Hunter Gehlbach. “How an Artificially Intelligent Virtual Assistant Helps Students Navigate the Road to College.” AERA Open 3, no. 4 (October 2017): 233285841774922. https://doi.org/10.1177/2332858417749220.
    • ID: 10.1177/2332858417749220 (DOI)

Update Metadata: 2020-12-22 | Issue Number: 1 | Registration Date: 2020-12-22