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Using artificial intelligence to assess personal qualities in college admissions
Personal qualities like prosocial purpose and leadership predict important life outcomes, including college success. Unfortunately, the holistic assessment of personal qualities in college admissions is opaque and resource intensive. Can artificial intelligence (AI) advance the goals of holistic adm...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Association for the Advancement of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10569720/ https://www.ncbi.nlm.nih.gov/pubmed/37824610 http://dx.doi.org/10.1126/sciadv.adg9405 |
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author | Lira, Benjamin Gardner, Margo Quirk, Abigail Stone, Cathlyn Rao, Arjun Ungar, Lyle Hutt, Stephen Hickman, Louis D’Mello, Sidney K. Duckworth, Angela L. |
author_facet | Lira, Benjamin Gardner, Margo Quirk, Abigail Stone, Cathlyn Rao, Arjun Ungar, Lyle Hutt, Stephen Hickman, Louis D’Mello, Sidney K. Duckworth, Angela L. |
author_sort | Lira, Benjamin |
collection | PubMed |
description | Personal qualities like prosocial purpose and leadership predict important life outcomes, including college success. Unfortunately, the holistic assessment of personal qualities in college admissions is opaque and resource intensive. Can artificial intelligence (AI) advance the goals of holistic admissions? While cost-effective, AI has been criticized as a “black box” that may inadvertently penalize already disadvantaged subgroups when used in high-stakes settings. Here, we consider an AI approach to assessing personal qualities that aims to overcome these limitations. Research assistants and admissions officers first identified the presence/absence of seven personal qualities in n = 3131 applicant essays describing extracurricular and work experiences. Next, we fine-tuned pretrained language models with these ratings, which successfully reproduced human codes across demographic subgroups. Last, in a national sample (N = 309,594), computer-generated scores collectively demonstrated incremental validity for predicting 6-year college graduation. We discuss challenges and opportunities of AI for assessing personal qualities. |
format | Online Article Text |
id | pubmed-10569720 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | American Association for the Advancement of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-105697202023-10-13 Using artificial intelligence to assess personal qualities in college admissions Lira, Benjamin Gardner, Margo Quirk, Abigail Stone, Cathlyn Rao, Arjun Ungar, Lyle Hutt, Stephen Hickman, Louis D’Mello, Sidney K. Duckworth, Angela L. Sci Adv Social and Interdisciplinary Sciences Personal qualities like prosocial purpose and leadership predict important life outcomes, including college success. Unfortunately, the holistic assessment of personal qualities in college admissions is opaque and resource intensive. Can artificial intelligence (AI) advance the goals of holistic admissions? While cost-effective, AI has been criticized as a “black box” that may inadvertently penalize already disadvantaged subgroups when used in high-stakes settings. Here, we consider an AI approach to assessing personal qualities that aims to overcome these limitations. Research assistants and admissions officers first identified the presence/absence of seven personal qualities in n = 3131 applicant essays describing extracurricular and work experiences. Next, we fine-tuned pretrained language models with these ratings, which successfully reproduced human codes across demographic subgroups. Last, in a national sample (N = 309,594), computer-generated scores collectively demonstrated incremental validity for predicting 6-year college graduation. We discuss challenges and opportunities of AI for assessing personal qualities. American Association for the Advancement of Science 2023-10-12 /pmc/articles/PMC10569720/ /pubmed/37824610 http://dx.doi.org/10.1126/sciadv.adg9405 Text en Copyright © 2023 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY). https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution license (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Social and Interdisciplinary Sciences Lira, Benjamin Gardner, Margo Quirk, Abigail Stone, Cathlyn Rao, Arjun Ungar, Lyle Hutt, Stephen Hickman, Louis D’Mello, Sidney K. Duckworth, Angela L. Using artificial intelligence to assess personal qualities in college admissions |
title | Using artificial intelligence to assess personal qualities in college admissions |
title_full | Using artificial intelligence to assess personal qualities in college admissions |
title_fullStr | Using artificial intelligence to assess personal qualities in college admissions |
title_full_unstemmed | Using artificial intelligence to assess personal qualities in college admissions |
title_short | Using artificial intelligence to assess personal qualities in college admissions |
title_sort | using artificial intelligence to assess personal qualities in college admissions |
topic | Social and Interdisciplinary Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10569720/ https://www.ncbi.nlm.nih.gov/pubmed/37824610 http://dx.doi.org/10.1126/sciadv.adg9405 |
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