Abstracts
Abstract
Background: Medical school applications often require short written essays or personal statements, which are purportedly used to assess professional qualities related to the practice of medicine. With generative artificial intelligence (AI) tools capable of supplementing or replacing inputs by human applicants, concerns about how these tools impact written assessments are growing. This study explores how AI influences the ratings of essays used for medical school admissions
Methods: A within-subject experimental design was employed. Eight participants (academic clinicians, faculty researchers, medical students, and a community member) rated essays written by 24 undergraduate students and recent graduates from McMaster University. The students were divided into four groups: medical school aspirants with AI assistance (ASP-AI), aspirants without AI assistance (ASP), non-aspirants with AI assistance (NASP-AI), and essays generated solely by ChatGPT 3.5 (AI-ONLY). Participants were provided training in the application of single Likert scale tool before rating. Differences in ratings by writer group were determined via one-way between group ANOVA.
Results: Analyses revealed no statistically significant differences in ratings across the four writer groups (p = .358). The intraclass correlation coefficient was .147.
Conclusion: The proliferation of AI adds to prevailing questions about the value personal statements and essays have in supporting applicant selection. We speculate that these assessments hold less value than ever in providing authentic insight into applicant attributes. In this context, we suggest that medical schools move away from the use of essays in their admissions processes.
Résumé
Contexte : Les demandes d'admission dans les facultés de médecine exigent souvent de courtes lettres de motivation écrites ou des lettres de présentation, qui devraient être utilisées pour évaluer les qualités professionnelles liées à la pratique de la médecine. Les outils génératifs d'intelligence artificielle (IA) étant capables de compléter ou de remplacer les données fournies par les candidats humains, l'impact de ces outils sur les évaluations écrites suscite de plus en plus d'inquiétudes. Cette étude explore l'influence de l'IA sur l'évaluation des lettres de motivation utilisées pour les admissions dans les facultés de médecine.
Méthodes : Un plan expérimental à l'intérieur d'un sujet a été utilisé. Huit participants (cliniciens universitaires, chercheurs de la faculté, étudiants en médecine et un membre de la communauté) ont évalué des lettres de motivation rédigées par 24 étudiants de premier cycle et diplômés récents de l'Université McMaster. Les étudiants ont été répartis en quatre groupes : les aspirants à la faculté de médecine avec l'aide de l'IA (ASP-IA), les aspirants sans l'aide de l'IA (ASP), les non-aspirants avec l'aide de l'IA (NASP-IA), et les lettres de motivation générées uniquement par ChatGPT 3.5 (IA-UNIQUEMENT). Les participants ont reçu une formation pour l'application de l'échelle de Likert unique avant d'évaluer. Les différences d'évaluation selon le groupe de rédacteurs ont été déterminées au moyen d'une ANOVA à sens unique entre les groupes.
Résultats : Les analyses n'ont révélé aucune différence statistiquement significative dans les évaluations entre les quatre groupes de rédacteurs (p = 0,358). Le coefficient de corrélation intraclasse était de 0,147.
Conclusion : La prolifération de l'IA renforce les questions qui se posent présentement sur la valeur des lettres de présentation et des lettres de motivation dans la sélection des candidats. Nous supposons que ces évaluations ont moins de valeur que jamais pour ce qui est de fournir un aperçu authentique des attributs des candidats. Dans ce contexte, nous suggérons que les facultés de médecine abandonnent l'utilisation des lettres de motivation dans leurs processus d'admission.
Appendices
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