College Composition and Communication Sep 2025

Using the AI Life Cycle to Unblackbox AI Tools: Teaching Résumé 2.0 with Résumé Analytics and Computational Job-Résumé Matching

Huiling Ding

Abstract

In response to disruptions introduced to the job market by AI resume screeners, this article introduces a novel theoretical framework for the life cycle of artificial intelligence systems to help unblackbox resume screening AI systems. It then applies the AI life cycle framework to a digital case study of RChilli’s job-resume matching algorithm. The article introduces an eleven-step computational job-resume matching assignment that writing instructors can use in their classrooms to explore the pedagogical implications offered by the AI life cycle framework. The assignment helps students simulate important phases in AI production and development while highlighting biases and ethical concerns in AI screening of resumes. By exploring job-resume analytics, this study helps to teach critical AI and data literacy, make job-resume matching algorithms more explainable, and transform how professional writing can be taught in the age of automated hiring.

Journal
College Composition and Communication
Published
2025-09-01
DOI
10.58680/ccc2025771112
CompPile
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Open Access
Closed
Topics
writing pedagogy teacher development professional writing labor and working conditions qualitative research artificial intelligence literacy studies editorial matter
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