Leveraging AI for Data-Informed Resume Writing

A Pedagogical Approach

Abstract

This article presents a pedagogical framework for teaching data-informed résumé writing in an AI-driven hiring environment. As applicant tracking systems (ATS) increasingly mediate between job seekers and human reviewers, students must learn to write résumés that communicate effectively with both audiences. The framework introduces four stages: viewing text as structured data, gathering job postings through web scraping, analyzing data with large language models (LLMs), and applying insights to revise résumés. This approach not only prepares students to navigate AI gatekeepers but also develops transferable AI literacy skills, fostering critical engagement with emerging technologies in professional communication.