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Journal of Information Systems Education

Abstract

This teaching tip presents a step-by-step module that enables information systems students to perform text mining directly in Microsoft Excel using Microsoft Copilot. The approach lowers technical barriers by allowing natural language prompts to generate structured outputs, while maintaining a focus on interpretation and managerial communication. The module scaffolds eight techniques, progressing from word frequency analysis and sentiment classification to visualization, topic modeling, anomaly detection, correlation analysis, semantic similarity, and simple predictive modeling. Activities use a classroom-sized subset of the Amazon Customer Reviews Dataset, providing authentic text with rich metadata. Ready-to-use prompts, screenshots, and instructor notes highlight common pitfalls and demonstrate how prompt refinement improves results. The module offers an accessible pathway for integrating AI-enabled text analytics into IS courses, fostering both technical competence and critical evaluation of AI outputs.

DOI

https://doi.org/10.62273/KRAV4122

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