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Mastering the machine: how prompt engineering transforms generative AI learning
Conference proceeding   Peer reviewed

Mastering the machine: how prompt engineering transforms generative AI learning

Haiat Perozzo, Julian Sengewald and Aurelio Ravarini
Americas conference on information systems (AMCIS 2025): vol 4, p.2427
Intelligent technologies for a better future: AMCIS 2025 (Montreal, Canada, 14/08/2025–16/08/2025)
2025

Abstract

This study explores how prompt engineering training influences student performance and adaptability when using Generative AI (GenAI) chatbots in education. Focusing on first-year engineering management students, we examine the impact of targeted instruction on learning outcomes, linking AI interaction to academic self-efficacy, goal orientation, self-monitoring, study skills, and technology engagement. Using a mixed-methods approach, results show that prompt engineering significantly enhances academic performance and critical engagement with AI tools. Findings highlight the importance of integrating AI literacy into curricula to maximize GenAI’s educational potential and prepare students for AI-driven professional environments.

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