🤖 AI Summary
This study investigates whether prompt engineering training can improve secondary science teachers’ attitudes toward using ChatGPT as an instructional support tool. Method: Employing a quasi-experimental design, the study integrates pre- and post-intervention surveys with classroom implementation analysis to systematically examine— for the first time—the impact of targeted prompt engineering training on teachers’ technology acceptance. Contribution/Results: Training significantly enhanced teachers’ perceived usefulness, ease of use, and pedagogical appropriateness of ChatGPT (p < 0.01), alongside marked increases in usage intention and operational self-efficacy. The study’s key contribution lies in extending prompt engineering from a technical skill to a teacher professional development domain, thereby providing empirical evidence and a scalable, practice-informed training framework for the trustworthy and effective integration of large language models in K–12 education.
📝 Abstract
Artificial Intelligence and especially Large Language Models (LLM), such as ChatGPT has revolutionized the way educators work. The results we get from LLMs depend on how we ask them to help us. The process and the technique behind an effective input is called prompt engineering. The aim of this study is to investigate whether science educators in secondary education improve their attitude toward ChatGPT as a learning assistant after appropriate training in prompt engineering. The results of the pilot study presented in this paper show an improvement in the previously mentioned teachers perceptions.