Prompting for products more than pedagogy: Prompt styles of elementary school teachers generating plane-figure questions with ChatGPT
DOI:
https://doi.org/10.59965/pijme.v5i1.366Keywords:
ChatGPT, Elementary school teachers, Prompt engineering, Question generation, Teacher professional developmentAbstract
Background: Teachers increasingly use ChatGPT to generate mathematics questions, yet their prompting and verification practices remain poorly understood.
Purpose: This study examined elementary teachers’ prompting styles and verification practices when generating plane-figure questions with ChatGPT.
Method: Ten teachers from one public school in Depok, Indonesia, participated in a single session. Conversations underwent content analysis using four prompting strategies, eight specification elements, and follow-up classifications. Prompt profiles were derived through explicit rules, and generated items were reviewed for mathematical correctness.
Results: Nine opening prompts were analysable. All were brief, imperative, and zero-shot, with a median length of 14 words. Output customisation mainly concerned quantity, question type, and format. Only three prompts specified grade level and three specified cognitive level; none included learning objectives, student profiles, or examples. Four follow-up requests concerned additional items or formatting, without requesting verification or justification. One of ten fully reviewed multiple-choice items lacked a correct option. Four profiles emerged: level-labelled specification, format-focused, open delegation, and answer-key oriented.
Implications: Professional development should strengthen pedagogical specification and verification of AI-generated questions.
Contribution: The study identifies four prompting profiles and proposes a specify–generate–verify–revise cycle to support teachers’ critical engagement with ChatGPT, although its effectiveness remains untested.
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