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Learners leveraging generative AI for creative problem solving: Focusing on the PISA 2022 creative thinking problems
Learners leveraging generative AI for creative problem solving: Focusing on the PISA 2022 creative thinking problems
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Learners leveraging generative AI for creative problem solving: Focusing on the PISA 2022 creative thinking problems
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Learners leveraging generative AI for creative problem solving: Focusing on the PISA 2022 creative thinking problems
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Learners leveraging generative AI for creative problem solving: Focusing on the PISA 2022 creative thinking problems
Learners leveraging generative AI for creative problem solving: Focusing on the PISA 2022 creative thinking problems
Journal Article

Learners leveraging generative AI for creative problem solving: Focusing on the PISA 2022 creative thinking problems

2025
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Overview
As STEM education evolves to meet the demands of the 21st century, fostering creativity and problem-solving skills has become a critical objective. This study examines how learners approach STEM problems requiring creative thinking with generative AI (GenAI). The research explores two key questions: (a) How do students engage in the creative problem solving (CPS) process using GenAI across social and scientific problem domains? (b) What differences exist between high- and low-performing students in using prompts during CPS with GenAI? The study involved 38 middle school students (aged 15) in Korea, who solved social and scientific problems from the PISA 2022 Creative Thinking Assessment using ChatGPT. The findings revealed distinct patterns in students' use of GenAI, varying by problem domain and performance level. In the social problem domain, students primarily relied on ChatGPT's suggestions, frequently using prompts to request fully developed solutions or elaboration on ChatGPT's ideas. In contrast, in the scientific problem domain, students developed problem solving strategies around their ideas and integrated ChatGPT's suggestions in a more balanced way. A detailed analysis of prompt strategies revealed prominent differences between high- and low-performing students. High-performing students used diverse prompt types and strategically combined them with their own insights. In contrast, low-performing students relied heavily on ChatGPT's suggestions with minimal modification or originality. This study contributes to understanding how learners engage with GenAI for CPS across different domains and provides implications for designing educational strategies that equip learners to utilize GenAI effectively for CPS in STEM education.
Publisher
International Forum of Educational Technology & Society,International Forum of Educational Technology & Society, National Taiwan Normal University, Taiwan,International Forum of Educational Technology & Society