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A game-changer for qualitative research: artificial intelligence as an efficient tool for analyzing student conceptions about microplastics
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Researchers found that AI can analyze student responses about microplastics just as accurately as human experts, but much faster. This breakthrough could help scientists and teachers study what people know about microplastics - tiny plastic particles that can harm our health - without spending months sorting through responses by hand. Better understanding of public knowledge about microplastics could lead to more effective health education and policy decisions.
Qualitative content analysis of learners’ conceptions is due to large datasets time-consuming. This study examined the potential of a Large Language Model (LLM) to support and accelerate qualitative content analysis without compromising validity. Written responses from 180 bachelor students at two German universities to four open-ended questions on microplastics were analysed. ChatGPT was used to inductively develop categories and to assign responses. Two human expert coders conducted the same procedures for comparison purposes. The inter-rater reliability was calculated using Cohen’s kappa and two independent ChatGPT runs were performed to test consistency. The categorization system generated by ChatGPT largely corresponded to the human-developed system. The two ChatGPT runs showed highly consistent classifications, with inter-rater reliabilities of up to κ = 0.96. That exceeded both intra- and inter-rater agreement of the human coders ( κ = 0.45–0.90). Overall, our findings suggest that LLMs can support valid and reliable qualitative content analysis while substantially reducing analysis time. In this respect, the use of LLMs may represent a methodological game-changer for qualitative research, making such approaches more efficient and accessible, also for classroom teachers.
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