AI Insight
A constructivist grounded theory study examined how 21 upper secondary students aged 15 to 18 in Southwest China engaged with generative AI tools during science learning. Students typically began with efficiency-driven, provisional trust in AI-generated content before progressing to evaluative strategies such as internal consistency checks, external corroboration, and recognition of AI limitations. Based on these assessments, students adapted their behavior by refining prompts, selectively reconstructing content, or discontinuing AI use when outputs proved unreliable, a process the authors characterize as regulated epistemic engagement.
Why it matters
Understanding how adolescent learners critically evaluate and reconstruct AI-generated content has direct implications for designing science curricula and pedagogical guidelines that support responsible and effective AI integration in secondary education.
IntroductionThis study examines how upper secondary students engage with generative artificial intelligence (GenAI) in science learning, focusing on how they move from initial trust to critical reconstruction of AI-generated content.MethodsUsing a constructivist grounded theory design, the study drew on semi-structured interviews with 21 students aged 15-18 from two upper secondary schools in Southwest China.ResultsThe findings show a dynamic process shaped by task demands, prior knowledge, time pressure, and teacher norms. Students often began with efficiency-driven use and provisional trust in fluent, authoritative responses. They then evaluated outputs through internal consistency checking, external corroboration, and growing awareness of GenAI’s limitations. Based on these judgements, students refined prompts, selectively reconstructed content, or abandoned GenAI when outputs were unreliable.DiscussionThe study argues that GenAI use in science learning is best understood as a form of regulated epistemic engagement.