Supplementary file 1_From initial trust to critical reconstruction: upper secondary studen

A 59.0 KB qualitative dataset from a 2026 study by Chun Zhou, examining how 21 upper secondary students in Southwest China engage with generative AI for science learning. The data, shared under a CC-BY-4.0 license, documents a process from initial trust to critical reconstruction of AI-generated content. It is based on semi-structured interviews with students aged 15-18.

Use Cases

• Analyzing student strategies for evaluating AI-generated content based on described processes like internal consistency checking and external corroboration.
• Modeling the factors influencing AI adoption in education based on mentioned variables like task demands, prior knowledge, and teacher norms.
• Studying the evolution of student trust in AI tools based on the described transition from provisional trust to critical reconstruction.

Strengths

• Data is derived from semi-structured interviews with 21 participants, providing qualitative depth.
• The study design is explicitly described as a constructivist grounded theory approach.
• The dataset is openly shared under a permissive CC-BY-4.0 license.

Limitations

• The dataset is very small at 59.0 KB, indicating limited scope.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.

Provenance

Source

Chun Zhou via figshare.

Collection Method

Semi-structured interviews conducted as part of a constructivist grounded theory study.

Time Range

Study published in 2026; specific data collection period is not stated.

Freshness

Last updated 2026-05-22 06:10:13; freshness should be verified.

Geography

Data collected from two upper secondary schools in Southwest China.