Cognitive Transitions in Human-AI Collaboration: Survey Data from 316 Participants

A figshare dataset by Xiaohui Shao, last updated April 2026, examines mechanisms of cognitive transitions in human-AI collaboration within higher education. The dataset likely contains survey or experimental data from 316 participants in an authentic instructional context, analyzed using fuzzy-set qualitative comparative analysis (fsQCA). It constructs a configurational model incorporating social interaction, tacit knowledge acquisition, internalization, self-motivation, and trust in AI.

Use Cases

• Identify equifinal pathways to cognitive transitions based on configurational conditions like social interaction and trust in AI.
• Analyze compensatory mechanisms where AI offsets deficiencies in human-centered conditions.
• Validate the application of fsQCA methodology for studying complex cognitive processes in educational settings.
• Explore the role of tacit knowledge acquisition and internalization in human-AI collaborative learning.

Strengths

• Data collected from 316 participants provides a substantive sample for analysis.
• Analysis employs fsQCA, a method suited for identifying complex, non-linear causal configurations.
• Study is grounded in an authentic instructional context, enhancing ecological validity.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The 52.3 KB file size suggests a limited scope, likely containing summary results or a supplementary document rather than raw data.

Provenance

Source

figshare

Collection Method

Data collected from 316 participants in an authentic instructional context.

Freshness

Last updated 2026-04-29 05:57:36; freshness should be verified.