Survey Data on Generative AI Learning Pathways

Juan Chen's dataset, updated in April 2026, supports research on how individuals learn to use Generative AI. It examines dual pathways: cognitive-utility factors like task-technology fit and socio-affective factors like emotion and appraisal. The data likely contains survey measures related to behavioral intention and learning outcomes.

Use Cases

• Modeling behavioral intention to use GenAI based on cognitive utility and socio-affective variables.
• Analyzing the relationship between task-technology fit appraisal and learning outcomes.
• Investigating the role of emotion as a pathway to technology learning and adoption.

Strengths

• Dataset size is explicitly stated as 56,939 units across two sources.
• Released under a permissive CC-BY-4.0 license, facilitating reuse.
• Cross-platform presence on Figshare and Dataverse indicates established sharing and potential for visibility.

Limitations

• Column names and specific variable definitions are not provided in any source, limiting immediate usability.
• Conflicting metadata: one source lists 'Harvard Dataverse' as the organization, while another does not specify an organization.
• No information on data collection method, time range, or participant demographics is provided.

Provenance

Source

Juan Chen

Collection Method

null

Time Range

null

Freshness

Last updated 2026-04-09

Geography

null