Errori-Attrib-AILit: User AI Literacy and ChatGPT Error Attribution

An exploratory study by Maurizio Norcia examines associations between user AI literacy and the causal explanations they provide for incorrect outputs from the ChatGPT chatbot. The dataset is stored in an 84.5 KB XLSX file and was last updated on May 8, 2026. It is shared under a CC-BY-4.0 license on figshare.

Use Cases

• Analyze correlations between AI literacy levels and types of causal explanations based on the study's focus.
• Train models to predict user attribution styles based on demographic or literacy features suggested by the description.
• Benchmark user trust and understanding of generative AI errors for comparative studies.

Strengths

• Dataset is small (84.5 KB), facilitating quick download and initial exploration.
• Explicitly licensed as CC-BY-4.0, allowing for broad reuse and sharing.
• Focus on a specific, timely research question linking AI literacy and error attribution.

Limitations

• Row count is unknown, which may limit suitability assessment for statistical analysis.
• Column-level documentation is absent; field semantics must be inferred after download.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

figshare

Collection Method

Likely collected via an exploratory user study, as described.

Freshness

Last updated 2026-05-08 10:27:29; freshness should be verified.