Shuling Wei's dataset on figshare summarizes a systematic review of 25 studies evaluating digital affordances of AI chatbots in nursing education. The review, employing affordance theory and a taxonomy of learning gains, identifies affordances like assistance provision and personalization, while noting gaps in evidence for others like facilitation and privacy. The dataset was last updated on April 24, 2026.
Use Cases
- Analyzing the relationship between specific digital affordances (e.g., personalization) and learning gains (cognitive, affective, behavioral) based on the review findings.
- Identifying gaps in empirical evidence for certain affordances (facilitation, privacy) to guide future research directions.
- Comparing the geographical distribution and methodological characteristics (study design, sample size, duration) of existing studies in the field.
Strengths
- Summarizes findings from 25 identified studies, providing a concrete scope.
- Explicitly lists reported digital affordances (assistance provision, personalization, etc.) and those lacking empirical support.
- Categorizes learning gains evidence into cognitive, affective, and behavioral domains with specific examples.
Limitations
- Row count and column-level documentation are unknown, limiting assessment of internal data structure.
- The dataset is very small (25.4 KB), suggesting it contains summary data rather than raw study data.
- The systematic review notes the evidence remains inconclusive for certain learning gains and that studies had short durations and limited sample sizes.
Provenance
- Source
- Shuling Wei
- Collection Method
- Systematic review employing affordance theory and a taxonomy of learning gains.
- Freshness
- Last updated 2026-04-24 04:23:40
- Geography
- Studies were mainly in Asia, according to the review.