35 pre-test-post-test designs and 37 post-test studies synthesize evidence on AI-enabled teaching. The dataset contains meta-analytic results, including effect sizes (g_p = 0.586, g_delta = 0.136), examining boundary conditions and moderating factors. Author Jie Cai published the analysis on figshare in April 2026.
Use Cases
- Analyze the impact of AI type on teaching effectiveness based on the heterogeneity analysis mentioned in the description
- Evaluate methodological factors influencing AI intervention outcomes based on the minor influence noted
- Support teacher role reconfiguration strategies based on the empirical insights provided
- Guide the enhancement of teachers' digital competencies based on the discussion of personalized and adaptive learning
Strengths
- Results synthesized from 35 pre-test-post-test designs and 37 post-test studies
- Effect sizes reported (g_p = 0.586, g_delta = 0.136)
- Low publication bias and robust analyses noted in the description
- Published under a CC-BY-4.0 license
Limitations
- Column-level documentation is absent; field semantics must be inferred after download
- Row count is unknown, which may limit suitability assessment
- The dataset is 11.8 KB, indicating a very limited scope likely containing summary statistics rather than raw study data
Provenance
- Source
- figshare
- Collection Method
- Meta-analytic review synthesizing existing studies
- Freshness
- Last updated 2026-04-29 05:57:31