MASEM Dataset on Educational AI Technology Adoption among Students（from 2020 to May 2025）
Description
This dataset supports a meta-analytic structural equation modelling (MASEM) study investigating the factors influencing students’ behavioural intention to use educational AI (EAI) technologies. The research integrates constructs from the Technology Acceptance Model (TAM), Theory of Planned Behaviour (TPB), and Artificial Intelligence Literacy (AIL), aiming to resolve inconsistencies in previous studies and improve theoretical understanding of EAI technology adoption. Research Hypotheses The study hypothesized that: Students’ behavioural intention (INT) to use EAI technologies is influenced by perceived usefulness (PU), perceived ease of use (PEU), attitude (ATT), subjective norm (SN), and perceived behavioural control (PBC), as described in TAM and TPB. AI literacy (AIL) directly and indirectly predicts PU, PEU, ATT, and INT. These relationships are moderated by contextual factors such as academic level (K–12 vs. higher education) and regional economic development (developed vs. developing countries). What the Data Shows The meta-analytic dataset comprises 166 empirical studies involving over 69,000 participants. It includes pairwise Pearson correlations among seven constructs (PU, PEU, ATT, SN, PBC, INT, AIL) and is used to compute a pooled correlation matrix. This matrix was then used to test three models via MASEM: A baseline TAM-TPB model, An internal-extended model with additional TPB internal paths, An AIL-integrated extended model. The AIL-integrated model achieved the best fit (CFI = 0.997, RMSEA = 0.053) and explained 62.3% of the variance in behavioural intention. Notable Findings AI literacy (AIL) is the strongest predictor of intention to use EAI technologies (Total Effect = 0.408). PU, ATT, and SN also significantly influence intention. The effect of PEU on intention is fully mediated by PU and ATT. Moderation analysis showed that the relationships differ between developed and developing countries and between K–12 and higher education populations. How the Data Can Be Interpreted and Used The dataset includes bivariate correlations between variables, publication metadata, sample sizes, coding information, and reliability values (e.g., CR scores). Suitable for replication of MASEM procedures, moderation analysis, and meta-regression. Researchers may use it to test additional theoretical models or assess the influence of new moderators (e.g., AI tool type). Educators and policymakers can leverage insights from the meta-analytic results to inform AI literacy training and technology adoption strategies.
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How the Data Was Gathered and How the Research Can Be Reproduced This dataset was constructed as part of a meta-analytic structural equation modelling (MASEM) study aimed at investigating the factors influencing students’ behavioural intention to use educational AI (EAI) technologies. The process followed these systematic steps: 1. Literature Search and Screening We conducted a comprehensive search across multiple academic databases, including ScienceDirect, Emerald, SAGE, SpringerLink, Wiley, IEEE Xplore, Taylor & Francis, Google Scholar, Web of Science, and Scopus, covering literature from 2010 to May 2025. We used structured search terms combining: ("artificial intelligence literacy" OR "AI literacy") AND ("educational AI" OR "AI in education") AND ("intention to use" OR "behavioural intention") AND ("students" OR "learners") After removing duplicates and applying inclusion/exclusion criteria, a total of 166 independent empirical studies were retained from an initial pool of over 5,000 results. 2. Inclusion Criteria Studies were included if they: Used quantitative methods involving student populations (K–12 or higher education); Reported at least one Pearson correlation coefficient between TAM, TPB, or AI literacy constructs; Included sample size data; Were published in English. Studies using unrelated theoretical models (e.g., TRA, ECM-ISC) or reporting insufficient statistical information were excluded. 3. Coding Protocol Two trained researchers independently coded all included articles. Coding involved: Effect sizes (Pearson’s r), Construct reliability (CR, α), Sample characteristics (education level, country/region), Publication metadata. Discrepancies were resolved through discussion. Only correlations supported by at least three studies were included to ensure statistical reliability for the MASEM stage. 4. Meta-Analysis Procedures Effect sizes (r) were synthesized using Comprehensive Meta-Analysis (CMA) v3.3.0.7. Corrections for measurement error were applied using reliability coefficients. Both fixed-effect and random-effects models were tested. Fisher’s Z transformations were performed, and harmonic mean sample size was calculated for structural modeling. Heterogeneity was assessed using the Q statistic and I² index. 5. MASEM Modelling Using the pooled correlation matrix, we conducted structural model testing in AMOS 29.0 across three models: A TAM-TPB baseline model, An internally extended model, An AI Literacy–integrated extended model. Model fit was evaluated with CFI, RMSEA, SRMR, TLI, AIC, and ECVI, and parameter inputs were adjusted using CR-based estimates for each construct. 6. Reproducibility The full correlation matrix, CR values, and coded study metadata are included in the dataset. Future researchers can replicate or extend the study using MASEM techniques, incorporating additional moderators (e.g., age, AI tool type) or sub-constructs of AI literacy.