Understand the factors that influence students' adoption

The gap

Evidence profile

Sourced from the stated research gap and future work of the source papers, classified as general, drawn from work published between 2025 and 2026, spanning 2 journals. Those papers have been cited 8 times in total.

Supporting evidence — 3 representative gaps

• Predicting STEM students' adoption of generative AI in academic contexts: an application of the UTAUT model (2025) · Frontiers in Education · cited 8× · doi

  There is a need to understand the factors that influence students' adoption of generative AI in academic contexts. Prior work has not fully explored the application of the Unified Theory of Acceptance and Use of Technology to generative AI adoption. There is a gap in the literature on the role of social influence and facilitating conditions in predicting behavioral intention to adopt generative AI.

  generalstated research gap
  Keywords: there need understand factors influence students adoption generative
• The Influence of Generative AI Usage Styles on Creative Self-Beliefs: Findings from a Longitudinal Study in Design Education (2026) · doi

  The findings of this study highlight the limitations of understanding the educational effects of generative AI solely in terms of its use or non-use, and suggest that its impact depends on usage style and the nature of the human–AI relationship. Although generative AI was primarily perceived as a tool for improving task efficiency, usage styles such as feedback provision and meaning summarization were associated with increases in CSE. However, this study has several limitations. First, the analysis was based on a single course context, and the sample was limited to 64 participants who completed all measurements; therefore, caution is required when generalizing the findings. Second, in the analysis by AI usage style, some categories included only a small number of participants, and thus, the results should be considered exploratory. Finally, the study was relatively short, and it is possible that the relationship between users and AI had not yet been sufficiently established. Future research should adopt a longer-term longitudinal approach to examine how interactions with generative AI evolve over time and how these changes influence CSB. In addition, incorporating factors such as perceived control and sense of agency toward AI into measurement frameworks may enable a more nuanced understanding of how human–AI relationships shape creativity. CRediT authorship contribution statement Riku Okamoto: Conceptualization, Methodology, Investigation, Formal analysis, Visualization, Writing – original draft, Writing – review & editing. Akiyoshi Inasaka: Conceptualization, Supervision, Resources, Investigation, Writing – review & editing.

  generalfuture workevidence 5/5
  Keywords: generative usage writing limitations understanding style human relationship perceived participants conceptualization investigation review editing highlight
• Acceptance of generative artificial intelligence among adults: A cross-sectional study of UTAUT-based dimensions and group differences (2026) · International Journal of Changes in Education · doi

  expectancy, The present study examined reported generative AI acceptance among 460 adults. The analysis was organized around four UTAUT-based dimensions measured using the GAIAS: performance expectancy, facilitating conditions, and social influence. The results provide descriptive and comparative evidence concerning participants’ perceptions of generative AI. They do not constitute direct evidence of behavioral intention, sustained adoption, or actual use. A number of major findings may be identified from the analysis. effort First, participants reported moderate to high overall acceptance of generative AI applications. In addition, approximately three quarters of the sample indicated that they used generative AI tools at least occasionally, mainly for everyday tasks, education, and work-related purposes. These findings suggest that generative AI was familiar to many participants in this digitally connected convenience sample. frequency should not be However, self-reported use Konti & Kotsis / International Journal of Changes in Education, 4(1), em110 15 / 18 interpreted as evidence of sustained adoption, intensive use, or mainstream integration within the wider adult population. particularly because only 19 participants reported not owning a personal computer. reported acceptance was more Second, the four GAIAS dimensions displayed a clear descriptive ordering. Effort expectancy, reflecting PEOU, received the highest mean score, followed by performance expectancy and facilitating conditions, while social influence received the lowest mean score. This pattern indicates that participants’ closely characterized by favorable perceptions of usability and usefulness than by perceived social encouragement. Because the study did not model behavioral intention or actual adoption, the findings should not be interpreted as demonstrating why adults adopt generative AI or as establishing that usability directly causes adoption. The predominance of effort expectancy especially draws attention to the important influence of usability and interface design on public views towards AI technologies. Third, several educational, and demographic, technological group differences were examined. No statistically significant differences between women and men were identified in overall generative AI acceptance or in any of the four GAIAS dimensions. However, this result should be interpreted cautiously because women and men were represented unequally in the sample, and the three participants in the “other/prefer not to answer” category were excluded from the inferential gender comparisons. Age showed the most consistent pattern of group differences in the separate analyses, particularly for effort expectanc

  generalfuture workevidence 5/5
  Keywords: generative participants expectancy reported acceptance adoption effort four dimensions gaias social influence evidence sample interpreted

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