Longitudinal studies or mixed-method approaches having qualitative data collection and interviews could provide deeper perceptions into the growing patterns of AI embracing with the passage of time.
Research gap analysis derived from 3 education papers in our local library.
The gap
Longitudinal studies or mixed-method approaches having qualitative data collection and interviews could provide deeper perceptions into the growing patterns of AI embracing with the passage of time.
Consensus across the literature
Clustered from 3 gap mentions across 3 papers via embedding cosine ≥ 0.62.
Research trend
Established — well-defined area with open sub-problems.
Supporting evidence — 3 representative gaps
- Towards the integration of artificial intelligence in Jordanian journalism practices from the UTAUT model perspective (2026) · doi
Longitudinal studies or mixed-method approaches having qualitative data collection and interviews could provide deeper perceptions into the growing patterns of AI embracing with the passage of time.
Keywords: longitudinal mixed approaches having qualitative collection interviews provide deeper perceptions growing patterns embracing passage time - Exploring Chinese EFL teachers’ adoption of generative AI: the roles of social influence and AI-PACK (2026) · doi
As this study relied on cross-sectional quantitative data, it cannot fully capture the dynamic evolution of teachers' intentions over time. Subsequent research should employ longitudinal designs or mixed-method approaches, triangulating quantitative data with qualitative interviews to provide deeper insights into the causal mechanisms of AI adoption.
Keywords: quantitative relied cross sectional cannot fully capture dynamic evolution teachers intentions time subsequent employ longitudinal - The effects of responsiveness, perceived warmth, and anthropomorphism on university students' use of conversational AI for learning support: a chain mediation analysis based on S-O-R framework (2026) · doi
The study employed only cross-sectional SEM design without qualitative data collection methods such as in-depth interviews, observational data, or focus group discussions. Future research should integrate mixed-method designs combining survey data with qualitative approaches to comprehensively uncover the psychological dynamics and cognitive processes underlying conversational AI usage for learning support.
Keywords: structural equation modeling cross-sectional design qualitative interviews conversational AI learning engagement
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