education3 papersavg year 2026quality 6/5weak evidence

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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