Factors Influencing Gen Z Acceptance of AI-Powered Geography Teaching Materials: A Structural Equation Modeling Approach

M Fikri Amrullah; Rizki Nor Amelia

doi:10.37905/jgej.v7i2.39026

Factors Influencing Gen Z Acceptance of AI-Powered Geography Teaching Materials: A Structural Equation Modeling Approach

M Fikri Amrullah, Rizki Nor Amelia

Abstract

Artificial Intelligence (AI) is increasingly used in higher education, yet evidence regarding which perceived characteristics are most strongly associated with students’ experiences of AI-supported learning remains mixed, particularly in geography education. This cross-sectional study examined the associations of perceived ease of use, perceived usefulness, personalization and adaptivity, and learning interactivity with Generation Z prospective geography teachers’ perceived impact of AI-supported geography learning. Participants were 165 prospective geography teachers from one public university in Indonesia. Data were collected using a structured 4-point Likert questionnaire whose items referred to AI-supported digital tools in general rather than to a single named AI platform, and the model was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Learning interactivity showed the strongest positive association with I_AI (β = .303; p = .006), followed by perceived usefulness (β = .283; p = .017) and perceived ease of use (β = .255; p = .026). Personalization and adaptivity were not significantly associated with I_AI (β = −.051; p = .599). The model explained 53.3% of the variance in I_AI (R² = .533). These findings describe relationships among prospective geography teachers’ perceptions and do not establish the effectiveness of AI, actual learning gains, or causal effects. Within this single-university context, learning interactivity appeared more salient than the measured personalization and adaptivity constructs, while the nonsignificant associations of personalization and adaptivity with I_AI should be interpreted as a context-specific finding rather than as evidence of a general decline or “paradox” of personalization.

Keywords

Artificial Intelligence in Education; Generation Z; Geography Education; PLS-SEM; Technology Acceptance Model

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DOI: https://doi.org/10.37905/jgej.v7i2.39026