Generative AI acceptance in study-travel contexts: an integrative model of affective and utilitarian pathways with...

Generative artificial intelligence is progressively reshaping study-travel-a curriculum-embedded educational practice. Yet, the psychological processes governing upper-primary and middle-school students' adoption of such technology in this context remain underexplored. Prior technology acceptance inquiries have predominantly centered on adult populations, leaving adolescent cognitive-developmental contingencies unaddressed.

This investigation aimed to elucidate the differential roles of affective versus utilitarian drivers in shaping study-travel participants' anticipated intentions to adopt GenAI, and to determine whether these pathways differ by grade level.

We constructed an integrative model grounded in the extended Unified Theory of Acceptance and Use of Technology (UTAUT2), supplemented by Piaget's developmental framework. The model incorporated perceived enjoyment as a mediator and perceived risk as a direct negative antecedent, with grade level specified as a moderator of selected pathways. Survey data from 741 students in grades 4-9, drawn from twelve schools across six Chinese cities, were analyzed using structural equation modeling, multi-group comparisons, and cluster-robust standard errors to account for the nested sampling structure. Measurement invariance across grade groups was established prior to structural comparisons.

Perceived enjoyment emerged as the largest estimated predictor of adoption intention in this sample (β = 0.43, p < 0.001), surpassing performance expectancy (β = 0.30, p < 0.001). Enjoyment fully mediated social influence effects and partially transmitted performance expectancy's influence. Perceived risk exerted a significant suppressive effect (β = -0.24, p < 0.001), with primary students exhibiting approximately twice the inhibitory magnitude of middle-school counterparts. Grade significantly moderated the performance expectancy-intention relationship, and provided borderline evidence for moderation of the risk-intention relationship, indicating systematic grade-related differences in how these pathways operate.

These findings advance technology acceptance theory by introducing grade-related contingency and establishing an "affective-efficacy" dual-channel model. Practical implications for designing grade-differentiated educational AI products are discussed.