Actionable companion · Instructional improvement

From evidence to district action: Generative AI acceptance in study-travel contexts: an integrative model of affective and utilitarian pathways with mediation and grade-level moderation

AI-assisted editorial implementation guide. The district actions are proposed applications, not findings or recommendations attributed to the original authors. No additional peer review is asserted.

Evidence and context

Confirm the study population, setting and task before applying its findings to your district.

Original authors’ abstract, reproduced under the linked source license:

Abstract Background 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. Objective 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. Method 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. Results 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. Conclusion 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. Keywords: adoption intention, generative AI, grade moderation, perceived enjoyment, perceived risk, study-travel, UTAUT2

A 90-day district pilot

1. Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
2. Days 16–30 · Design. Choose one instructional unit and a specific teacher-supervised AI use. Compare it with the existing approach using a common learning objective, equivalent tasks and a shared rubric. Name an accountable owner and set the baseline, success threshold and budget before implementation.
3. Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
4. Days 61–90 · Decide. Measure unaided learning on a new task, factual errors, student workload and teacher review time. Disaggregate results where group sizes permit responsible interpretation. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Yuan Li; Dexin Li. Generative AI acceptance in study-travel contexts: an integrative model of affective and utilitarian pathways with mediation and grade-level moderation. Frontiers in Psychology (2026-10-06). https://doi.org/10.3389/fpsyg.2026.1961221

Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.