Generative AI · Education leadership

Actionable companion · Assessment and evidence of learning

From evidence to district action: Trust and Perceived Usefulness in GenAI-Assisted Assessment Adoption Among Design Teachers in Higher Education: Distinct Judgments and Their Associations with Behavioral Intention

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.

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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:

Generative artificial intelligence (GenAI) is increasingly being considered for assessment-related work, but educators need to judge whether it is reliable enough to serve as professional input and whether it is useful enough to justify its incorporation into assessment practice. This study examined whether trust (TR) and perceived usefulness (PU) are empirically distinct judgments and whether their independent associations with behavioral intention (BI) to use GenAI for assessment differ. Cross-sectional survey data from 352 design teachers at universities and higher vocational colleges in China were analyzed using confirmatory factor analysis, covariance-based structural equation modeling, comparisons of alternative structural specifications, and 5000-sample bootstrap tests. Measurement evidence supported treating TR and PU as separate latent judgments. In the correlated parallel model (Model C2), PU was positively associated with BI, whereas the TR–BI association was nonsignificant; a formal test of the unstandardized path coefficients showed that the PU–BI coefficient was significantly larger than the TR–BI coefficient. Because Model C2 was structurally just-identified, its global fit was not interpreted as independent evidence of structural robustness. Sensitivity analyses accounting for prior GenAI-assisted assessment use yielded the same substantive path pattern. The findings concern acceptance judgments and behavioral intention rather than actual adoption or assessment outcomes. They suggest that reliability and perceived task value are related but nonredundant considerations when educators consider GenAI support while retaining final assessment responsibility.

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. Redesign one low-stakes assessment: require a first attempt, an AI-assisted revision, a source-check log and a short oral explanation. Use the same rubric for the comparison task. 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. Blind-score reasoning, factual accuracy and unaided transfer on a fresh task. Record suspected misconduct separately from demonstrated evidence. 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

Jie Wang; Yuri Sim. Trust and Perceived Usefulness in GenAI-Assisted Assessment Adoption Among Design Teachers in Higher Education: Distinct Judgments and Their Associations with Behavioral Intention. Behavioral Sciences (2026-10-17). https://doi.org/10.3390/bs16101930

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.