Actionable companion · Assessment and evidence of learning
From evidence to district action: Perceived GenAI competency and university teachers’ intention to continue using generative artificial intelligence: Extending the technology acceptance model
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:
Generative artificial intelligence (GenAI) is reshaping higher education, and understanding teachers’ intention to continue using GenAI is increasingly important in this context. Grounded in the technology acceptance model (TAM), this study examines how different dimensions of teachers’ perceived GenAI competency are associated with perceived ease of use (PEOU), perceived usefulness (PU), and GenAI use continuance intention (GenAI-UCI). Using survey data from 615 university teachers in Qingdao, China, structural equation modeling was employed to examine the hypothesized relationships among perceived GenAI competency dimensions, TAM-based beliefs, and continuance intention. Results showed that perceived GenAI competency dimensions related to teaching, research, and professional engagement were positively associated with PU, whereas perceived basic understanding showed a small and non-robust association with PEOU. PU was positively associated with GenAI-UCI, whereas PEOU showed no significant direct association with GenAI-UCI. Overall, the findings suggest that perceived GenAI competency is not a homogeneous capability; rather, different competency dimensions were associated with different TAM-based beliefs within the proposed model. The findings highlight the importance of teachers’ perceptions of GenAI usefulness in their professional practices for understanding continuance intention in higher education.
A 90-day district pilot
- 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.
- 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.
- 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.
- 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
Qiusu Wang. Perceived GenAI competency and university teachers’ intention to continue using generative artificial intelligence: Extending the technology acceptance model. PLOS One (2026-09-17). https://doi.org/10.1371/journal.pone.0358596
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.