Generative AI · Education leadership

Actionable companion · Assessment and evidence of learning

From evidence to district action: Generative Artificial Intelligence Use and Learning Engagement Among Medical Undergraduates: Statistical Indirect and Configurational Associations Involving Basic Psychological Need Satisfaction

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

Health-professions education context: test transfer to K–12 rather than assuming it.

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

Abstract As generative artificial intelligence (GAI) becomes increasingly integrated into undergraduate medical education, its relationship with students’ learning engagement warrants further investigation. Guided by self-determination theory, this cross-sectional questionnaire study surveyed 498 medical undergraduates at a medical university in China and used structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) to examine average associations and configurational patterns among GAI use, basic psychological need satisfaction, and learning engagement. SEM results showed that GAI use was positively but weakly associated with basic psychological need satisfaction, whereas basic psychological need satisfaction was more strongly associated with learning engagement. The direct association between GAI use and learning engagement was not statistically significant, while the statistical indirect association through basic psychological need satisfaction was significant but small. The fsQCA results showed that high GAI use was not a necessary condition for high learning engagement. The configuration combining autonomy, competence, and relatedness need satisfaction had the highest coverage and showed relatively stable results across threshold adjustments, with competence need satisfaction as a core condition. These findings suggest that the educational relevance of GAI use may be better understood in relation to students’ psychological need conditions than to use frequency alone. Keywords: generative artificial intelligence, medical undergraduates, learning engagement, basic psychological need satisfaction, fuzzy-set qualitative comparative analysis

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

Qian He; Guanglei Chang; Ning Su; Yingying Yang; Jun Ma. Generative Artificial Intelligence Use and Learning Engagement Among Medical Undergraduates: Statistical Indirect and Configurational Associations Involving Basic Psychological Need Satisfaction. Behavioral Sciences (2026-09-20). https://doi.org/10.3390/bs16091694

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.