Generative AI · Education leadership

Actionable companion · Assessment and evidence of learning

From evidence to district action: The relationship between hope and learning engagement among Chinese university students: the serial mediating roles of generative AI acceptance and self-directed learning

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:

Abstract The rapid development of generative artificial intelligence (GenAI) has transformed higher education and created new opportunities for student learning. However, the psychological and behavioral factors associated with students’ learning engagement in AI-supported learning environments remain insufficiently understood. Drawing upon Hope Theory, the Technology Acceptance Model (TAM), and Self-directed Learning Theory, this study proposed a serial mediation model to examine the serial mediating roles of generative AI acceptance and self-directed learning in the association between hope and learning engagement. Data were collected from 478 Chinese university students via an online questionnaire. The data were analyzed using SPSS (Version 26.0) and AMOS (Version 24.0), and structural equation modeling (SEM) was employed to evaluate the proposed model. The results showed that hope was positively associated with learning engagement. Moreover, generative AI acceptance and self-directed learning each demonstrated significant indirect associations in the relationship between hope and learning engagement. A significant serial indirect association through generative AI acceptance and self-directed learning was also observed. These findings are consistent with a psychological–technological–behavioral framework linking hope, generative AI acceptance, self-directed learning, and learning engagement in AI-supported learning environments. The study contributes to the application of Hope Theory in the context of generative AI-supported education, advances the growing literature on generative AI acceptance in higher education, and provides practical implications for fostering students’ learning engagement through the effective integration of GenAI and self-directed learning. Keywords: generative AI acceptance, hope, learning engagement, self-directed learning, university student

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

Yinshun Zhang; Zimin Wang; Huafu Xu. The relationship between hope and learning engagement among Chinese university students: the serial mediating roles of generative AI acceptance and self-directed learning. Frontiers in Psychology (2026-08-24). https://doi.org/10.3389/fpsyg.2026.1935139

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.