Generative AI · Education leadership

Actionable companion · Assessment and evidence of learning

From evidence to district action: The relationship between college students' AI technology dependence and learning burnout: a chain mediation analysis of technology acceptance and AI self-efficacy

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 Objective Against the backdrop of the deep integration of generative artificial intelligence into higher education, the non-adaptive use of AI learning tools among college students has become a critical risk factor affecting learning psychology and academic adaptation. Methods Based on the Technology Acceptance Model (TAM) and Self-Efficacy Theory (SET), this study constructed a chain mediation model to examine the sequential mediating associations of technology acceptance and AI self-efficacy in the relationship between AI technology dependence and learning burnout. Results The results show that AI technology dependence is significantly positively associated with learning burnout, technology acceptance plays a significant independent mediating role between AI technology dependence and learning burnout, AI self-efficacy plays a significant independent mediating role between AI technology dependence and learning burnout, technology acceptance and AI self-efficacy form a significant chain mediation path. Conclusion This study clarifies the multi-level psychological transmission mechanism of AI technology dependence is related to learning burnout, enriches theoretical achievements in the fields of AI education application and learning burnout, and provides empirical basis and practical implications for universities to carry out AI literacy education, alleviate college students' learning burnout, and promote a healthy and sustainable human-computer collaborative learning model. Keywords: AI self-efficacy, AI technology dependence, college students, learning burnout, technology acceptance

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

Chen Gu; Bingchen He; Xinping Zhang. The relationship between college students' AI technology dependence and learning burnout: a chain mediation analysis of technology acceptance and AI self-efficacy. Frontiers in Psychology (2026-05-22). https://doi.org/10.3389/fpsyg.2026.1843366

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.