Generative AI · Education leadership

Actionable companion · Assessment and evidence of learning

From evidence to district action: From enhancement to over-reliance: a mixed-method study of generative AI and sustainable learning performance

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:

The rapid integration of generative artificial intelligence (AI) in education has raised important questions regarding its impact on student learning outcomes. While prior research has primarily focused on AI adoption, limited attention has been given to its influence on sustainable learning performance and the potential risks associated with its use. Addressing this gap, this study develops a comprehensive model to examine both the positive and negative effects of AI use on learning by integrating AI literacy, self-regulated learning, cognitive offloading, and individual differences. A mixed-method approach was employed, combining time-lagged survey data collected in three waves from 623 university students in China with qualitative interviews conducted with educators. The quantitative data were analyzed using PLS-SEM, while qualitative data were examined through thematic analysis to provide deeper insights into the observed relationships. The findings reveal that AI literacy significantly enhances critical AI evaluation, which, along with self-regulated learning, promotes effective AI use. Effective AI use positively influences sustainable learning performance but also increases AI over-reliance, which negatively affects learning outcomes. Furthermore, polychronicity moderates key relationships, indicating that multitasking tendencies shape both AI dependency and learning effectiveness. The qualitative findings support and explain these results, highlighting the behavioral mechanisms underlying AI-supported learning. Overall, the study demonstrates that the impact of AI on learning is dual in nature and depends on how students engage with the technology, offering important implications for theory and educational practice.

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

Li Gao; Yanli Sun; Safeer Ullah Khan. From enhancement to over-reliance: a mixed-method study of generative AI and sustainable learning performance. Frontiers in Psychology (2026-06-29). https://doi.org/10.3389/fpsyg.2026.1847369

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.