Generative AI · Education leadership

Actionable companion · Assessment and evidence of learning

From evidence to district action: Academic AI overreliance scale: development and preliminary psychometric validation in health sciences university students

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 Introduction The rapid integration of generative artificial intelligence (AI) into higher education has raised growing concerns regarding its potential impact on academic autonomy, self-regulated learning, and cognitive functioning. However, limited attention has been given to the progressive delegation of academic cognitive and self-regulatory processes to intelligent systems. This study aimed to develop and examine the initial psychometric properties of the Academic AI Overreliance Scale among health sciences university students. Methods Two independent studies were conducted in Ecuador. In Study 1, an Exploratory Factor Analysis (EFA) was performed with 437 university students to identify the underlying structure of an initial 24-item pool. In Study 2, a Confirmatory Factor Analysis (CFA) was conducted with an independent sample of 437 university students to evaluate the factorial validity, measurement invariance, and internal consistency of the refined 22-item version. Results Study 1 supported a two-factor solution composed of Emotional-Dysregulated Reliance and Cognitive Reliance. Study 2 confirmed the correlated two-factor model, showing excellent fit indices (CFI = 0.994, TLI = 0.994, RMSEA = 0.066, SRMR = 0.056) and strict invariance across sex groups. Internal consistency was adequate for both Emotional-Dysregulated Reliance (α = 0.94, ω = 0.94) and Cognitive Reliance (α = 0.88, ω = 0.88). Discussion The findings suggest that Academic AI Overreliance may represent a specific form of cognitive and self-regulatory externalization associated with the integration of generative AI into academic activities, rather than a traditional form of technological addiction. The scale provides preliminary evidence of validity based on internal structure, measurement invariance, and reliability, and may serve as an initial instrument for future research on generative AI, academic reasoning, and intellectual autonomy in higher education. Keywords: academic autonomy, cognitive offloading, generative artificial intelligence, psychometric validation, scale development, self-regulated learning

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

Daniel Oleas; Jose A Rodas; David Alarcón Rubio. Academic AI overreliance scale: development and preliminary psychometric validation in health sciences university students. Frontiers in Digital Health (2026-07-21). https://doi.org/10.3389/fdgth.2026.1882677

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.