Actionable companion · Assessment and evidence of learning
From evidence to district action: ChatGPT and higher education in Latin America: measuring perceived academic skills
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.
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 use of generative artificial intelligence tools such as ChatGPT has transformed various academic practices in higher education, generating interest in understanding its perceived impact on the development of students' communicative and analytical skills. This study aimed to validate the Student Perception of ChatGPT instrument. A non-experimental quantitative design with an instrumental and comparative approach was applied to a sample of 2,579 students with prior experience using ChatGPT, from Ecuador, Mexico, Chile, Brazil, Colombia, and Guatemala. The questionnaire included 18 items organized into two key dimensions, communicative skills and analytical skills, measured on a five-point Likert scale. Internal consistency analyses (α = 0.943; ω = 0.944), as well as exploratory and confirmatory factor analyses, were conducted. The results confirmed high reliability and a coherent two-factor structure, with consistent groupings among items related to academic writing, digital communication, critical thinking, and problem-solving. These findings provide empirical evidence of the instrument's validity to measure students' perceptions of ChatGPT's contribution to the development of such skills. It is recommended to conduct longitudinal, mixed-method, and performance-based studies to more precisely examine its actual effect on university learning.
A 90-day district pilot
- 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.
- 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.
- 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.
- 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
Carlos Enrique George-Reyes; Christian A. Romero; Ennio Jesús Merida-Cordova; Enrique Yeguas-Bolivar. ChatGPT and higher education in Latin America: measuring perceived academic skills. Frontiers in Research Metrics and Analytics (2026-06-16). https://doi.org/10.3389/frma.2026.1816902
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.