Actionable companion · Learning design & outcomes
Learning with agency: Artificial Intelligence in higher education: use and appropriation of tools by university students
AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not claims or recommendations attributed to the source authors. No independent peer review of this guide is asserted.
Evidence and context
Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): Higher education. Verify the participant population and task before transferring the findings.
Original authors’ abstract, reproduced under the linked source license:
Introduction This research presents the results of the study to analyze the perception of Artificial Intelligence (AI) and its relationship with the learning and Academic Performance of students at a university in northwestern Mexico during the period February to May 2025. Method An analytical, cross-sectional study was conducted. The students in the study were selected through non-probabilistic sampling, comprising 289 students in the third, fourth, and sixth semesters. Those who did not complete the questionnaire correctly were excluded from this sampling. The instrument was developed using a Likert-type scale with responses: strongly disagree, disagree, indifferent, agree, and strongly agree. Results The results of the present research confirm that integrating AI-based tools into educational environments is changing how students relate to knowledge, promoting more personalized and adaptive learning experiences. by means of confirmatory factor analysis (CFA), an analysis procedure in structural equation modeling (SEM).
What kind of evidence is this?
Intelligent tutoring & adaptive learning · Survey / observational study
Stage: Higher education. Screening labels describe reported context and study-design language. They are not a risk-of-bias rating or proof of effectiveness for an age band.
Study methods · source passage
To understand the meaning of Artificial Intelligence, Rouhiainen (2018) defines AI as the ability of computers to perform activities that normally require human intelligence. This is because AI is the ability of machines to use algorithms, learn from data, and apply what they learn to decision-making, just as a human would. The development of Artificial Intelligence is divided into three stages, each with distinct capabilities. In the first stage, the software operates according to logical rules so that a system can act intelligently, such as playing chess. In the second stage of AI, machine learning techniques are used to extract patterns and knowledge from large amounts of data, which is where AI is currently. In the third stage, it is estimated that the basis of artificial superintelligence will be established, with systems capable of understanding the real world and offering innovative solutions to new or unknown problems (Franganillo, 2023).
Limitations and uncertainty · source passage
In the study by De Jesus et al. (2025), which challenges the use of artificial intelligence in higher education, they mention the limitations of ChatGPT and similar tools in the preparation of academic texts and the development of research activities. It is essential to use them wisely. Although their potential is undeniable, these technologies do not constitute a definitive solution for complex learning processes, especially in academic tasks that demand methodological rigor and conceptual depth. Likewise, an excessive dependence on automation could affect the researcher's ability to strengthen essential competencies, encourage the depersonalization of intellectual work, and promote a reductionist vision of the educational process, focused more on efficiency than on the deep construction of knowledge.
Design the system before the pilot
Document the learner model, mastery rule, hint sequence and adaptation signal. Compare adaptive routing with the same content in a non-adaptive sequence. Check whether early errors lead to persistent low-expectation pathways.
- Specify a measurable learning objective, a comparator and a task without tool assistance before adopting a system.
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. Choose one disciplinary task. Require a first attempt, a documented AI consultation, a critique of the output and an independent explanation. Align the comparison group and scoring rubric with the same objective. 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 and accuracy on a new task without AI. Distinguish model examination scores, student perceptions and actual learner gains; only the last directly tests educational benefit. 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
Raúl González; Roberto Valdés; Esteban Pérez; Ernesto Leon-Castro. Artificial Intelligence in higher education: use and appropriation of tools by university students. Frontiers in Psychology (2026-07-03). https://doi.org/10.3389/fpsyg.2026.1840886
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.