Agentic education · Learning science

Actionable companion · Self-regulation & metacognition

Learning with agency: The Role of Individual Capabilities in Maximizing the Benefits for Students Using GenAI Tools in Higher Education

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.

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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:

Abstract Although the adoption and benefits of GenAI (Generative Artificial Intelligence) tools among higher education students have been widely explored in existing studies, less is known about how individual capabilities influence the use of these tools. Drawing on the Information System Success Model (ISSM) and the Expectation–Confirmation Model (ECM), this study examines how students’ capabilities, including critical thinking, self-directed learning ability, and AI literacy, impact the quality of information obtained from GenAI tools. Additionally, it explores the relationships among information quality, student satisfaction, and the intention to continue using GenAI tools in higher education. Survey data from 1448 GenAI tools users in Chinese universities reveal that students with stronger capabilities tend to extract higher-quality information, which in turn fosters their satisfaction with GenAI tools and the intention to continue using these tools. The findings highlight the crucial role of individual capabilities in maximizing the potential of GenAI tools, and it emphasizes the need to cultivate students’ critical thinking, self-directed learning ability, and AI literacy to achieve sustainable success in the GenAI era. Theoretically, this study extends the ISSM and ECM by exploring the impact of students’ capabilities and the mediating role of user satisfaction between information quality and the intention to continue using GenAI tools. Practically, this study provides implications for educators and policymakers to enhance students’ capabilities, thus maximizing the potential benefits of GenAI tools in higher education. Keywords: GenAI tools, higher education students, usage behavior, critical thinking, self-directed learning ability, artificial intelligence literacy

What kind of evidence is this?

Supporting AI learning science · 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

This study was carried out from 28 December 2023 to 10 January 2024 through an online questionnaire, following a convenience sampling procedure. Questionnaires were distributed among undergraduate and graduate students at 21 universities. The research protocol was approved in advance, and all data were collected anonymously. Before completing the questionnaire, participants could review the informed consent, which detailed the purpose of the study and the safe storage of their data. Participants would only begin completing the questionnaire after agreeing to participate in the study. Out of the 2694 potential respondents, a valid data set of 1784 (66.22% of the responses) participants was obtained.

Limitations and uncertainty · source passage

This study adds to our current understanding of how students’ capabilities influence the use of GenAI tools. Although there has been substantial research on the adoption of GenAI tools among higher education students, limited attention has been given to how students’ capabilities shape their effective use of this cutting-edge technology. This study suggests that students with higher levels of critical thinking, self-directed learning ability, and AI literacy are more capable of obtaining relevant information, evaluating its accuracy, and integrating it into their learning processes, leading to the sustained use of GenAI tools. It suggests that the mere availability of GenAI tools is insufficient, as students must possess the necessary cognitive and technical capabilities to fully benefit from these tools. This underscores the significance of equipping students with these key capabilities in a GenAI learning environment, as they are fundamental for maximizing the benefits of such technologies.

Design the system before the pilot

Use this study to inform a learning mechanism or implementation question. It is not direct evidence that autonomous agents improve learning. Keep the proposed agent behavior separate from what the source actually tested.

  • Use plan–attempt–check–reflect prompts and compare confidence with actual performance. Keep learner choice and reflection visible.
  • Use common rubrics, blind scoring where feasible and a fresh unaided reasoning task. Document permissible assistance and avoid treating AI-detector scores as proof of misconduct.

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. 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.
  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 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

Jia Qi; Ji’an Liu; Yanru Xu. The Role of Individual Capabilities in Maximizing the Benefits for Students Using GenAI Tools in Higher Education. Behavioral Sciences (2025-03-07). https://doi.org/10.3390/bs15030328

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.