Agentic education · Learning science

Actionable companion · Assessment & reasoning

Learning with agency: A multinational study on the factors influencing university students’ attitudes and usage of ChatGPT

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:

Artificial intelligence models, like ChatGPT, have the potential to revolutionize higher education when implemented properly. This study aimed to investigate the factors influencing university students’ attitudes and usage of ChatGPT in Arab countries. The survey instrument “TAME-ChatGPT” was administered to 2240 participants from Iraq, Kuwait, Egypt, Lebanon, and Jordan. Of those, 46.8% heard of ChatGPT, and 52.6% used it before the study. The results indicated that a positive attitude and usage of ChatGPT were determined by factors like ease of use, positive attitude towards technology, social influence, perceived usefulness, behavioral/cognitive influences, low perceived risks, and low anxiety. Confirmatory factor analysis indicated the adequacy of the “TAME-ChatGPT” constructs. Multivariate analysis demonstrated that the attitude towards ChatGPT usage was significantly influenced by country of residence, age, university type, and recent academic performance. This study validated “TAME-ChatGPT” as a useful tool for assessing ChatGPT adoption among university students. The successful integration of ChatGPT in higher education relies on the perceived ease of use, perceived usefulness, positive attitude towards technology, social influence, behavioral/cognitive elements, low anxiety, and minimal perceived risks. Policies for ChatGPT adoption in higher education should be tailored to individual contexts, considering the variations in student attitudes observed in this study.

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

The current study employed a cross-sectional design with an electronic distribution of a previously validated survey instrument51. The “TAME-ChatGPT” instrument has been shown as a reliable, valid, and practical tool to assess university students’ attitudes towards ChatGPT51. Specifically, the TAME-ChatGPT tool helps to unravel the role of factors such as risk perceptions, perceived usefulness, ease of use, attitudes towards technology, and behavioral aspects in the adoption of ChatGPT as an educational tool among students51.

Limitations and uncertainty · source passage

In this study, the major findings illustrated that the adoption of ChatGPT among university students is influenced by both socio-demographic variables and various TAM constructs as modeled in “TAME-ChatGPT”. Additionally, the study findings revealed that ChatGPT was perceived to have both positive and negative aspects among the participating students reflecting the ongoing debate regarding ChatGPT1,14. This appears conceivable in light of the current evidence showing that the use of AI-based tools for educational purposes were perceived as a double-edged sword1,5,14,70. On one hand, these tools can be valuable in delivering timely, efficient, and personalized support to a broad student population promoting equity in education71–74. On the other hand, valid concerns should be emphasized including the possible generation of inaccurate and biased educational content among other ethical concerns1,5,75,76. Additionally, Safranek et al. highlighted the current limitations of ChatGPT in medical education including the inability to provide comprehensive contextual information along with its lack of intuitive patient assessment capabilities which are essential skills acquired during medical education75. An early systematic review by Sallam emphasized the relatively below bar performance of ChatGPT in some topics hindering its current utility in healthcare education1. Similarly, multiple later studies confirmed this concern of generating inaccurate content in specific topics (e.g., Radiology, Microbiology)77–79.

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 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

Maram Abdaljaleel; Muna Barakat; Mariam Alsanafi; Nesreen A. Salim; Husam Abazid; Diana Malaeb; Ali Haider Mohammed; Bassam Abdul Rasool Hassan; Abdulrasool M. Wayyes; Sinan Subhi Farhan; Sami El Khatib; Mohamad Rahal; Ali Sahban; Doaa H. Abdelaziz; Noha O. Mansour; Reem AlZayer; Roaa Khalil; Feten Fekih-Romdhane; Rabih Hallit; Souheil Hallit; Malik Sallam. A multinational study on the factors influencing university students’ attitudes and usage of ChatGPT. Scientific Reports (2024-01-23). https://doi.org/10.1038/s41598-024-52549-8

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.