Agentic education · Learning science

Actionable companion · Motivation & engagement

Learning with agency: Advancing Nursing Education: Evaluating Artificial Intelligence (AI) and Escape Room (ER) Approaches for E‐Learning and Interpretation Skills

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. Health-professions learning must be distinguished from K–12 instruction.

Original authors’ abstract, reproduced under the linked source license:

Introduction The spread of new educational technologies has challenged the cold and inflexible framework of traditional education, aiming to replace centuries‐old methods with short, low‐cost methods to improve learning and educational productivity. Thus, this study aimed to compare the effects of artificial intelligence (AI) and escape rooms (ERs) on e‐learning satisfaction and the skill of arterial blood gas (ABG) interpretation in nursing students. Materials and Methods The current study was conducted as a quasi‐experiment on 91 nursing students in the third to sixth academic semesters, with a convenient sampling approach to AI, ER, and control groups by balanced block randomization. In the AI group, participants used ChatGPT; the ER group used the Twine platform; and the control group used the traditional method once a week. The E‐learning satisfaction scale (ELSS) and arterial blood gas interpretation scale (ABGIS) were filled before and two weeks after the end of interventions. Data were analyzed using descriptive and inferential statistical tests in SPSS V.25 at a 0.05 significance level. Results In comparison to ER and control, AI significantly enhanced e‐learning satisfaction and ABG interpretation (p < 0.001). In terms of content, interface, and communication satisfaction, AI demonstrated superiority over ER (p < 0.001). The intervention made a big difference—explaining nearly 90% of the changes in ELSS and about 40% in ABGIS. Among all the factors, AI stood out the most, driving major improvements in both learning outcomes and student engagement. Conclusion The use of AI compared to the educational ER and conventional methods significantly increases the learning of ABG interpretation and satisfaction with e‐learning. It is suggested that the positive and effective capabilities of AI be used as an educational assistant to enhance learning and teaching experiences in medicine and nursing.

What kind of evidence is this?

Supporting AI learning science · Comparative / pre-post study reported

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

Using the convenience sampling method, the study participants were chosen from among the third to sixth academic semester nursing students at the School of Nursing and Midwifery of Ilam. The inclusion criteria were having a personal smartphone, being willing to participate in the research, being familiar with basic computer skills, attending the study full‐time, and receiving a score of less than 10 on the arterial blood gas interpretation scale (ABGIS). The exclusion criteria were being transferred to another university, having a history of working as a paramedic or performing student work in a hospital, failing a previous semester’s practical nursing unit specifically due to clinical skill deficiencies, taking part in related workshops or research within the previous three months, not completing the questionnaire, or withdrawing from the study. All subjects gave their written and informed permission after enrolling in the research.

Limitations and uncertainty · source passage

The strengths of the present study include extensive inclusion and exclusion criteria, random assignment, comparison of more than three learning methods, and sufficient sample size. The limited weaknesses of this study include the length of sessions, the short time to complete the instruments, and no full psychometric validation for ELSS and ABGIS.

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.

  • Measure engagement separately from achievement. Check whether novelty-driven participation persists and whether it translates to independent performance.

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

Azin Salimi; Masoumeh Nouri; Amirreza Moradi; Alireza Vasiee. Advancing Nursing Education: Evaluating Artificial Intelligence (AI) and Escape Room (ER) Approaches for E‐Learning and Interpretation Skills. Nursing Research and Practice (2026-04-10). https://doi.org/10.1155/nrp/8869482

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.