Agentic education · Learning science

Actionable companion · Assessment & reasoning

Learning with agency: Using Hologram-Based Augmented Reality in Anatomy Learning: The TEACHANATOMY Randomized Trial

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 Purpose Augmented reality (AR) can generate realistic holograms overlaid on the real-word environment to provide an interactive learning experience. However, further research is needed to assess the impact of such technologies on knowledge acquisition. This study aims to evaluate the efficacy of an AR learning application in anatomy education. Method TEACHANATOMY, a controlled, randomized trial, was conducted from September 14–October 24, 2022, at the University of Zurich and the Swiss Federal Institute of Technology. It included first- and second-year medical students with no prior anatomy education and compared learning with an interactive hologram-based AR learning module—incorporating adaptive learning and gamification elements—with traditional learning (TL) methods including textbooks, videos, and online resources. Forty-eight participants were randomly allocated to the AR or TL group. The primary outcome consisted of the scores on the theoretical and practical knowledge tests. Secondary outcomes included adverse health symptoms and user experience. Results The AR group performed significantly better on both theoretical (median [IQR] scores: AR: 18.8 [16.6–20.0]; TL: 9.4 [7.7–11.3]; P < .001) and practical (AR: 14.0 [12.3–14.7]; TL: 5.0 [4.0–6.0]; P < .001) knowledge tests. The most common adverse health symptoms were headache, reported by 13/24 (54.2%) TL participants and 9/24 (37.5%) AR participants, and fatigue, and experienced by 13/24 (54.2%) TL participants and 2/24 (8.3%) AR participants. All participants evaluated learning with TEACHANATOMY as a positive experience, rating it as efficient and easy to understand. All participants agreed learning with AR can be beneficial in learning anatomy, with 89.6% (43/48) and 100% (48/48) expressing willingness to use AR as a learning tool for theoretical and practical anatomy learning, respectively. Conclusions Findings show a short-term learning benefit with the use of the TEACHANATOMY learning application, thus supporting the implementation of interactive hologram-based AR technologies to improve knowledge in anatomy education.

What kind of evidence is this?

Intelligent tutoring & adaptive learning · Randomized 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

This study was a prospective, controlled, randomized, 2-arm, parallel trial conducted at the Balgrist University Hospital from September 14 to October 24, 2022. Although involving 2 universities in Zurich, it was classified as a single-center trial due to the neuroanatomy course being a joint program between these institutions. The trial protocol is available as Supplemental Digital Appendix 1 (at http://links.lww.com/ACADMED/B685). The study followed the Consolidated Standards of Reporting Trials (CONSORT) extension guidelines (https://www.equator-network.org/reporting-guidelines/consort-routine/) and was approved by the Cantonal Ethics Committee of Canton Zurich (BASEC-Nr. Req-202200906).

Limitations and uncertainty · source passage

The results of this study should be interpreted with caution due to potential limitations. First, the study was considered a single site study with a single cohort and a small number of subjects, thus limiting the generalizability of our findings. Additionally, there may be significant variation in individual ability and performance on assessment tasks in general, as well as with AR technology. A second limitation was the absence of a follow-up test to assess the knowledge retention potential of AR-based learning. This is an important aspect that should be considered in future studies. Third, the repetition block in the AR study session included questions designed to assess and improve participants knowledge, implemented with adaptive learning technology and gamification elements. This could represent an advantage for AR group participants independent of the AR technology itself, introducing a potential bias. Finally, the novelty effect of the technology may have influenced participants’ satisfaction and should be taken into consideration, as it could introduce biases in the interpretation of user experience results.

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.

  • 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

Lukas Zingg; Melanie Häusler; Jonas Hein; Sascha Jecklin; Sören Kottner; Dominic Gascho; Nicola Cavalcanti; Phillippe Voinov; Tobias Götschi; Fabio Carrillo; Florian Lagler; Philipp Fürnstahl; Mazda Farshad. Using Hologram-Based Augmented Reality in Anatomy Learning: The TEACHANATOMY Randomized Trial. Academic Medicine (2025-03-03). https://doi.org/10.1097/acm.0000000000006012

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.