Consumer-grade virtual reality has recently become available for both desktop and mobile platforms and may redefine the way that students learn. However, the decision regarding which device to utilise within a curriculum is unclear. Desktop-based VR has considerably higher setup costs involved, whereas mobile-based VR cannot produce the quality of environment due to its limited processing power. This study aimed to compare performance in an anatomical knowledge test between two virtual reality headsets, the Oculus Rift and Gear VR, as well as to investigate student perceptions and adverse health effects experienced from their use. An identical lesson on spine anatomy was presented to subjects using either the Oculus Rift or Gear VR, with no significant differences observed in test scores from participants using either device, with both groups answering 60% of the questions correctly. However, 40% of participants experienced significantly higher rates of nausea and blurred vision when using the Gear VR (P < 0.05). It was established that the more cost effective mobile-based VR was just as suitable for teaching isolated-systems than the more expensive desktop-based VR. These outcomes show great promise for the effective use of mobile-based virtual reality devices in medical and health science education.

New accessible learning methods delivered through mobile mixed reality are becoming possible in education, shifting pedagogy from the use of two dimensional images and videos to facilitating learning via interactive mobile environments. This is especially important in medical and health education, where the required knowledge acquisition is typically much more experiential, self-directed, and hands-on than in many other disciplines. Presented are insights obtained from the implementation and testing of two mobile mixed reality interventions across two Australian higher education classrooms in medicine and health sciences, concentrating on student perceptions of mobile mixed reality for learning physiology and anatomy in a face-to-face medical and health science classroom and skills acquisition in airways management focusing on direct laryngoscopy with foreign body removal in a distance paramedic science classroom. This is unique because most studies focus on a single discipline, focusing on either skills or the learner experience and a single delivery modality rather than linking cross-discipline knowledge acquisition and the development of a student’s tangible skills across multimodal classrooms. Outcomes are presented from post-intervention student interviews and discipline academic observation, which highlight improvements in learner motivation and skills, but also demonstrated pedagogical challenges to overcome with mobile mixed reality learning.

Introduction: With the increasing accessibility of tools such as ChatGPT, Copilot, DeepSeek, Dall-E, and Gemini, generative artificial intelligence (GenAI) has been poised as a potential, research timesaving tool, especially for synthesising evidence. Our objective was to determine whether GenAI can assist with evidence synthesis by assessing its performance using its accuracy, error rates, and time savings compared to the traditional expert-driven approach.Methods: To systematically review the evidence, we searched five databases on 17 January 2025, synthesised outcomes reporting on the accuracy, error rates, or time taken, and appraised the risk-of-bias using a modified version of QUADAS-2.Results: We identified 3,071 unique records, 19 of which were included in our review. Most studies had a high or unclear risk-of-bias in Domain 1A: review selection, Domain 2A: GenAI conduct, and Domain 1B: applicability of results. When used for (1) searching GenAI missed 68% to 96% (median = 91%) of studies, (2) screening made incorrect inclusion decisions ranging from 0% to 29% (median = 10%); and incorrect exclusion decisions ranging from 1% to 83% (median = 28%), (3) incorrect data extractions ranging from 4% to 31% (median = 14%), (4) incorrect risk-of-bias assessments ranging from 10% to 56% (median = 27%).Conclusion: Our review shows that the current evidence does not support GenAI use in evidence synthesis without human involvement or oversight. However, for most tasks other than searching, GenAI may have a role in assisting humans with evidence synthesis.