ChatGPT versus a customized AI chatbot (Anatbuddy) for anatomy education: A comparative pilot study.

Large Language Models (LLMs) have the potential to improve education by personalizing learning. However, ChatGPT-generated content has been criticized for sometimes producing false, biased, and/or hallucinatory information. To evaluate AI's ability to return clear and accurate anatomy information, this study generated a custom interactive and intelligent chatbot (Anatbuddy) through an Open AI Application Programming Interface (API) that enables seamless AI-driven interactions within a secured cloud infrastructure.

Twelve tips for co-production of online learning.

In the digital age, experts in digital learning tools, or learning technologists (LTs), play an increasingly important role in the creation and delivery of online learning in health professions education. However, their expertise in the selection, curation and implementation of digital tools is often underutilized due to imbalanced relationships and lack of effective collaboration between faculty and LTs. Here, we describe how the co-production model can be applied to build equal and synergistic partnerships between faculty and LTs, so as to optimize the use of digital affordances and enhance online learning.

Rehearsal-based digital serious boardgame versus a game-free e-learning tool for anatomical education: Quasi-randomized controlled trial.

Serious games may resolve problems relating to low motivation in complex medical topics such as anatomy. However, they remain relatively novel introductions to the science of learning, and further research is required to ascertain their benefits. This study describes the overall development and testing of a digital serious boardgame designed to facilitate the rehearsal of musculoskeletal anatomy based on self-determination theory with considerations for the psychological state of Flow.

Freestanding and Scalable Force-Softness Bimodal Sensor Arrays for Haptic Body-Feature Identification.

Tactile technologies that can identify human body features are valuable in clinical diagnosis and human-machine interactions. Previously, cutting-edge tactile platforms have been able to identify structured non-living objects; however, identification of human body features remains challenging mainly because of the irregular contour and heterogeneous spatial distribution of softness. Here, freestanding and scalable tactile platforms of force-softness bimodal sensor arrays are developed, enabling tactile gloves to identify body features using machine-learning methods.

An Exploratory Digital Board Game Approach to the Review and Reinforcement of Complex Medical Subjects Like Anatomical Education: Cross-sectional and Mixed Methods Study.

Background: Serious games have the potential to resolve educational problems faced by medical students, such as insufficient rehearsal due to boredom and lack of motivation. However, serious games' relatively novel concepts in science and many genres of games that are common in recreation remain underresearched in the literature. Board games are one such genre that, despite their potential, affordability, and flexibility, are rarely designed for medical students, and little is known about student perceptions of them and their compatibility with rehearsal.