To support effective collaborative learning, it is essential to enhance collective efficacy and provide timely, tailored feedback throughout the learning process. However, it is often difficult for a single instructor to monitor multiple groups and deliver feedback that meets the specific needs of each team in real time. This study aims to develop and validate a systematic design for an AI chatbot that enhances collective efficacy in collaborative learning environments. The design will be devised by developing and validating design principles. The design-based research methodology was conducted to develop the design principles, which were then validated by experts and evaluated for usability. This process involved a review of relevant literature and case analysis, followed by expert validation and usability test to confirm the design principles’ validity. The final design principles are composed of four design elements— (1) support for group cohesion, (2) support for affective cohesion, (3) support for collaborative learning activities, and (4) facilitation of dialogue—and ten design principles. These include the principle of belongingness formation, interdependence, positive atmosphere, empathy formation, sharing, collaborative problem-solving, social regulation, immediate scaffolding, familiarity, and personification. The design principles are further supported by 46 actionable sub-guidelines. Validation was conducted through three rounds of expert review and usability testing with educators and developers, whose diverse perspectives informed both the pedagogical soundness and technical feasibility of the principles. This study contributes to the field by offering theoretically grounded and practically applicable guidelines for designing AI chatbots in education. Importantly, the principles are designed to be usable by educators without requiring technical expertise, thus supporting scalable and accessible implementation in real classrooms.

Background: In healthcare education, understanding and managing cognitive load is crucial for enhancing learning outcomes for students, healthcare professionals, patients, and the general public. Despite numerous studies developing cognitive load classification models, there is a lack of comprehensive guidelines on how to effectively utilize these models in healthcare education. This study reviews cognitive load classification models using physiological data to provide insights and guidelines for their development in healthcare contexts. Methods: A scoping review was conducted on studies published between 2015 and 2024, identified through databases including Scopus, Web of Science, PubMed, EMBASE, and PsycINFO. The search terms included “cognitive load,” “physiology,” “data,” and “classification.” Inclusion criteria were peer-reviewed journal articles in English, focused on the healthcare context, utilizing experimental physiological data, and developing classification models. After screening 351 articles, ten studies met the inclusion criteria and were analyzed in detail. Results: Task design predominantly focused on measuring intrinsic cognitive load by adjusting task difficulty. Data collection mainly utilized EEG (electroencephalogram) and body movement data. SVM (support vector machine) algorithms were the most frequently used for model development, with cross-validation and feature selection employed to prevent overfitting. This study derived the importance of clearly defining cognitive load types, designing appropriate tasks, establishing reliable ground truths with multiple indicators, and selecting contextually relevant data. Conclusions: This study provides a comprehensive analysis of cognitive load classification models using physiological data in healthcare education, offering valuable guidelines for their development. Despite the study’s limitations, including a small number of analyzed papers and limited diversity in educational contexts, it offers critical insights for using and developing cognitive load classification in healthcare education. Future research should explore the applicability of these models across diverse educational settings and populations, aiming to enhance the effectiveness of healthcare education and ultimately improve learning and healthcare outcomes.

Biomedical graduate education requires systematic reform, as traditional lecture-based approaches do not meet the interdisciplinary and clinically oriented