Although research on schema has been widely investigated for the past decades, little research has addressed the development of a systematic instructional design theory using schema principles and processes. This study proposes a systematic schema-based instructional design model, including general and schema analysis, schema-based design, and development processes and techniques for evaluating a learner’s acquired schema. By synthesizing empirical studies, this study comprehensively reviews literature on schema and foundational principles for learning. The goal of the study is to enrich the knowledge base of schema-based instructional design for different learning environments. Thus, the study is concluded by a discussion on how to utilize a schema-based instructional design for self-paced learning environments with additional implications and further recommendations.

Clinical reasoning has been characterized as being an essential aspect of being a physician. Despite this, clinical reasoning has a variety of definitions and medical error, which is often attributed to clinical reasoning, has been reported to be a leading cause of death in the United States and abroad. Further, instructors struggle with teaching this essential ability which often does not play a significant role in the curriculum. In this article, we begin with defining clinical reasoning and then discuss four principles from the literature as well as a variety of techniques for teaching these principles to help ground an instructors’ understanding in clinical reasoning. We also tackle contemporary challenges in teaching clinical reasoning such as the integration of artificial intelligence and strategies to help with transitions in instruction (e.g., from the classroom to the clinic or from medical school to residency/registrar training) and suggest next steps for research and innovation in clinical reasoning.

This experimental study examined the effects of segmentation and self-explanation designs on cognitive load in instructional videos. Four types of instructional videos (segmentation, self-explanation, combined, and control) were created and tested by 121 undergraduate students randomly assigned to one of four research groups. The results of students’ self-ratings on the cognitive load survey showed that the segmenting design produced a significantly less germane cognitive load than the two non-segmenting designs (self-explanation and control). The self-explanation design did not produce a significantly more germane load than the control design. However, students’ dispositions toward segmentation and self-explanation designs were generally positive and supported the theoretical justifications reported in the literature. The findings are discussed, along with segmentation dilemmas, limitations, and future study implications.

What was the educational challenge? Reflective practice (RP) fosters professional growth but is often hindered by unclear purpose and minimal guidance. Designing comprehensive RP assignments is challenging because it takes time and not all faculty possess the skills to generate effective assignments. This innovation addressed two challenges: (1) creating clear, scaffolded reflective assignments using the transparent assessment framework (TAF) and (2) reducing faculty workload in assignment design. What was the solution? ChatGPT 4o was used to design RP assignments in health professions education (HPE). The TAF guided the design of the assessment. How was the solution implemented? A four-step process was followed to facilitate the design of the assignment. ChatGPT 4o was prompted to design the assignment and refined for the course. What lessons were learned that are relevant to a wider global audience? A pilot study in three graduate-level HPE courses showed ChatGPT-assisted assignments improved clarity, structure, and student performance while decreasing faculty preparation time. What are the next steps? We plan to expand this research to obtain student feedback on the effectiveness of the redesigned assignment and to explore the effectiveness of AI-generated reflective assignments with medical students who may require more structured guidance and contextualized prompts.