教育学最新文献

Self-directed learning (SDL) is integral to medical education. It helps in fostering critical thinking, independence, and problem-solving skills. With advancements in technology, digital tools like search engines, interactive content, and large language model (LLM) chatbots have become supplementary tools to traditional materials such as textbooks. However, limited data exist on SDL resource preferences among medical students in India since the inception of LLMs like ChatGPT. To address this, 64 medical students participated in a classroom-based SDL session on anemia. Students freely selected resources during a 40-min preparation period, followed by 20 min of writing responses to a total of five higher-order knowledge questions (i.e., questions started with "Explain why"). Postsession, they anonymously reported their resource use. Among 63 valid responses, 46.03% used one resource, 39.68% used two, and 14.29% used three. Search engines (61.9%) and LLM chatbots (60.32%) were the most frequently used, followed by textbooks (26.98%), with less reliance on notes, journals, and videos. Hence, there is a growing preference for search engines and LLM chatbots as an educational tool in self-directed learning in a classroom setting.NEW & NOTEWORTHY This study reports a shift in educational resource use for self-directed learning (SDL) among medical students after introduction of large language model (LLM) chatbots. Students use multiple sources, with digital tools like search engines and LLM chatbots nearly matching each other as the most preferred resources. Despite the accessibility and efficiency of digital tools, traditional resources like textbooks remain relevant, though less frequently chosen.

Freely accessible generative artificial intelligence (GenAI) poses challenges to physiology education regarding learning and academic integrity. Although many studies have explored the capabilities of GenAI to complete assessments, few have implemented educative activities to highlight GenAI risks and benefits or explored physiology students' perceptions and uses of GenAI. Our study implemented a learning activity, designed using constructivist principles, to allow physiology students to explore GenAI and consider its use in assessment tasks. The activity engaged students (n = 236) enrolled in a second-year physiology subject over 2 years. The activity began with students being directed to critique a sample exam answer as a form of content revision. The answer had been covertly generated by ChatGPT, and it lacked depth and contained some hallucinated facts. Students then engaged in discussion about the use of GenAI for university study and assessment. Questions were used to stimulate thought and discussion, and student responses were collected via Padlet (492 posts). Thematic analysis of the comments highlighted students' beliefs about using GenAI and perceived benefits and risks. There was a general trend of increasing acceptance of using GenAI, and using it for assessment, over time. Students were concerned about breaching academic integrity guidelines, information accuracy and sources, and the negative effect it might have on their learning. At the conclusion of the activity, the revelation that ChatGPT wrote the sample exam answer reinforced the need for responsible GenAI use.NEW & NOTEWORTHY Constructivist learning tenets were used to guide the design of a critical evaluation learning activity about GenAI, to enable physiology students to make informed decisions regarding the use of GenAI in their learning and assessment. The trend of increasing acceptance of GenAI coincided with increasing student beliefs about uses of GenAI being perceived as responsible. Student concerns about academic integrity and ethical considerations persisted, yet academic misconduct cases increased.

This research focuses on Generation Z (Gen Z) students, specifically those in nursing colleges. Gen Z individuals display unique characteristics in terms of thinking, personality, lifestyle, and learning preferences compared to preceding generations, necessitating adaptations in teaching methodologies within nursing schools. This study explores the effectiveness of the jigsaw technique (JST) in engaging first-year undergraduate nursing students in the learning process. Four topics (modules): Cardiovascular system (module 1), Respiratory system (module 2), Endocrine system (module 3), and Central nervous system (module 4) were selected. Modules 1 and 2 were taught by JST to group I (jigsaw group) and by conventional didactic lectures to group II (lecture group). The groups alternated teaching methods for the remaining modules. Scores in pretest, posttest, and retention tests were higher in group I than in group II. The results were statistically highly significant (P = 0.000) for modules 1, 2, and 4 and not significant (P = 0.411) for module 3. Analysis of student feedback revealed that 63% of students liked JST. Seventy-one percent responded that this is an interesting way of learning the topic, helped them improve their communication skills, and improved interaction with their peers. Seventy-seven percent found that JST helped them understand the topic easily. Sixty-nine percent think that this technique should be used for teaching other physiological concepts and for other undergraduate subjects as well. The study concludes that using and integrating this student-centric teaching method into Gen Z nursing education holds promise for building a foundation of robust knowledge and developing essential personality skills crucial for future nursing professionals.NEW & NOTEWORTHY In our study, we found that the jigsaw technique (JST) significantly improves understanding, comprehension, and retention of topics among nursing students. It also enhances teamwork, self-confidence, and communication skills, aligning with the preferences of Generation Z students. Student feedback analysis reveals that JST facilitates easier understanding of topics, increases self-confidence, improves interpersonal skills, and creates an interactive learning environment. The authors suggest practical implications for nursing education by integrating JST into the curriculum, despite time constraints.

Dramatization, a teaching method where each student acts out or mimics a cell or body parts while the entire group represents the physiological process was adapted to produce original teaching videos paired with a pretest that activates memory and a posttest to prevent misconceptions. Three physiology instructors collaborated on Zoom to create six DramaZoom videos (Dramatization via Zoom) focused on hormone signaling with negative feedback in different contexts. In these videos, each instructor personalizes a different part of an organ system or a physiological process, which allows the visualization of complex concepts in endocrinology. DramaZoom videos utilize theater, personification, and humor to represent physiological processes in a fun and creative way that facilitates students to learn and remember the content. Our goal was to introduce DramaZoom videos as an original teaching tool and present evidence of its efficacy on student learning. We analyzed the impact of DramaZoom videos on students' knowledge acquisition at three distinct levels (1st year medical students, 3rd and 4th year undergraduate science students, and 1st year undergraduate nursing students) and investigated whether the mode of delivery of the videos (face to face during regular classroom teaching or asynchronous in a virtual classroom) affected student learning. Our data show that knowledge in all three student groups improved significantly after viewing DramaZoom videos independently of the mode of delivery. Our data indicate that DramaZoom videos combined with memory activation due to the pretest are an effective tool to instruct this cohort of students regardless of level and delivery mode.NEW & NOTEWORTHY DramaZoom is a teaching tool paired with a pretest to activate memory. It promotes learning for both medical students and undergraduate students with different majors in the study cohort. DramaZoom creates an opportunity for a fun learning experience that promotes knowledge gain in physiology regardless of whether the teaching setting is face to face or completely virtual. Future research will be done to investigate the long-term retention of content.

Generative artificial intelligence (AI) large language models have become sufficiently accessible and user-friendly to assist students with course work, studying tactics, and written communication. AI-generated writing is almost indistinguishable from human-derived work. Instructors must rely on intuition/experience and, recently, assistance from online AI detectors to help them distinguish between student- and AI-written material. Here, we tested the veracity of AI detectors for writing samples from a fact-heavy, lower-division undergraduate anatomy and physiology course. Student participants (n = 190) completed three parts: a hand-written essay answering a prompt on the structure/function of the plasma membrane; creating an AI-generated answer to the same prompt; and a survey seeking participants' views on the quality of each essay as well as general AI use. Randomly selected (n = 50) participant-written and AI-generated essays were blindly uploaded onto four AI detectors; a separate and unique group of randomly selected essays (n = 48) was provided to human raters (n = 9) for classification assessment. For the majority of essays, human raters and the best-performing AI detectors (n = 3) similarly identified their correct origin (84-95% and 93-98%, respectively) (P > 0.05). Approximately 1.3% and 5.0% of the essays were detected as false positives (human writing incorrectly labeled as AI) by AI detectors and human raters, respectively. Surveys generally indicated that students viewed the AI-generated work as better than their own (P < 0.01). Using AI detectors in aggregate reduced the likelihood of detecting a false positive to nearly 0%, and this strategy was validated against human rater-labeled false positives. Taken together, our findings show that AI detectors, when used together, become a powerful tool to inform instructors.NEW & NOTEWORTHY We show how online artificial intelligence (AI) detectors can assist instructors in distinguishing between human- and AI-written work for written assignments. Although individual AI detectors may vary in their accuracy for correctly identifying the origin of written work, they are most effective when used in aggregate to inform instructors when human intuition gets it wrong. Using AI detectors for consensus detection reduces the false positive rate to nearly zero.

Here we describe an approach and overall concept of how to train undergraduate university students to understand basic regulation and integration of glucose and fatty acid metabolism in response to fasting, intake of carbohydrates, and aerobic exercise. During lectures and both theoretical and practical sessions, the students read, analyze, and discuss the fundamentals of the Randle cycle. They focus on how metabolism is regulated in adipose tissue, skeletal muscle, and liver at a molecular level under various metabolic conditions. Subsequently, students perform one of four different trials: 1) overnight fast followed by ingestion of jelly sandwiches and lemonade ad libitum for up to 15 minutes; 2) overnight fast followed by ingestion of a chocolate bar and a soda; 3) overnight fast followed by ingestion of carrots; and 4) light fast and aerobic exercise for 2 hours, while monitoring glucose and fatty acid levels. The data from these trials clearly show that glucose levels are kept constant at around 5 mM, while fatty acid levels rise to 300-700 µM after an overnight fast. Upon carbohydrate intake, glucose levels increase, whereas fatty acid levels are reduced. In response to aerobic exercise, the glucose level is kept constant at 5 mM, while fatty acid levels increase over time. Collectively, the data clearly recapitulate the essence of the Randle cycle. The exercise shows the great pedagogical value of experiments within practical courses to help students gain knowledge of energy metabolism and regulation of biochemical pathways. In an active learning environment, students successfully tackled physiological assignments, enhancing constructive communication and collaboration among peers.NEW & NOTEWORTHY Explore our study on how undergraduates learn about glucose and fatty acid metabolism through a blend of lectures and dynamic practical experiments. Our paper highlights how students delve into the Randle cycle and its regulation in various metabolic scenarios, gaining insights through hands-on trials. This innovative approach not only deepens understanding but also enhances collaborative skills. Dive into our findings to see how active learning shapes future scientists.

A good knowledge of the theoretical foundations of medicine helps students and physicians to better recognize and treat patients with complex medical conditions, including sepsis and septic shock. The article describes the authors' experience in implementing the analysis of sepsis and septic shock using a high-fidelity simulated clinical scenario in the course of pathological physiology for preclinical medical students. The unique aspect of our approach is the integration of core physiology concepts, such as homeostasis, causality, structure-function relationships, and fundamental pathophysiology concepts (e.g., etiology, pathogenesis, cell and tissue damage, inflammation, symptoms, and syndromes) in the analysis of the patient's condition on the high-fidelity simulator with preclinical medical students. According to the students' feedback, the use of a high-fidelity simulator to analyze the sepsis and septic shock scenario increased their interest in the class, improved their motivation to learn the material, and helped them adapt in a safe environment to making decisions based on a large amount of data about a complex patient condition in a time-sensitive situation.NEW & NOTEWORTHY The authors applied core theoretical concepts of physiology and the fundamental concepts of pathological physiology for teaching sepsis and septic shock clinical scenarios on the high-fidelity simulator in the course of pathological physiology for preclinical medical students. It elevated students' interest and motivation, enhanced the educational experience, and prepared students better for real-world clinical decision-making. We consider that this idea might be an inspiration to colleagues and invite further discussion.

The alignment of learning materials with learning objectives (LOs) is critical for successfully implementing the problem-based learning (PBL) curriculum. This study investigated the capabilities of Gemini Advanced, a large language model (LLM), in creating clinical vignettes that align with LOs and comprehensive tutor guides. This study used a faculty-written clinical vignette about diabetes mellitus for third-year medical students. We submitted the LOs and the associated clinical vignette and tutor guide to the LLM to evaluate their alignment and generate new versions. Four faculty members compared both versions, using a structured questionnaire. The mean evaluation scores for original and LLM-generated versions are reported. The LLM identified new triggers for the clinical vignette to align it better with the LOs. Moreover, it restructured the tutor guide for better organization and flow and included thought-provoking questions. The medical information provided by the LLM was scientifically appropriate and accurate. The LLM-generated clinical vignette scored higher (3.0 vs. 1.25) for alignment with the LOs. However, the original version scored better for being educational level-appropriate (2.25 vs. 1.25) and adhering to PBL design (2.50 vs. 1.25). The LLM-generated tutor guide scored higher for better flow (3.0 vs. 1.25), comprehensive and relevant content (2.75 vs. 1.50), and thought-provoking questions (2.25 vs. 1.75). However, LLM-generated learning material lacked visual elements. In conclusion, this study demonstrated that Gemini could align and improve PBL learning materials. By leveraging the potential of LLMs while acknowledging their limitations, medical educators can create innovative and effective learning experiences for future physicians.NEW & NOTEWORTHY This study evaluated a large language model (LLM) (Gemini Advanced) for creating aligned problem-based learning (PBL) materials. The LLM improved the alignment of the clinical vignette with learning goals. The LLM also restructured the tutor guide and added thought-provoking questions. The LLM guide was well organized and informative, but the original vignette was considered more educational level-appropriate. Although the LLM could not generate visuals, AI can improve PBL materials, especially when combined with human expertise.

In development, the interrelationship between physiology and anatomy is challenging as learners must appreciate how physiological processes and anatomical structures change over time. In addition, the dynamic relationships between structure and function are often concealed largely due to the inaccessibility of the embryo in higher-order organisms. This makes it difficult for students to appreciate normal intricate balances or interpret the physiological consequences of developmental disruptions to normal embryological development. In this paper, the applicability of the chick embryo model for use in practical classes is explored as students can observe developmental processes firsthand within a controlled in ovo environment. Practical approaches involved in developing the chick embryo model are described and then expanded to demonstrate how the model can be utilized to showcase cardiovascular system development as an example. The model is further adapted to explore the effect of teratogenic disruptors such as ethanol on normal cardiovascular processes and highlights how prenatal alcohol exposure results in cardiovascular anomalies associated with fetal alcohol syndrome, such as septal defects and altered cardiac physiology. In class, students can directly observe chick development from 0 to 8 days postfertilization. Measurable outcomes, such as comparisons in septal thickness, are calculated, while questions and answers to stimulate student discussion around functional changes and the impact of maternal consumption of alcohol are provided as resource material. The method outlined uses relatively inexpensive materials and requires little space, making it a cost-effective educational tool to support student learning of embryology.NEW & NOTEWORTHY This study explores the use of the chick embryo model as a teaching aid to illustrate connections between anatomy and physiology during development. Providing direct observation opportunities, the model allows students to witness organ formation and the impact of teratogens, focusing on cardiovascular abnormalities associated with fetal alcohol syndrome. The paper outlines practical methodologies to assess developmental outcomes. Its adaptability, affordability, and ability to spark discussions make the model a valuable resource for diverse educational environments.

Despite extensive studies on large language models and their capability to respond to questions from various licensed exams, there has been limited focus on employing chatbots for specific subjects within the medical curriculum, specifically medical neuroscience. This research compared the performances of Claude 3.5 Sonnet (Anthropic), GPT-3.5 and GPT-4-1106 (OpenAI), Copilot free version (Microsoft), and Gemini 1.5 Flash (Google) versus students on multiple-choice questions (MCQs) from the medical neuroscience course database to evaluate chatbot reliability. Five successive attempts of each chatbot to answer 200 United States Medical Licensing Examination (USMLE)-style questions were evaluated based on accuracy, relevance, and comprehensiveness. MCQs were categorized into 12 categories/topics. The results indicated that, at the current level of development, selected AI-driven chatbots, on average, can accurately answer 67.2% of MCQs from the medical neuroscience course, which is 7.4% below the students' average. However, Claude and GPT-4 outperformed other chatbots, with 83% and 81.7% correct answers, which is better than the average student result. They were followed by Copilot (59.5%), GPT-3.5 (58.3%), and Gemini (53.6%). Concerning different categories, Neurocytology, Embryology, and Diencephalon were the three best topics, with average results of 78.1-86.7%, and the lowest results were for Brain stem, Special senses, and Cerebellum, with 54.4-57.7% correct answers. Our study suggested that Claude and GPT-4 are currently two of the most evolved chatbots. They exhibit proficiency in answering MCQs related to neuroscience that surpasses that of the average medical student. This breakthrough indicates a significant milestone in how AI can supplement and enhance educational tools and techniques.NEW & NOTEWORTHY This research evaluates the effectiveness of different AI-driven large language models (Claude, ChatGPT, Copilot, and Gemini) compared to medical students in answering neuroscience questions. The study offers insights into the specific areas of neuroscience in which these chatbots may excel or have limitations, providing a comprehensive analysis of chatbots' current capabilities in processing and interacting with certain topics of the basic medical sciences curriculum.