Mapping hospital data to characterize residents' educational experiences.

BACKGROUND: Experiential learning through patient care is fundamental to graduate medical education. Despite this, the actual content to which trainees are exposed in clinical practice is difficult to quantify and is poorly characterized. There remains an unmet need to define precisely how residents' patient care activities inform their educational experience.  METHODS: Using a recently-described crosswalk tool, we mapped principal ICD-10 discharge diagnosis codes to American Board of Internal Medicine (ABIM) content at four training hospitals of a single Internal Medicine (IM) Residency Program over one academic year to characterize and compare residents' clinical educational experiences. Frequencies of broad content categories and more specific condition categories were compared across sites to profile residents' aggregate inpatient clinical experiences and drive curricular change. RESULTS: There were 18,604 discharges from inpatient resident teams during the study period. The crosswalk captured > 95% of discharges at each site. Infectious Disease (ranging 17.4 to 39.5% of total discharges) and Cardiovascular Disease (15.8 to 38.2%) represented the most common content categories at each site. Several content areas (Allergy/Immunology, Dermatology, Obstetrics/Gynecology, Ophthalmology, Otolaryngology/Dental Medicine) were notably underrepresented (≤ 1% at each site). There were significant differences in the frequencies of conditions within most content categories, suggesting that residents experience distinct site-specific clinical content during their inpatient training. CONCLUSIONS: There were substantial differences in the clinical content experienced by our residents across hospital sites, prompting several important programmatic and curricular changes to enrich our residents' hospital-based educational experiences.

Life as an Intelligence Test: Intelligence, Education, and Behavioral Genetics.

Using the large datasets available with new gene sequencing and biobank projects, behavioral geneticists are developing tools that attempt to predict individual intelligence based on genetics. These predictive tools are meant to enable a 'precision education' that will transform society. These technological developments have not changed the fundamental aims of a program with a long history. Behavioral genetics is continuous with previous attempts to match personal characteristics to heredity, such as sociobiology and evolutionary psychology, and threatens racial and other forms of bias. From these older paradigms, it inherits an understanding of intelligence as informational processing shaped by mechanistic and computational metaphors as well as a view of society and education organized around competition. Because of these influences, these models misdescribe fundamental aspects of human engagement with the world and disregard other concepts of intelligence, which creates problems for the precision education that researchers hope to construct using genetic knowledge.

Huxleyan utopia or Huxleyan dystopia? "Scientific humanism", Faure's legacy and the ascendancy of neuroliberalism in education.

In addition to the longstanding threat posed by narrow economism, faith in the possibility of peace and progress through democratic politics - central to the humanistic vision of the 1972 Faure report - today faces additional challenges. These challenges include the ascendancy of neurocentrism in the global policyscape. Whereas the effects of neoliberalism on education have been extensively critiqued, the implications of a newer, related ideological framework known as neuroliberalism remain under-theorised. Neuroliberalism combines neoliberal ideas concerning the role of markets in addressing social problems with beliefs about human nature ostensibly grounded in the behavioural, psychological and neurological sciences. This article critically examines a recent initiative of one of UNESCO's Category 1 Institutes - the Mahatma Gandhi Institute of Education for Peace and Sustainable Development (MGIEP) - that seeks to mainstream neuroscience and digital technology within global educational policy. Comparing the visions of the 1972 Faure, the 1996 Delors and the 2021 Futures of Education reports with MGIEP's International Science and Evidence Based Education Assessment (ISEEA), the authors analyse continuity and change in UNESCO's attempts to articulate a vision of "scientific humanism" which advocates the use of science for the betterment of humanity. They argue that ISEEA's overall recommendations - as represented in its Summary for Decision Makers (SDM) - reinforce a reductive, depoliticised vision of education which threatens to exacerbate educational inequality while enhancing the profits and power of Big Tech. These recommendations exemplify a neuroliberal turn in global education policy discourse, marking a stark departure from the central focus on ethics and democratic politics characteristic of UNESCO's landmark education reports. Reanimating, in cruder form, visions of a scientifically-organised utopia of the kind that attracted UNESCO's inaugural Director-General, Julian Huxley, ISEEA's recommendations actually point towards the sort of dystopian "brave new world" of which his brother, Aldous Huxley, warned.

Utopie huxleyenne ou dystopie huxleyenne? « L'humanisme scientifique ¼, l'héritage de Faure et la montée du néolibéralisme dans l'éducation ­ Outre faire face à la menace que pose depuis longtemps l'économisme étroit, la foi dans la possibilité que la politique démocratique peut produire paix et progrès ­ un pilier de la vision humaniste du rapport Faure paru en 1972 ­ se heurte aujourd'hui à d'autres défis, entre autres à la montée du neurocentrisme dans le champ politique mondial. Tandis que les effets du néolibéralisme sur l'éducation ont été abondamment critiqués, peu d'hypothèses ont été formulées au sujet de ce qu'implique une notion plus récente, mais liée à lui sur le plan idéologique et connue sous le nom de neurolibéralisme. Le neurolibéralisme associe des idées néolibérales sur le rôle des marchés pour résoudre des problèmes sociaux avec la conviction que la nature humaine est prétendument ancrée dans les sciences comportementales, psychologiques et neurologiques. Cet article porte un regard critique sur une initiative récente d'un des instituts de catégorie 1 de l'UNESCO, l'Institut Mahatma Gandhi d'éducation pour la paix et le développement durable (MGIEP), qui cherche à intégrer les neurosciences et la technologie du numérique dans la politique mondiale de l'éducation. Les auteurs comparent les visions des rapports Faure en 1972 et Delors en 1996 et du rapport de 2021 sur les futurs de l'éducation avec l'évaluation internationale de l'éducation basée sur la science et des éléments concrets (ISEEA) réalisée par le MGIEP, pour analyser la continuité et les changements dans les tentatives de l'UNESCO d'articuler une vision de « l'humanisme scientifique ¼ prônant d'utiliser la science pour améliorer l'humanité. Ils avancent que les recommandations principales de l'ISEEA, telles que l'évaluation les présente dans son récapitulatif à l'intention des décideurs, renforce une vision réductrice et dépolitisée de l'éducation, qui menace d'exacerber les inégalités en matière d'éducation tout en accroissant les profits et la puissance des big tech. Ces recommandations illustrent un tournant neurolibéral dans le discours mondial sur la politique de l'éducation, qui se démarque absolument de l'intérêt central pour l'éthique et la politique démocratique, caractéristiques des rapports historiques de l'UNESCO sur l'éducation. Ravivant, sous une forme plus rudimentaire, des visions d'une utopie structurée scientifiquement du type de celles qui attiraient le premier directeur général de l'UNESCO, Julian Huxley, les recommandations de l'ISEEA laissent en réalité entrevoir la sorte de « meilleur des mondes ¼ dystopique contre laquelle son frère, Aldous Huxley, mettait en garde.

The contributions of AI in the development of ideological and political perspectives in education.

Opportunities and difficulties have arisen for the educational system as a result of the development of "artificial intelligence" technology. In the era of "artificial intelligence", the main features of ideological and political education in colleges are the development of the intelligence revolution, the development of teaching concepts, and the omnipotence of teaching content, and teaching methods. This study further explores the necessity and development of "artificial intelligence technology" in college ideological and political education through a questionnaire survey, and promotes the organic integration of artificial intelligence and ideological and political education. The results show that [1] College students have positive attitudes toward the application of artificial intelligence in college ideological and political education, and college students are looking forward to the intelligent services and changes brought by artificial intelligence technology in college ideological and political education [2]. According to the results of the questionnaire survey, this paper proposes the development path of college ideological and political education in the era of artificial intelligence, that is, schools and teachers need to improve the transformation of traditional education as well as the construction of contemporary Internet education. This study offers the possibility of interdisciplinary research, expands the scope of ideological and political education research, and provides a reference for front-line teaching to a certain extent.

Artificial Intelligence for Personalised Ophthalmology Residency Training.

Residency training in medicine lays the foundation for future medical doctors. In real-world settings, training centers face challenges in trying to create balanced residency programs, with cases encountered by residents not always being fairly distributed among them. In recent years, there has been a tremendous advancement in developing artificial intelligence (AI)-based algorithms with human expert guidance for medical imaging segmentation, classification, and prediction. In this paper, we turned our attention from training machines to letting them train us and developed an AI framework for personalised case-based ophthalmology residency training. The framework is built on two components: (1) a deep learning (DL) model and (2) an expert-system-powered case allocation algorithm. The DL model is trained on publicly available datasets by means of contrastive learning and can classify retinal diseases from color fundus photographs (CFPs). Patients visiting the retina clinic will have a CFP performed and afterward, the image will be interpreted by the DL model, which will give a presumptive diagnosis. This diagnosis is then passed to a case allocation algorithm which selects the resident who would most benefit from the specific case, based on their case history and performance. At the end of each case, the attending expert physician assesses the resident's performance based on standardised examination files, and the results are immediately updated in their portfolio. Our approach provides a structure for future precision medical education in ophthalmology.

Utility of a slopes difference test for probing longitudinal multilevel aptitude treatment interactions: a simulation.

To determine which interventions work best for which students, precision education researchers can examine aptitude-treatment interactions (ATI) or skill-by-treatment interactions (STI) using longitudinal multilevel modeling. Probing techniques like the slopes difference test fit an ATI or STI framework, but power for using slopes difference tests in longitudinal multilevel modeling is unknown. The current study used simulation to determine which design factors influence the power of slopes difference tests. Design factors included effect size, number of waves, number of clusters, participants per cluster, proportion of assignment to the treatment group, and intraclass correlation. Of these factors, effect size, number of waves, number of clusters, and participants per cluster were the strongest determinants of power, model convergence, and rates of singularity. Slopes difference tests had greater power in longitudinal multilevel modeling than where it is originally utilized: multiple regression.

Artificial Intelligence and Radiology Education.

Implementation of artificial intelligence (AI) applications into clinical practice requires AI-savvy radiologists to ensure the safe, ethical, and effective use of these systems for patient care. Increasing demand for AI education reflects recognition of the translation of AI applications from research to clinical practice, with positive trainee attitudes regarding the influence of AI on radiology. However, barriers to AI education, such as limited access to resources, predispose to insufficient preparation for the effective use of AI in practice. In response, national organizations have sponsored formal and self-directed learning courses to provide introductory content on imaging informatics and AI. Foundational courses, such as the National Imaging Informatics Course - Radiology and the Radiological Society of North America Imaging AI Certificate, lay a framework for trainees to explore the creation, deployment, and critical evaluation of AI applications. This report includes additional resources for formal programming courses, video series from leading organizations, and blogs from AI and informatics communities. Furthermore, the scope of "AI and radiology education" includes AI-augmented radiology education, with emphasis on the potential for "precision education" that creates personalized experiences for trainees by accounting for varying learning styles and inconsistent, possibly deficient, clinical case volume. © RSNA, 2022 Keywords: Use of AI in Education, Impact of AI on Education, Artificial Intelligence, Medical Education, Imaging Informatics, Natural Language Processing, Precision Education.

Nature vs. Nurture in Precision Education: Insights of Parents and the Public.

BACKGROUND: The philosophical debate about the roles of nature versus nurture in human flourishing is not new. But the rise of precision education-a growing field of research that encourages the use of genetic data to inform educational trajectory and interventions to better meet student needs-has renewed historical and ethical concerns. A major worry is that "genetic hype" may skew public perceptions toward a deterministic perception of the child's educational trajectory, regardless of the child's capacities, and underestimation of environmental factors affecting educational outcomes. We tested this hypothesis with parents and adults from the general public in the US. METHODS: A newly developed computerized implicit association test (IAT) to assess automatic associations between genetics or environments and student behaviors that are associated with educational achievement was administered to samples of parents of children below 21 years old (n = 450) and adults from the general public (n = 419). The samples were representative of the adult US population and adjusted to oversample Black/African American participants. An overall D score for participants' IATs (range: [-2, 2]) was calculated on the basis of the speed of participants' responses. RESULTS: The mean IAT score for both samples indicated stronger association between the quality of being a good student and environment rather than genetics (parents: mean=-0.146, t = -6.56, p < 0.001; general public: mean = -0.249, t = -9.45, p < 0.0001). Younger participants from the general public showed a stronger association between genetics and educational success than middle-aged participants (ß = -0.301, p = 0.006). CONCLUSION: The views of parents and the general public on behavioral genetics and education are complex but call for investment in creating educational environments that are supportive of student success. Future research is needed to understand differences across age groups and to explore views of other stakeholders involved in determining children's educational trajectories about the roles of nature versus nurture in precision education.

Testing the talented child: Direct-to-consumer genetic talent tests in China.

One controversial area of direct-to-consumer genetic testing in China is "genetic talent testing" for children. In this study, I show that while experts criticize genetic talent testing as unscientific, the persistence of genetic talent testing is not merely a product of parents' scientific illiteracy. Instead, genetic talent testing reflects parents' pragmatic use of technology in response to the parenting pressures in contemporary China. Parents see the results of genetic talent testing as offering an advantage for their children when combined with the intensive parenting strategy of precision education. Drawing on the sociology of testing, I argue how genetic talent testing in China is a product of broader concerns about population quality and can potentially reshape how parents imagine quality children through the theory of multiple intelligences. My study of this "off label" use of direct-to-consumer genetic testing also suggests that scientists need to broaden their imagination of potential misuses of their technologies.

Profiling of Learners in Medical Schools as a Move Toward Precision Education: Protocol for a Scoping Review.

BACKGROUND: Academic experiences seek to get the best out of learners, maximizing performance and developing the skills and competencies needed to foster lifelong learning. The more personalized and tailored the academic experience among learners, the better the outcome. Precision education is a novel approach to research and practice, which is concerned with identifying and tailoring education to the precise needs of the learner. An emerging area of precision education is using data to develop learner profiles for a better understanding of individual learners relative to the characteristics and competencies of lifelong learners. OBJECTIVE: This scoping review aims to identify literature that reports on profiling learners within medical schools. Our review, as described in this paper, will describe the characteristics being measured, the methods and data sources used to generate profiles, and the resulting profiles that emerge. This review aims to provide guidance to those supporting medical school learners on the current state of learner profiling. METHODS: This scoping review will use the Population, Concept, and Context framework, published by Joanna Briggs Institute, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guidelines. The search strategy was developed in collaboration with a library specialist. An initial search was conducted in PubMed, ERIC, Google Scholar, Cochrane, CINAHL, and SCOPUS. Data will be extracted, and 2 authors will undertake the screening procedure using the Preferred Reporting Items for Systematic reviews and Meta-Analyses Extension for Scoping Reviews checklist. RESULTS: The database searches yielded 166 results, and title and abstract screening of 135 extracted articles is currently underway after eliminating 31 duplicates. We anticipate the scoping review to be completed in the first week of October 2022. The final scoping review will present the findings in a narrative and pictorial fashion. CONCLUSIONS: This review will help guide scholars looking to understand the current state of learner profiling within medical schools. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/41828.

The double helix at school: Behavioral genetics, disability, and precision education.

The prospect of using behavioral genetic data in schools is gaining momentum in the U.S., with some scholars advocating for the tailoring of educational interventions to students' genetic makeup ("precision education"). Public perspectives on testing for and using behavioral genetic data in schools can affect policies but are unknown. We explored public views in the U.S. (n = 419) on key issues in precision education. The introduction of a child's behavioral genetic information regarding Attention-Deficit/Hyperactivity-Disorder was associated with beliefs that such data should be considered in educational planning for the child and increased medicalization, but also a belief in treatment efficacy. Most participants expressed interest in learning about children's behavioral genetic predispositions but would disapprove of testing without parental consent. Differences by participants' race, ethnicity and educational attainment were observed. Our findings indicate the public's complex understanding of genetic information and the challenges for wide implementation of precision education in the U.S.

Analysis of AI Precision Education Strategy for Small Private Online Courses.

In recent years, the learning efficacy of online to offline (O2O) teaching methods seems to outperform traditional teaching methods in the field of education. Students can use a small private online course (SPOC) teaching platform to preview class-related materials, learn basic knowledge, and enhance the practical experience of system development in offline courses. The research team applied an artificial intelligence (AI) precision education strategy to design a teaching experiment that evaluated whether this approach may lead to better learning outcomes. In addition to questionnaire surveys to ascertain students' attitudes toward and their satisfaction with learning, this study employed in-depth interviews to understand a potential influence on changes in teachers' curriculum design and teaching approaches when SPOCs was integrated into the traditional university classroom, as well as the impact of the AI precision education model. The results showed that the AI precision education model may facilitate students' learning experience and enhance student achievement.