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Abstract In accordance with the outcomes from a number of reports, there are cognitive and academic improvements derived from chess learning and chess playing. This evidence, however, endures three key limitations: (a) ignoring theoretical premises about the concept of transfer, (b) several shortcomings regarding ideal experiment guidelines, and (c) an uncritical faith in null hypothesis significance testing (NHST) statistical analyses. The present review scrutinized the NHST outcomes from 45 studies describing chess instruction interventions (n = 12,705) in nineteen countries that targeted cognitive ability (100 tests) and academic performance (108 tests), with a mean Hedge’s effect size g = 572 (95% CI = [0.127, 1.062]). There was a lower average statistical power, a higher proportion of false positive outcomes, larger publication biases, and lower replication rates for the studies in the academic performance domain than in the cognitive ability domain. These findings raised reasonable concerns over the evidence about the benefits of chess instruction, which was particularly problematic regarding academic achievement outcomes. Chess should perhaps be regularly taught, however, regardless of whether it has a direct impact or not in cognitive abilities and academic performance, because these are far transfer targets. The more likely impact of chess on near transfer outcomes from higher quality studies remains at present unexplored.

BackgroundOver the last few years, there has been increasing attention to the involvement of the central nervous system in Duchenne muscular dystrophy (DMD). The aim of this study was to assess the spectrum of neurodevelopmental and mental disorders and possible required intervention in our cohort of 264 boys and adults with DMD.MethodsWe retrospectively analysed clinical notes and psychological assessments, including routinely performed cognitive tests and clinical observations. Intelligence quotients and site of mutations were also noted.Results103/264 individuals (39%) had symptoms compatible with one of the following diagnoses: attention deficit hyperactivity disorder (ADHD) (n=26), autism spectrum disorder (ASD) (n=11), depressive mood/disruptive mood dysregulation disorder (n=27), anxiety disorder (n=17), obsessive-compulsive disorder (n=2), psychosis risk syndrome (n=7), and 13 had a more complex phenotype. ADHD and ASD were more frequent in infancy, emotional dysregulation during early adolescence, and psychosis and more severe phobias in older boys and adults. The risk of developing these disorders did not increase with the concomitant involvement of the dystrophin isoforms Dp140 and Dp71. Pharmacological treatment was suggested for 48 individuals but was started only in 24, as it was refused by the remaining 24 families.ConclusionsOur findings confirm that neurodevelopmental and mental disorders are common in DMD and are likely to have a multifactorial nature. These findings support the need for disease-specific assessments and the need to increase awareness of the possible behavioural and social difficulties among families and healthcare professionals.

An aspect of metacognition associated with broader functional abilities in several clinical conditions, but previously unexamined in autism, is self-assessment (i.e., the ability to accurately self-evaluate one’s own performance). We compared self-assessment between 37 autistic adults without intellectual disability to 39 non-autistic (NA) controls on a series of three general cognitive and three social cognitive tasks. Whereas autistic adults and NA adults did not differ in their self-assessment accuracy on general cognitive tasks, they did on social cognitive tasks, with autistic adults demonstrating lower accuracy. The direction of their inaccuracy was variable (i.e., both over and underestimation), and self-assessment was largely unrelated to their level of social functioning. Over versus underestimation may have different functional implications, and warrants future investigation.

BACKGROUND Artificial intelligence (AI) application is increasingly used in all fields, especially, in medicine. However, for the successful incorporation of AI-driven tools into medicine, healthcare professional should be equipped with the necessary knowledge. From that, we aimed to assess the AI readiness among medical students in Jordan. METHODS A cross-sectional survey was conducted among medical students across 6 Jordanian universities. Prevalidated Medical Artificial Intelligence Readiness Scale for Medical Students questionnaire was used. The questionnaire was distributed through social media groups of students. SPSS v.27 was used for analysis. RESULTS A total of 858 responses were collected. The mean AI readiness score was 64.2%. Students scored more in the ability domain with a mean of 22.57. We found that academic performance (Grade point average) positively associated with overall AI readiness (P = .023), and prior exposure to AI through formal education or experience significantly enhances readiness (P = .009). In contrast, AI readiness levels did not significantly vary across different medical schools in Jordan. Notably, most students (84%) did not receive a formal education about AI from their schools. CONCLUSION Incorporation of AI education in medical curricula is crucial to close knowledge gaps and ensure that students are prepared for the use of AI in their future career. Our findings highlight the importance of preparing students to engage with AI technologies, and to be equipped with the necessary knowledge about its aspect.

Background Measuring artificial intelligence (AI) readiness among medical students is essential to assess how prepared future doctors are to work with AI technology. Therefore, this study aimed to examine the factors influencing AI readiness among medical students at Kermanshah University of Medical Sciences, both by evaluating the current situation and considering future developments. Methods This was a cross-sectional descriptive-analytical study. The statistical population consisted of 800 first- to fifth-year medical students selected through convenient sampling at Kermanshah University of Medical Sciences from November to March 2023. The data collection tools were demographic checklists and Persian version questionnaire of the medical artificial intelligence readiness scale for medical students (MAIRS-MS). The data were analyzed at a significance level of P  < 0.05 using independent t-test, and analysis of variance (ANOVA) tests through SPSS-24 software. Results Most of the students were male (56.13%). The overall score for medical AI readiness was 70.59 ± 19.24 out of a maximum possible score of 110. Students had the highest mean score of 9.73 ± 2.96 out of 15 in vision and the lowest mean score of 25.74 ± 7.52 out of 40 in ability. The overall mean of AI readiness (71.84 ± 18.27) was higher in females than males (69.62 ± 19.93), but this difference was not significant ( p  = 0.106). Furthermore, the mean total score of AI readiness increased with the increasing age of the students. Conclusion Our findings underscore the need to prepare students to work with AI technologies and to provide them with the essential knowledge and skills across different areas of AI. Accordingly, the Kermanshah University of Medical Sciences student’s education unit should set up more AI training centers to provide and introduce basic artificial intelligence courses. Moreover, universities should identify the needs of students based on scientific evidence, and the medical education system should design AI training programs in its educational framework in the same direction.

The foundation for talent development as a framework for gifted education can be found in a synthesis of the psychological literature on creativity, eminence, giftedness, and high performance. The talent development framework acknowledges the contributions of both general cognitive ability and domain-specific abilities to achievement, as well as the malleability of these ability constructs. Talent development is also consistent with research on the contributions of non-cognitive or psychosocial factors to school achievement, as well as studies on factors that influence the attainment of scholarly productivity and artistry within specific domains of non-academic talent. Although there are several theoretical frameworks and models of giftedness, talent development, ability, and intelligence, each with varied areas of emphasis and desired outcomes, the research base and practical applications for the talent development megamodel (TDMM) can serve as a guide to leaders and school administrators in making fiscal and programmatic decisions that maximize short- and long-term impacts for individuals and society. In this article, we discuss some of the practical implications of the model for assessment, curriculum and instruction, and psychosocial development within a school context.

Background As artificial intelligence (AI) becomes increasingly integral to healthcare, preparing medical and health sciences students to engage with AI technologies is critical. Objectives This study investigates the perceived AI readiness of medical and health sciences students in Saudi Arabia, focusing on four domains: cognition, ability, vision, and ethical perspectives, using the Medical Artificial Intelligences Readiness Scale for Medical Students (MAIRS-MS). Methods A cross-sectional survey was conducted between October and November 2023, targeting students from various universities and medical schools in Saudi Arabia. A total of 1,221 students e-consented to participate. Data were collected via a 20-minute Google Form survey, incorporating a 22-item MAIRS-MS scale. Descriptive and multivariate statistical analyses were performed using Stata version 16.0. Cronbach alpha was calculated to ensure reliability, and least squares linear regression was used to explore relationships between students’ demographics and their AI readiness scores. Results The overall mean AI readiness score was 62 out of 110, indicating a moderate level of readiness. Domain-specific scores revealed generally consistent levels of readiness: cognition (58%, 23.2/40), ability (57%, 22.8/40), vision (54%, 8.1/15) and ethics (57%, 8.5/15). Nearly 44.5% of students believed AI-related courses should be mandatory whereas only 41% reported having such a required course in their program. Conclusions Medical and health sciences students in Saudi Arabia demonstrate moderate AI readiness across cognition, ability, vision, and ethics, indicating both a solid foundation and areas for growth. Enhancing AI curricula and emphasizing practical, ethical, and forward-thinking skills can better equip future healthcare professionals for an AI-driven future.

Orthographic knowledge is one of several contributors to developing literacy skills. However, our understanding of how orthographic knowledge contributes to both spelling and reading is incomplete due to a lack of consistency in defining and measuring orthographic knowledge. The goal of the present study was to empirically test whether or not orthographic knowledge could be construed as a multi-dimensional construct that contributes to reading and spelling skill in school-aged children. Using regression analyses, the current study supports the conceptualization of orthographic knowledge as a multi-dimensional construct, consisting of both word specific and general orthographic knowledge. For school-aged children, both types of orthographic knowledge make separate and unique contributions to both reading and spelling, over and above the contributions of phonological skills. Results illustrate the importance of orthographic knowledge to developing literacy skills.

Non-academic characteristics and traits, such as academic self-efficacy and conscientiousness, have maintained the interest of higher education researchers for decades. A considerable amount of research has found that these non-cognitive variables (NCVs) are generally predictive of undergraduate success. However, most prior studies have focused on the use of NCVs in 4-year colleges, and understanding if and how these measures predict the academic trajectories of community college students has received less attention. As past work has indicated that NCVs are differentially predictive for students from different backgrounds, such an examination is needed. Drawing on data from two diverse community colleges, our study addresses this need by exploring if seven popular NCVs predict both short and longer-term student outcomes, how these measures are related to help-seeking behaviors, and their utility in identifying students at risk of serious academic failures. We find statistically significant relationships between NCVs and GPA outcomes but conclude that their practical utility appears trivial.

Background The faculty development programs associated with positive outcomes are the ones that are designed in accordance with organizational needs, are based on a theoretical framework, target inter-professional learning and use multiple teaching strategies to teach a single skill. Rather than being short, one-time events, these programs are long term with contextual engagement of participants. To meet this challenge, a six-month long certificate course in health professions education, based on constructivist theory, was designed. The objective of this study was to evaluate the impact of this focused training, at Kirkpatrick level II, on learning of item writing skills by the participants. Methods This quasi-experimental study was conducted from January 2019 to June 2020 at National University of Medical Sciences, Pakistan. A total of 133 faculty members were enrolled in the program. Of these, data from 124 students (75 male and 49 female participants), who had passed our CHPE program, was included in the study. The longitudinal engagement comprised of three steps. In step 1(pre-intervention) participants submitted 5MCQs each, resulting in a total of 620 MCQs. For the step 2 (intervention), a six-hour workshop on writing single best MCQs with peer and faculty feedback during the five-day long face to face session was conducted for all the participants. Subsequently four courses including learning theories, curriculum planning, teaching and learning and assessment were delivered over the course of next six months with special focus on building participants’ capacity on blueprinting so as to highlight the linkages between various courses and their significance through a single snapshot document. An assignment on writing single best MCQs was given as part of course on assessment and faculty gave individual feedback to participants. Finally, for the step 3- post intervention, the skills were tested in the end of program exam. A validated checklist was used to score the items on quality parameters. A total of 1800 MCQ items (600 at each step) were analyzed at these three points of intervention; and the scores obtained were compared to assess if the item writing skills had improved. Results The average scores across three steps showed an increasing trend. The Friedman ANOVA test results indicated that there was a statistically significant difference across the three time points with a value of test statistics χ 2 (2, n  = 600 = 955.86, P  < 0.05). The Wilcoxon signed rank test showed significant differences between each pair of steps, supporting the finding of an improvement trend in the scores across all steps. Step 3 had the highest value of mean ranks (20.24 ± 0.05) and a higher median score (Md = 21). Bonferroni correction showed that at the threshold level of 0.0167, all three comparisons are still statistically significant. Conclusion Contextual engagement of participants through a longitudinal faculty development program, incorporating varied teaching techniques like individual and group work, practice and prompt feedback and peer review, improves participants’ MCQ writing skills, such that they construct MCQs that assess higher cognitive skills with fewer item writing flaws.