Purpose: Learning focus has shifted from conventional teacher-centered to student-centered; therefore, methods used to support and encourage learners must be considered. Meanwhile, the individual differences between learners should be taken into consideration by medical educators. Aiming to achieve a better learning experience, the current study investigates the relationship between personality traits and learning styles and their effect on students’ academic achievement. Subjects and Methods: An analytical, cross-sectional study, encompassing a sample of 333 undergraduate first-year medical students, at the Faculty of Medicine, Suez Canal University in Egypt was conducted. The Big Five personality traits test and VARK learning styles questionnaires were used to assess students’ personality traits and learning styles, respectively, in the medical education module which is a part of the Foundation II module. The former is five weeks’ duration and is followed by a summative exam at the end. Students’ academic achievement was determined from their grades in Foundation II module by the end of the semester. Results: Study findings generally indicate that the highest domain of learning styles among students is the kinesthetic domain. A statistically significant difference between males and females regarding their learning style preferences was detected. Also, there was a statistically significant relationship between auditory and kinesthetic learning styles and academic achievement. Though there was a significant relationship between kinesthetic learning style with both openness traits and academic achievement, no significant statistical relationship was found between any of the personality traits and academic achievement. Conclusion: This study concludes that learning style could affect students’ academic achievement. It reveals that the kinesthetic learning style is the most preferred learning style among our medical students, also the agreeableness and openness traits were the most common personality traits among them. No significant relationship was detected between personality traits and academic achievement.

Background: With the strike of Covid-19, an unprecedented rapid shift to remote learning happened worldwide with a paradigm shift to online learning from an institutional adjuvant luxury package and learner choice into a forced solo choice. This raises the question of quality assurance. While some groups have already established standards for online courses, teaching and programs yet very little information is included on methodology of their development and very little emphasis is placed on the online learning experience. Nevertheless, no work has been done specifically for medical education institutions.Aim: To develop a set of descriptors for best practice in online learning in medical education utilizing existing expertise and needs.Methods: This work utilizes a qualitative multistage approach to identify the descriptors of best practice in online learning starting with a question guided focus group, thematic analysis, Delphi technique and an expert consensus session done simultaneously for triangulation. This was done involving 32 institution in 19 countries.Results: This materialized into the development of a set of standards, indicators, and development of a checklist for each standard area. The standard areas identified were organizational capacity, educational effectiveness, and human resources each of which listed a number of standards. Expert consensus sessions identified the need for qualification of data and thus the development of indicators for best practice.Conclusion: Standards are needed for online learning experience and their development and redesign is situational and needs to be enhanced methodologically in axes that are pertaining to the needs of the education community. Taking such axes into consideration by educators and institutions will lead to planning and implementing successful online learning activities, while taking them into consideration by the evaluators will help them conduct comprehensive audits and provide stakeholders with highly informative evaluation reports.

Background: This is a practice guide for the evaluation tool specifically created to objectively evaluate longitudinal faculty development programs (FDP) using the “5×2 -D backward planning faculty development model”. It was necessary to create this tool as existing evaluation methods are designed to evaluate linear faculty development models with a specific endpoint. This backward planning approach is a cyclical model without an endpoint, consisting of 5 dynamic steps that are flexible and interchangeable, therefore can be a base for an evaluation tool that is objective and takes into account all the domains of the FDP in contrast to the existing, traditional, linear evaluation tools which focus on individual aspects of the program. The developed tool will target evaluation of longitudinal faculty development programs regardless of how they were planned. Methodology: Deductive qualitative grounded theory approach was used. Evaluation questions were generated and tailored based on the 5 × 2-D model followed by 2 Delphi rounds to finalize them. Based on the finalized evaluation questions from the results of the Delphi rounds, two online focus group discussions (FGDs) were conducted to deduce the indicators, data sources and data collection method. Results: Based on the suggested additions, the authors added 1 new question to domains B, with a total of 42 modifications, such as wording changes or discarding or merging questions. Some domains received no comments, therefore, were not included in round 2. For each evaluation question, authors generated indicators, data sources and data collection methods during the FGD. Conclusion: The methodology used to develop this tool takes into account expert opinions. Comprehensiveness of this tool makes it an ideal evaluation tool during self-evaluation or external quality assurance for longitudinal FDP. After its validation and testing, this practice guide can be used worldwide, along with the provided indicators which can be quantified and used to suit the local context.

Background: Programmatic assessment supports flexible learning and individual progression but challenges educators to develop frequent assessments reflecting different competencies. The continuous creation of large volumes of assessment items, in a consistent format and comparatively restricted time, is laborious. The application of technological innovations, including artificial intelligence (AI), has been tried to address this challenge. A major concern raised is the validity of the information produced by AI tools, and if not properly verified, it can produce inaccurate and therefore inappropriate assessments. Objective: This study was designed to examine the content validity and consistency of different AI chatbots in creating single best answer (SBA) questions, a refined format of multiple choice questions better suited to assess higher levels of knowledge, for undergraduate medical students. Methods: This study followed 3 steps. First, 3 researchers used a unified prompt script to generate 10 SBA questions across 4 chatbot platforms. Second, assessors evaluated the chatbot outputs for consistency by identifying similarities and differences between users and across chatbots. With 3 assessors and 10 learning objectives, the maximum possible score for any individual chatbot was 30. Third, 7 assessors internally moderated the questions using a rating scale developed by the research team to evaluate scientific accuracy and educational quality. Results: In response to the prompts, all chatbots generated 10 questions each, except Bing, which failed to respond to 1 prompt. ChatGPT-4 exhibited the highest variation in question generation but did not fully satisfy the “cover test.” Gemini performed well across most evaluation criteria, except for item balance, and relied heavily on the vignette for answers but showed a preference for one answer option. Bing scored low in most evaluation areas but generated appropriately structured lead-in questions. SBA questions from GPT-3.5, Gemini, and ChatGPT-4 had similar Item Content Validity Index and Scale Level Content Validity Index values, while the Krippendorff alpha coefficient was low (0.016). Bing performed poorly in content clarity, overall validity, and item construction accuracy. A 2-way ANOVA without replication revealed statistically significant differences among chatbots and domains (P<.05). However, the Tukey-Kramer HSD (honestly significant difference) post hoc test showed no significant pairwise differences between individual chatbots, as all comparisons had P values >.05 and overlapping CIs. Conclusions: AI chatbots can aid the production of questions aligned with learning objectives, and individual chatbots have their own strengths and weaknesses. Nevertheless, all require expert evaluation to ensure their suitability for use. Using AI to generate SBA prompts us to reconsider Bloom’s taxonomy of the cognitive domain, which traditionally positions creation as the highest level of cognition.

Ulster University’s School of Medicine embraces a problem-based learning (PBL) approach, yet crafting scenarios for this method poses challenges, requiring collaboration among medical and academic experts who are often difficult to convene. This obstacle can compromise scenario quality and ultimately impede students’ learning experiences. To address this issue, the school trialed the use of AI technology to develop a case scenario focusing on headaches caused by cerebral haemorrhage. The process involved a dialogue between a single “author” and ChatGPT, with their outputs combined into a complete clinical case adhering to the school’s standard template. Six experienced PBL tutors conducted quality checks on the scenario. The tutors did not immediately endorse its use, recommending further enhancements. Suggestions included updating terminology, names, spelling, and protocols to align with current best practices, providing additional explanations such as interventions and improvements post-initial stability, incorporating real scans instead of descriptions, reviewing symptoms and timelines for realism, and addressing comprehension issues by refraining from directly providing answers and including probing questions instead. From this trial, several valuable lessons were learned: AI can assist a single author in crafting medical scenarios, easing the challenges of organizing expert teams. However, the author’s role shifts to reviewing and enhancing depth, guided by a template, with clinician input crucial for authenticity. ChatGPT respects patient data privacy and confidentiality by abstaining from providing scanned images, and while AI can generate discussion questions for tutorials, it may require modification to enhance specificity and provoke critical thought. Furthermore, AI can generate multiple-choice questions and compile reading resources to support self-directed learning. Overall, adopting AI technology can improve efficiency in the case-writing process.