Students suffer from stress as a result of many factors, including educational unpreparedness, financial strain and inability to integrate socially. This mixed methods study aimed to investigate stress levels of undergraduate students in a post-1992, Scottish university and the potential for measures of stress to act as an indicator of a student’s intention to continue. The study sampled primarily engaged students as tests were administered during timetabled classes and required the students’ voluntary participation. The level of perceived stress reported by these students appeared to be high (mean PSS-14 scores of 18.42 (SD 8.452) and 24.57 (SD 8.969) for males and females, respectively) and was coupled with intention to drop out across all study levels (12.1% of students sampled reported ‘seriously considered dropping out’). Perceived stress score predicted a student’s intention to withdraw but this association did not transfer to actual withdrawal, suggesting that other factors, most likely coping mechanisms, play a part in mediating the withdrawal behaviour. Unfortunately, despite the seemingly high levels of stress and potential worry over dropout, students are reluctant to seek support and many were unaware of the support services available through the university. Given the engaged nature of these students, their feelings are unlikely to be made known to staff as they will not necessarily register on non-attendance lists or be flagged because of missing assignments, which are often used as indicators of potential problems. This brings to light a previously hidden student group that may benefit from additional support to prevent unnecessary underachievement or dropout.

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.

This paper will examine the role academic colleagues can play in the Widening Participation process (predominately outreach), by providing examples of innovative practice from the University of East Anglia. At the University of East Anglia a unique academic post is held in each Faculty to ensure that Widening Participation students are supported throughout their time at university and that subject-specific outreach sessions can utilise world-leading research to raise aspirations. This paper will highlight examples from Modern Foreign Languages and Health Sciences to demonstrate how academics can help raise aspirations and support long-term intervention projects. Norfolk is both a rural and costal county and faces difficult challenges in raising aspirations as some areas of the county have a progression rate to HE as low as 8%. Thus, by creating a dynamic range of activities in collaboration with academic colleagues, a breadth of targeted activities can be delivered, activities that also benefit current UG students, academics, and support the reputation of the university itself

Problem-based learning (PBL) and early clinical placements (CP) are recognised as complementary strategies for developing clinical reasoning (CR) in medical education. However, how alternating between these formats influences the CR process from students’ perspectives remains underexplored. This qualitative-led exploratory mixed-methods study examined how curriculum sequencing shapes Graduate Entry Medical students’ perceptions of their CR process. Fourteen Year-2 students participated across two pre-existing streams: one began with PBL and the other with CP before switching. Across these alternating phases, students completed the Self-Assessment of Clinical Reflection and Reasoning (SACRR), applied reasoning through vignette-based single-best-answer (SBA) questions to prompt reflection on their reasoning processes. Students’ reflections were further explored through in-depth semi-structured interviews. Thematic analysis formed the primary interpretive strand, supported by descriptive quantitative data. Interview findings revealed that alternating PBL and CP encouraged students to reflect on, apply, and progressively refine their reasoning skills. Students valued the complementary relationship between classroom discussion and authentic clinical exposure, citing case-based dialogue, GP teaching, and supportive environments as key enablers, while heavy workloads, examination pressures, and over-guided PBL sessions were perceived as barriers to CR process. Overall, CR development emerged as a gradual, experiential process enhanced by the dynamic interplay of PBL and clinical learning. These findings underscore the importance of integrating structured discussion with authentic patient encounters rather than privileging one learning format or sequence over the other.