Types Of Questions Research Articles

Automatic distractor generation in multiple-choice questions: a systematic literature review

Background Multiple-choice questions (MCQs) are one of the most used assessment formats. However, creating MCQs is a challenging task, particularly when formulating the distractor. Numerous studies have proposed automatic distractor generation. However, there has been no literature review to summarize and present the current state of research in this field. This study aims to perform a systematic literature review to identify trends and the state of the art of automatic distractor generation studies. Methodology We conducted a systematic literature following the Kitchenham framework. The relevant literature was retrieved from the ACM Digital Library, IEEE Xplore, Science Direct, and Scopus databases. Results A total of 60 relevant studies from 2009 to 2024 were identified and extracted to answer three research questions regarding the data sources, methods, types of questions, evaluation, languages, and domains used in the automatic distractor generation research. The results of the study indicated that automatic distractor generation has been growing with improvement and expansion in many aspects. Furthermore, trends and the state of the art in this topic were observed. Conclusions Nevertheless, we identified potential research gaps, including the need to explore further data sources, methods, languages, and domains. This study can serve as a reference for future studies proposing research within the field of automatic distractor generation.

Homebuilt Imaging-Based Spatial Transcriptomics: Tertiary Lymphoid Structures as a Case Example.

Spatial transcriptomics methods provide insight into the cellular heterogeneity and spatial architecture of complex, multicellular systems. Combining molecular and spatial information provides important clues to study tissue architecture in development and disease. Here, we present a comprehensive do-it-yourself (DIY) guide to perform such experiments at reduced costs leveraging open-source approaches. This guide spans the entire life cycle of a project, from its initial definition to experimental choices, wet lab approaches, instrumentation, and analysis. As a concrete example, we focus on tertiary lymphoid structures (TLS), which we use to develop typical questions that can be addressed by these approaches.

Filtered products of injective objects in Grothendieck categories

The transfer of properties from a family of modules to their direct product or to their direct sum has always been a topical subject of great interest. In this paper we address this type of questions in the general framework of (locally finitely generated) Grothendieck categories and for more general constructions that leave as particular cases direct sums or products: F -products, F being a filter on the chosen index set. Thus, we study the conditions on the categories for the F -products of any family of (M-)injective objects, or else of any family of copies of a single injective object, to be injective.

Generative AI

Generative AI is developing at an exponential pace, showing immense potential for reshaping teaching and learning methodologies. Designing high-quality examinations demands careful consideration, such as incorporating diverse question types to assess different levels of understanding and ensuring alignment with learning outcomes. However, many university educators encounter challenges in designing effective examinations due to insufficient training. This paper outlines our work-in-progress in leveraging generative AI, specifically large language models, as a copilot in examination design. In this study, we are evaluating large language models’ performance in assessing examination questions based on four key quality criteria. Preliminary findings indicate that ChatGPT 3.5 achieved comparable performance to the state-of-the-art in classifying questions by cognitive complexity, as defined by Bloom’s taxonomy. Additionally, ChatGPT 3.5 demonstrated promising initial results across other criteria. In conclusion, generative AI shows substantial potential to serve as a valuable tool in assisting university educators in enhancing the overall quality of examination design.

Trainee pharmacists’ evaluation of the formal learning elements within the N. Ireland foundation training year

Abstract Introduction Foundation Training Year (FTY) is a 12-month programme which prepares trainee pharmacists (TPs) to develop the skillset of a registered pharmacist and aids preparation for the Common Registration Assessment (CRA) via both work-based learning and formal learning. The formal learning included within the NI FTY programme consists of development day sessions with associated pre-requisite materials, including exam-style questions. The TPs attend 13 development days which are focused on high and medium weighted topics within the Registration Assessment Framework1 . The sessions take place online and the format is that of case-based discussion and exam-style questions, as well as two mock exams. Aim The aim of this study was to analyse TPs’ evaluation of how the formal learning elements prepared them for the CRA. Methods TPs (n=128) within FTY 2022/2023 completed an end-of-year evaluation relating to the whole programme, including the formal learning elements. 128 valid responses were received. The evaluation took the form of an online questionnaire with various question types, from Likert scale style(1-9) to free-text responses and was administered via an online link emailed to TPs. No ethical approval was required as the evaluation was not a component of a research project. Results In relation to the formal learning elements of the programme, 61% of trainee pharmacists rated the pre-requisite materials for the development day sessions at 6 and above whilst 57% of respondents rated the development day sessions at this level. An increased number of respondents (86%) rated the practice exam-style questions accessed via Moodle at 6 and above with the vast majority (99%) strongly agreeing/agreeing that regular exposure to exam style questions helped them to learn on an ongoing basis, particularly for high and medium weighted registration assessment topics. TPs valued the opportunity to attempt mock exams, with 99% strongly agreeing/agreeing that the mocks prepared them to sit the CRA and helped them to identify developmental needs. Discussion / conclusion The majority of TPs valued the formal learning elements of the FTY and felt the sessions and mock exams prepared them to sit the CRA. TP experience of exposure to exam questions was overwhelmingly positive with TPs acknowledging the importance of regular exposure to this element which allows them to develop their knowledge and become proficient in answering the types of questions asked in the CRA. This provides support for the inclusion of practice exam-style questions on a routine basis in the formal learning programme to support the FTY. Limitations to the study are that TPs completed the survey prior to obtaining results for the CRA, which may have influenced their responses to the questions posed, however the N. Ireland results were higher than the national average, with 95.6% first time sitters passing the exam in June 2023. Given this data, we changed formal learning to focus primarily on high and medium-weighted topics of the Registration Assessment Framework1, delivering interactive webinars where TPs answer exam-style questions with additional supplemental learning provided, where needed, by the expert tutor. Reference 1. General Pharmaceutical Council. Registration assessment framework for sittings in 2024. 2024. https://assets.pharmacyregulation.org/files/2024-04/registration-assessment-framework-for-sittings-in-2024.pdf [accessed 3rd June 2024]

Teachers’ use of questions and the science/religion encounter: Basil Bernstein and the impossibility of the unthinkable

AbstractThis article examines the teaching of creation in a year six and a year three Religious Education (RE) class in schools in the south of England with a focus on the type and role of teacher questioning in relation to classroom discussions. The nature of knowledge, curriculum content and the relationship between RE and other subjects is currently under scrutiny and there is an ongoing debate about the negative effects of presenting science and religion as epistemically siloed. This paper presents an analysis of the way two teachers in two primary schools used questions to frame the relationship between science and religion where the aim was to minimise the polarisation of religion and science. Using Bernstein’s idea of the pedagogic device and the related notions of frame, classification and the unthinkable the analysis suggests that teachers employed both visible and invisible pedagogies that limited the diversity of ideas that were considered legitimate in discussions, and which therefore influenced the nature of pupil questions and responses. We suggest that despite attempts by both teachers to diminish the strong boundaries between different types of knowledge their use of questions serves to reinforce them.

The Effect of Job Interview Question Type on Faking and Interviewee Reactions

The Effect of Job Interview Question Type on Faking and Interviewee Reactions

What’s Your Problem? Writing Effective Research Questions for Quality Publications

Research questions play a central role in papers, indicating the phenomenon or problem that is being addressed, and clearly stating what is being investigated. Indeed, the presence of research questions within a research article is an established quality indicator, and research questions form the basis of determining hypotheses, methodology, methods, and how interpretations and analysis of outcomes are undertaken. Given the key purpose of a research article is to communicate the phenomenon or problem that is being addressed, research questions play a central role in clearly stating what is being investigated. The Journal of University Teaching and Learning Practice (JUTLP) seeks to publish high quality research papers which offer impactful guidance for teaching and learning practice in higher education. In this editorial, we explore what research questions are, how they are used, their key characteristics, different types of questions, and how to write them. We also make practical recommendations for authors, reviewers, and editors in how to use research questions, what to look out for when reviewing manuscripts and making decisions, and how to best support authors in producing quality publications by ensuring impactful research questions are included.

Enhancing Assessment Practices with Moodle: A Comprehensive Study on Teachers’ Use of Different Question Types and the Impact of FastTest PlugIn

This study investigates the evolution of online assessment practices through Moodle, a leading Learning Management System (LMS). It explores Moodle’s array of question types alongside the transformative impact of FastTest PlugIn (FTP) on educators’ assessment methods during and after the COVID-19 pandemic. This research examines the efficacy of various question types and import formats for large-scale question elaboration. Surveys have been conducted before and after developing courses to train educators on FTP. By analysing trends before and after adopting FTP, the study aims to gain insight into how educators from different universities and disciplines have adapted their assessment strategies among technological advancements. The introduction of FTP has significantly simplified question creation and integration within Moodle. The study also highlights differences in assessment practices between STEM and non-STEM disciplines. Surveys indicate that, following the FTP training course, the use of all question types has increased: Matching (35%), Missing Word (39%), and Cloze (22%). This work provides a comprehensive overview of current LMS trends and Moodle’s evolving role in educational technology. It emphasises the impact of technological advancements, such as FTP, on assessment practices and aims to offer actionable insights for enhancing online assessments.

Evidence-Based Potential of Generative Artificial Intelligence Large Language Models on Dental Avulsion: ChatGPT Versus Gemini.

In this study, the accuracy and comprehensiveness of the answers given to questions about dental avulsion by two artificial intelligence-based language models, ChatGPT and Gemini, were comparatively evaluated. Based on the guidelines of the International Society of Dental Traumatology, a total of 33 questions were prepared, including multiple-choice questions, binary questions, and open-ended questions as technical questions and patient questions about dental avulsion. They were directed to ChatGPT and Gemini. Responses were recorded and scored by four pediatric dentists. Statistical analyses, including ICC analysis, were performed to determine the agreement and accuracy of the responses. The significance level was set as p < 0.050. The mean score of the Gemini model was statistically significantly higher than the ChatGPT (p = 0.001). ChatGPT gave more correct answers to open-ended questions and T/F questions on dental avulsion; it showed the lowest accuracy in the MCQ section. There was no significant difference between the responses of the Gemini model to different types of questions on dental avulsion and the median scores (p = 0.088). ChatGPT and Gemini were analyzed with the Mann-Whitney U test without making a distinction between question types, and Gemini answers were found to be statistically significantly more accurate (p = 0.004). The Gemini and ChatGPT language models based on the IADT guideline for dental avulsion undoubtedly show promise. To guarantee the successful incorporation of LLMs into practice, it is imperative to conduct additional research, clinical validation, and improvements to the models.

Dynamic multi teacher knowledge distillation for semantic parsing in KBQA

Knowledge base question answering (KBQA) is an important task of extracting answers from a knowledge base by analyzing natural language questions. Semantic parsing methods convert natural language questions into executable logical forms to obtain answers on the knowledge base. Conventional approaches often prioritize singular logical forms, overlooking the distinct strengths inherent in various logical frameworks for problem solving. Recognizing that different logical forms may excel in addressing specific types of questions, our aim is to harness these strengths. By integrating the strengths of different logical forms, we expect to achieve more comprehensive and effective semantic parsing solutions. In our paper, we propose a Dynamic Multi Teacher Knowledge Distillation for Semantic Parsing (DMTKD-SP). DMTKD-SP leverages a collection of teacher models, each mastering a unique logical form, to collaboratively guide a student model so that knowledge from different logical forms can be transferred into the student model. To achieve this, we employ a confidence-based weight assignment module to dynamically assign weights for each teacher model. Furthermore, we introduce a self-distillation mechanism to mitigate the confusion caused by simultaneous learning from multiple teachers. We evaluate DMTKD-SP across variants of the KQA Pro dataset, demonstrating an accuracy improvement of 0.35% on five types of questions, with a notable 0.75% improvement for Count questions.

Commercial forensic interviewing in the South African context: the Ponaletso CFI framework

Purpose The continued prevalence of financial crime and the lack of effective commercial forensic interviewing strategies prompted the development, presented here, of a conceptual framework to explain the relational dynamics conducive to interactions during a commercial forensic interview (CFI). The purpose of this study is to present such a framework which may facilitate the process of obtaining admissible evidence in different forums. Design/methodology/approach A grounded theory approach was adopted to develop a conceptual framework, drawing on the findings of a scoping review, direct observations of interactions involving commercial forensic practitioners (FPs) and interviewees, and data obtained from semi-structured interviews, making use of critical realism as a meta-theoretical framework. Findings Data obtained from the review informed the integrated approach of the conceptual framework, thus combining the structure of the PEACE model and creating an optimal interpersonal context informed by the person-centred approach (PCA). Observational data were used to identify the relational qualities associated with the interpersonal styles (clusters of relational qualities) of FPs in the CFI context, thereby contributing to complex relational dynamics. Interview data were used both to describe effective and ineffective interpersonal styles and how they played out in the CFI with interviewees, yielding information that unfortunately proved not to be admissible in a legal context. The findings of the different phases indicated the need for an integrated conceptual approach, the Ponaletso CFI framework, which draws on the structure of the PEACE model and the PCA, as expressed in the relational qualities of interactional pattern analysis (IPA). The framework consists of the interview stage (before, during and after), the structure (phases) of the PEACE model, the type of questions (open- or closed-ended) according to the PCA, FPs’ interpersonal styles (clusters of relational qualities as explained by IPA) and the observable relational dynamics between FPs and interviewees. When FPs use the Ponaletso CFI framework, there is a greater likelihood of extracting admissible evidence because the interview is guided by a clear structure with a step-by-step questioning approach by FPs, while taking into consideration the relational dynamics to which they contribute. Originality/value The proposed conceptual framework considers relational dynamics and how FPs can navigate towards optimal relational dynamics in CFI contexts. FPs can apply the structure, ask appropriate questions at the relevant phases and observe the relational dynamics playing out in the interpersonal context of the CFI. To the best of the authors’ knowledge, this is the first attempt at creating an integrated interviewing framework for conducting CFIs aligned with the legal context in South Africa. Using the Ponaletso CFI framework may aid FPs in obtaining admissible evidence and facilitate an optimal interpersonal context during commercial forensic investigations while respecting interviewees’ constitutional rights.

Playing with words: Comparing the vocabulary and lexical diversity of ChatGPT and humans

The introduction of Artificial Intelligence (AI) generative language models such as GPT (Generative Pre-trained Transformer) and conversational tools such as ChatGPT has triggered a revolution that can transform how text is generated. This has many implications, for example, as AI-generated text becomes a significant fraction of the text, would this affect the language capabilities of readers and also the training of newer AI tools? Would it affect the evolution of languages? Focusing on one specific aspect of the language: words; will the use of tools such as ChatGPT increase or reduce the vocabulary used or the lexical diversity? This has implications for words, as those not included in AI-generated content will tend to be less and less popular and may eventually be lost. In this work, we perform an initial comparison of the vocabulary and lexical diversity of ChatGPT and humans when performing the same tasks. In more detail, two datasets containing the answers to different types of questions answered by ChatGPT and humans, and a third dataset in which ChatGPT paraphrases sentences and questions are used. The analysis shows that ChatGPT-3.5 tends to use fewer distinct words and lower diversity than humans while ChatGPT-4 has a similar lexical diversity as humans and in some cases even larger. These results are very preliminary and additional datasets and ChatGPT configurations have to be evaluated to extract more general conclusions. Therefore, further research is needed to understand how the use of ChatGPT and more broadly generative AI tools will affect the vocabulary and lexical diversity in different types of text and languages.

Mesopelagic Fish Traits: Functions and Trade‐Offs

ABSTRACTFishes inhabiting the mesopelagic zone of the world's oceans are estimated to account for the majority of the world's fish biomass. They have recently attracted new attention because they are part of the biological carbon pump and have been reconsidered as a contribution to food security. Hence, there is an urgent need to understand how environmental conditions and species interactions shape their assemblages, and how they contribute to the functioning of marine ecosystems. Trait‐based approaches are valuable for addressing these types of questions. However, the biology and ecology of mesopelagic fishes are understudied compared to fishes in shallow and epipelagic waters. Here, we synthesise existing knowledge of traits of mesopelagic fishes and relate them to their role in survival, feeding and growth and reproduction, the key functions that contribute to fitness. Vertical migrations, specialised vision and the use of bioluminescence are among the most striking adaptations to the conditions in the mesopelagic realm. Many traits are interrelated as a result of trade‐offs, which may help to understand selection pressures. While morphological traits are straightforward to observe, major knowledge gaps exist for traits that require frequent sampling, assessment under experimental conditions or age determination. The unique adaptations of mesopelagic fishes need to be included in management strategies as well as fundamental research of the habitat.

국어과 중등교사 임용시험(2002-2024) ‘고전 시가’ 영역의 분석 및 방향

Objectives The purpose of this study was to analyze the questions in the ‘classical poetry’ area in the Korean Language Department Secondary Teacher Appointment Test (2002-2024), and to suggest what direction to proceed in the future. Methods Questions in the classical poetry area were extracted from the Korean language department secondary teacher appointment test questionnaire and analyzed ‘tendency’ and ‘type of question’ based on the direction and principles of questions provided by the Korea Institute for Curriculum and Evaluation. Results The tendency of the questions could be divided into three main categories. First, there was a great tendency to consist of 2-3 passages only within the classical poem. Second, there was a bias in the classical poem section that is presented every year. Third, there were many cases where the works that were presented reappeared in different forms with similar content. The types of questions were analyzed by dividing them by period. First, in the 2000s, there was a great tendency to write questions by memorizing knowledge or content about the work. Second, questions before and after 2010 were presented focusing on knowledge of literary history and understanding and analysis of works. Third, the written questions from 2014 to the present are presented to allow objective evaluation as much as possible, and the narrative questions are presented in the direction of analysis, reasoning, synthesis, and evaluation based on data. Conclusions Based on the analysis results, four directions for the future classical poetry appointment test are proposed. First, it is necessary to expand the scope of complex passages in classical poetry. Second, reduce the deviation of the frequently asked branches, and evenly present the branches of oral literature, Korean literature, and Chinese literature. Third, develop questions to measure more diverse and higher-order thinking. Fourth, the questions are presented so that the essence of the classic poem, “a poem” and “a song (歌)” can be confirmed or realized.

Exploration Into The Ability of Fifth-Grade Students To Solve Decimal Problems Using Proportional Reasoning

Proportional Reasoning is a complex mathematical way of thinking about interconnected ideas such as multiplication and division of whole numbers, fractions, ratios, powers, measurements, and percent. This research aims to identify students' thinking processes in solving decimal problems that require proportional reasoning. This research was conducted on 5th grade elementary school students in Klaten, Central Java, Indonesia, using test instruments, interviews and documentation studies. The test given consists of five types of questions, namely tests on questions that require students to use additive, multiplicative thinking skills, use reasonable or appropriate ratios, build unit structures (groupings), and understand invariants. The research results found that the proportional reasoning indicator that was more dominantly used by students was the ability to think multiplicatively, while the sub-indicators were writing and completing calculations but the final results were not correct. Apart from that, other findings explain that the low ability is due to the prerequisite material not being mastered, students not understanding the questions, and students not understanding the concept of the topic. Thus, these results confirm that proportional reasoning abilities in grade 5 elementary school are still quite low.

Informatics assessment of COVID-19 data collection: an analysis of UK Biobank questionnaire data

BackgroundThere have been many efforts to expand existing data collection initiatives to include COVID-19 related data. One program that expanded is UK Biobank, a large-scale research and biomedical data collection resource that added several COVID-19 related data fields including questionnaires (exposures and symptoms), viral testing, and serological data. This study aimed to analyze this COVID-19 data to understand how COVID-19 data was collected and how it can be used to attribute COVID-19 and analyze differences in cohorts and time periods.MethodsA cohort of COVID-19 infected individuals was defined from the UK Biobank population using viral testing, diagnosis, and self-reported data. Changes over time, from March 2020 to October 2021, in total case counts and changes in case counts by identification source (diagnosis from EHR, measurement from viral testing and self-reported from questionnaire) were also analyzed. For the questionnaires, an analysis of the structure and dynamics of the questionnaires was done which included the amount and type of questions asked, how often and how many individuals answered the questions and what responses were given. In addition, the amount of individuals who provided responses regarding different time segments covered by the questionnaire was calculated along with how often responses changed. The analysis included changes in population level responses over time. The analyses were repeated for COVID and non-COVID individuals and compared responses.ResultsThere were 62 042 distinct participants who had COVID-19, with 49 120 identified through diagnosis, 30 553 identified through viral testing and 934 identified through self-reporting, with many identified in multiple methods. This included vast changes in overall cases and distribution of case data source over time. 6 899 of 9 952 participants completing the exposure questionnaire responded regarding every time period covered by the questionnaire including large changes in response over time. The most common change came for employment situation, which was changed by 74.78% of individuals from the first to last time of asking. On a population level, there were changes as face mask usage increased each successive time period. There were decreases in nearly every COVID-19 symptom from the first to the second questionnaire. When comparing COVID to non-COVID participants, COVID participants were more commonly keyworkers (COVID: 33.76%, non-COVID: 15.00%) and more often lived with young people attending school (61.70%, 45.32%).ConclusionTo develop a robust cohort of COVID-19 participants from the UK Biobank population, multiple types of data were needed. The differences based on time and exposures show the important of comprehensive data capture and the utility of COVID-19 related questionnaire data.

Maternal Question Use Relates to Syntactic Skills in 5- to 7-Year-Old Children.

This study examined how mothers' question-asking behavior relates to their child's syntactic skills. One important aspect of maternal question-asking behavior is the use of complex questions when speaking with children. These questions can differ based on both their purpose and structure. The purpose may be to seek out information, to teach, or to get a simple yes/no response. Questions may even be rhetorical, with no answer intended at all. Structurally, questions can include a wh-word (who, what, when, where, why, and how) or not; however, these wh-questions are important because they elicit utterances from the child and support vocabulary development. Despite wh-questions eliciting a response from children, it remains unknown how these questions relate to children's syntactic skills. Thirty-four mother-child dyads participated in a 15-min seminaturalistic play session. Children were between the ages of 5 and 7 years (M = 6.26 years, SD = 1.04 years; 20 girls/14 boys). The Diagnostic Evaluation of Language Variation (DELV) assessment was used to measure syntactic skills in children. Using the Systematic Analysis of Language Transcripts, questions were categorized based on structure (wh-questions vs. non-wh-questions) and purpose (information-seeking, pedagogical, or yes/no and rhetorical questions). A repeated-measures analysis of covariance and a linear regression model were implemented to address the frequency of different questions asked by mothers, as well as what types of questions are most related to children's concurrent syntactic skills. When controlling for total maternal utterances, results revealed that non-wh-questions and rhetorical/yes and no questions were the most frequent types of questions produced by mothers, in terms of structure and purpose, respectively. However, wh-questions were predominantly information-seeking questions. This is important, as the use of information-seeking wh-questions was positively associated with children's syntactic skills, as measured by the DELV, and resulted in children producing longer utterances in response to these questions, as determined by child mean length of utterance in words. Taken together, these findings suggest maternal use of wh-questions aids syntactic skills in children ages 5-7 years, likely because they require a more syntactically complex response on the child's behalf. https://doi.org/10.23641/asha.27276891.

Shifting the spotlight: What do we mean by ‘religious language’?

Abstract There are mainly two types of questions asked about religious language: those about identity (e.g., what is a religious language?) and those about meaning (e.g., what do its sentences say?). Most philosophers focus on the latter because while they disagree about meaning, they agree that some sentences are religious and that our understanding of them does not depend on us knowing what makes them religious. In this article, I provide two reasons why questions about identity should receive more attention. First, theories of identity and theories of meaning share a two-way relationship where the characteristics of one influence those of the other, and so overlooking identity overlooks important characteristics of meaning. Second, the study of religious language has been shaped by this relationship for some time and being aware of it improves our understanding of conventional trends and contemporary debates. If successful, this article will motivate philosophers to reconsider the role of identity in research and to dedicate more effort to its study.

A guideline-informed language model for paediatric cardiology demonstrates high performance in answering complex medical questions

Abstract Background Paediatric cardiology presents unique challenges with its diverse and complex cases, limited evidence base, and the necessity for multi-expert involvement in decision-making processes. In this context, the introduction of generative pre-trained transformer (GPT) based large language models (LLMs) offers a potential avenue for the provision of complex information and clinical decision support. Purpose This study evaluates the quality of three different GPT LLMs in answering complex medical questions, including a state-of-the-art preview model that incorporates the German paediatric cardiology guidelines. Methods Seven paediatric cardiologists and paediatric cardiac surgeons generated 72 questions, including complex questions and medical cases with associated questions. The questions were categorized by difficulty and required knowledge (factual and experience-based or mostly experience-based). We prompted the questions to three LLMs: GPT 3.5, GPT 4 and a GPT 4 turbo preview. The GPT 4 turbo preview was customized by incorporating all guidelines from the German Society for Paediatric Cardiology by a retrieval function. Employing one complex instruction for all questions, we prompted the LLMs to provide precise and detailed expert-level responses. The responses from each model were evaluated by experts based on relevance, factual accuracy, severity of possible harm, completeness, superfluous content, and age-related appropriateness from 0 (very bad) to 7 (very good). Differences were calculated using the Kruskal-Wallis-test in SPSS Version 28. Results Our findings indicated a good performance of all models regarding the dimensions tested. The figures show the average ratings (Figure 1, Figure 2A) and highlight significant differences after Bonferroni correction in bold (Figure 2B). The GPT 4 turbo preview, including the retrieval of guidelines, provided significantly more relevant (average rating [AR] 5.94, meaning mostly relevant), accurate (AR 5.6, meaning between somewhat and mostly accurate) and complete (AR 5, meaning fairly complete) answers compared to GPT 3.5 and GPT 4. In terms of difficulty levels or the type of questions, there was no significant difference in rating. Relevance ratings were slightly better in factual questions (AR 5.7) than in those requiring more experience-based knowledge (AR 5,3). Although GPT4 had higher average scores compared to GPT 3.5 in all dimensions except superfluous content, the differences in rating were not statistically significant. All models had relevant difficulties considering the age-related aspects of the questions (AR 4.06-4.45, p=0.455). Conclusion This study highlights the potential and limitations of AI language models in addressing complex medical questions in fields characterized by highly individualized decision-making scenarios. The findings advocate for the development of more specialized AI tools in medicine, tailored to specific medical fields and patient age groups.Fig 1:Average ratings of LLMsFig 2:Rating differences between LLMs