High-level Questions Research Articles

Exploring annotation taxonomy in grouped bar charts: A qualitative classroom study

Annotations are an essential part of data analysis and communication in visualizations, which focus a readers attention on critical visual elements (e.g. an arrow that emphasizes a downward trend in a bar chart). Annotations enhance comprehension, mental organization, memorability, user engagement, and interaction and are crucial for data externalization and exploration, collaborative data analysis, and narrative storytelling in visualizations. However, we have identified a general lack of understanding of how people annotate visualizations to support effective communication. In this study, we evaluate how visualization students annotate grouped bar charts when answering high-level questions about the data. The resulting annotations were qualitatively coded to generate a taxonomy of how they leverage different visual elements to communicate critical information. We found that the annotations used significantly varied by the task they were supporting and that whereas several annotation types supported many tasks, others were usable only in special cases. We also found that some tasks were so challenging that ensembles of annotations were necessary to support the tasks sufficiently. The resulting taxonomy of approaches provides a foundation for understanding the usage of annotations in broader contexts to help visualizations achieve their desired message.

The Environmental Enteric Dysfunction Biopsy Initiative (EEDBI) Consortium: mucosal investigations of environmental enteric dysfunction

Environmental enteric dysfunction (EED) is an asymptomatic acquired disorder characterized by upper small bowel inflammation, villus blunting, and gut permeability. It is a major contributor to poor growth in childhood as well as other highly consequential outcomes such as delayed neuorcognitive development. After decades of intermittent interest in this entity, we are now seeing a resurgence in the field of EED. However, recent studies have been hampered by a lack of investigation of the target tissue—the upper small bowel. In 2016, the EEDBI (Environmental Enteric Dysfunction Biopsy Initiative) Consortium was established as a common scientific platform across 3 independent EED biopsy cohort studies in Bangladesh, Pakistan, and Zambia. Two centers in the United States recruited comparison groups of children undergoing endoscopy for clinical indications. The EEDBI Consortium goal was to augment the contributions of the individual centers and answer high-level questions amenable to analysis and interpretation across the studies. Here, we describe the Consortium and its cohorts and recruitment procedures across studies. We also offer details applicable to all papers in this supplement, which describe EED mucosal histology, morphometry, immunohistochemistry, and transcriptomics as well as histology relationship to pathogens and biomarkers.

Ways of questioning, ways of gesturing: how teachers elicit different ways of students’ thinking in elementary science classrooms

ABSTRACT Questions and gestures used in pedagogical contexts have each received considerable attention. However, rarely have studies focused on the relationship between questions and gestures and what this relationship may mean for pedagogical practice and the development of higher-level thinking. Drawing on previous research into the cognitive levels of questions and gestures, this study addresses this issue through focusing on the type of gestures used when teachers ask questions in elementary science classrooms. Participants were two Grade One science teachers, and video recordings constitute the major data sources. The study is primarily qualitative, with frequency analyses conducted to analyse the cognitive levels of questions and the cognitive distribution of gestures in the questions. The results show that elementary science classrooms are dominated by low cognitive level questions, which reflects a direct instruction teaching method. However, the use of higher cognitive level questions suggests that teachers use some learner-centred approaches to challenge and encourage students for deeper levels of thinking. Overall, there is a correspondence between the cognitive levels of questions and of gestures, e.g. LCL gestures are most commonly distributed in LCL questions, although variations in the specific use of gestures in each type of question exist. This research contributes to the understanding of the interplay between questions and gestures within the elementary science teaching-learning context, and has implications for science teachers and teacher educators to consider the relationship between cognitive levels of gestures and questions and its role in developing higher-level thinking in elementary science pedagogy.

Student-generated questions: An exploration of an instructional strategy with young children

Student-generated questions enhance deeper learning and empower active participation and critical thinking. Researchers have extensively explored teacher questions, but a notable gap persists in how to teach young children to ask questions effectively. This mixed-methods pilot study examined the impact of teaching questioning skills using the Question Formulation Technique (QFT) in an early childhood summer program for children from under-resourced areas. Pre-post oral language assessment scores from 33 children were analyzed to determine the impact of the QFT. Results reveal that using this method, teachers can effectively guide young students to ask more and better questions. Students involved in the research exhibited a substantial increase in the number of questions posed. They demonstrated a qualitative improvement in their approach to questioning, significantly increasing the number of wonderment questions (high-level questions). Conclusions indicate that using the QFT in early childhood classrooms can encourage young children to generate questions. Based on these findings, the authors conclude that teaching learners to ask meaningful questions can enhance their engagement, comprehension, and overall academic performance in diverse populations.

Measuring vaccination coverage and concerns of vaccine holdouts from web search logs

To design effective vaccine policies, policymakers need detailed data about who has been vaccinated, who is holding out, and why. However, existing data in the US are insufficient: reported vaccination rates are often delayed or not granular enough, and surveys of vaccine hesitancy are limited by high-level questions and self-report biases. Here we show how search engine logs and machine learning can help to fill these gaps, using anonymized Bing data from February to August 2021. First, we develop a vaccine intent classifier that accurately detects when a user is seeking the COVID-19 vaccine on Bing. Our classifier demonstrates strong agreement with CDC vaccination rates, while preceding CDC reporting by 1–2 weeks, and estimates more granular ZIP-level rates, revealing local heterogeneity in vaccine seeking. To study vaccine hesitancy, we use our classifier to identify two groups, vaccine early adopters and vaccine holdouts. We find that holdouts, compared to early adopters matched on covariates, are 67% likelier to click on untrusted news sites, and are much more concerned about vaccine requirements, development, and vaccine myths. Even within holdouts, clusters emerge with different concerns and openness to the vaccine. Finally, we explore the temporal dynamics of vaccine concerns and vaccine seeking, and find that key indicators predict when individuals convert from holding out to seeking the vaccine.

Performance of ChatGPT on American Board of Surgery In-Training Examination Preparation Questions

IntroductionChat Generative Pretrained Transformer (ChatGPT) is a large language model capable of generating human-like text. This study sought to evaluate ChatGPT’s performance on Surgical Council on Resident Education (SCORE) self-assessment questions. MethodsGeneral surgery multiple choice questions were randomly selected from the SCORE question bank. ChatGPT (GPT-3.5, April-May 2023) evaluated questions and responses were recorded. ResultsChatGPT correctly answered 123 of 200 questions (62%). ChatGPT scored lowest on biliary (2/8 questions correct, 25%), surgical critical care (3/10, 30%), general abdomen (1/3, 33%), and pancreas (1/3, 33%) topics. ChatGPT scored higher on biostatistics (4/4 correct, 100%), fluid/electrolytes/acid-base (4/4, 100%), and small intestine (8/9, 89%) questions. ChatGPT answered questions with thorough and structured support for its answers. It scored 56% on ethics questions and provided coherent explanations regarding end-of-life discussions, communication with coworkers and patients, and informed consent. For many questions answered incorrectly, ChatGPT provided cogent, yet factually incorrect descriptions, including anatomy and steps of operations. In two instances, it gave a correct explanation but chose the wrong answer. It did not answer two questions, stating it needed additional information to determine the next best step in treatment. ConclusionsChatGPT answered 62% of SCORE questions correctly. It performed better at questions requiring standard recall but struggled with higher-level questions that required complex clinical decision making, despite providing detailed responses behind its rationale. Due to its mediocre performance on this question set and sometimes confidently-worded, yet factually inaccurate responses, caution should be used when interpreting ChatGPT’s answers to general surgery questions.

Review of Primary and Secondary Education Institutions Scholarship Exam Science Questions According to the Renewed Bloom Taxonomy

In this research, secondary school science questions in the Primary and Secondary Education Institutions Scholarship Examination (IOKBS) were examined. IOKBS science questions from 2018 to 2023 (6 years) constitute the sample of the research. While examining the questions, evaluations were made according to the renewed Bloom taxonomy (RBT) criteria. Qualitative research, which is based on understanding and questioning an event in depth, constitutes the research method of the study. Document analysis, which is a qualitative research method and allows access to written sources without wasting time, was used to collect data. In total, 600 science questions in IOKBS were analyzed. The questions were categorized according to knowledge and cognitive process dimensions of RBT, and their frequencies were calculated. When 600 IOKBS questions were evaluated according to the knowledge dimension of RBT, it was determined that most questions were conceptual (71 questions) in fifth grade, factual (73 questions) in sixth grade, conceptual (65 questions) in seventh grade and conceptual (58 questions) in eighth grade. No question containing metacognitive information could be identified. It has been observed that the accumulation is greater in factual and conceptual knowledge. When the questions were evaluated according to cognitive level, it was determined that the most questions in the fifth grade were from the comprehension level (54 questions), in the sixth grades from the remembering level (68 questions), in the seventh grades from the understanding (55 questions) and remembering levels (55 questions), and in the eighth grades from the comprehension level (54 questions). It has been determined that there is no question in the creation step. It was observed that the number of tests evaluated in the exam measured lower-level questions was higher than the number of measurements in higher-level questions. The study concluded that the science questions in IOKBS consist of a structure that does not prioritize creativity and supports a rote-based thinking style. Science test questions in IOKBS may focus on cognitive domain steps that will reveal higher-level thinking.

What Large Language Models Know

Abstract The strengths and weaknesses of generative applications built on large language models are by now well-known. They excel at the production of discourse in a variety of genres and styles, from poetry to programs, as well as the combination of these into novel forms. They perform well at high-level question answering, dialogue, and reasoning tasks, suggesting the possession of general intelligence. However, they frequently produce formally correct but factually or logically wrong statements. This essay argues that such failures—so-called hallucinations—are not accidental glitches but are instead a by-product of the design of the transformer architecture on which large language models are built, given its foundation on the distributional hypothesis, a nonreferential approach to meaning. Even when outputs are correct, they do not meet the basic epistemic criterion of justified true belief, suggesting the need to revisit the long neglected relationship between language and reference.

Dwelling with sustainable pedagogy aboard Amtrak: pathways for transdisciplinary and affective general education

ABSTRACT Intersecting pressures within higher education ask geographers to be ever-more applied, global, community-engaged, field-based, and sustainable. Particularly within the liberal arts education systems, we navigate these pressures for both students seeking geography degrees and those completing their general education. While aspirational if often economically driven, these goals are in some ways contradictory. How can a course be both field and community based? How can a program deliver global and sustainable experiences? Where is the balance between applied practice and critical theory? Focusing beyond these high-level questions, while nevertheless engaging with them, we question the very possibility of drawing upon the legacy of community-based field research to offer a study away experience to students that not only teaches sustainable tourism, but practices it. Using autoethnographic fieldnotes and student surveys, we ask how field-based programs may show significant curricular application both within and beyond geography. We do so via a case study of our Amtrak Tourism: Trains, Cities, and Sustainability domestic faculty-led study away program which seeks to simultaneously practice and teach multiple forms of place-based sustainable-tourism. Our findings point towards the significance of slow tourism and dwelling as key components necessary in navigating both the pedagogical and touristic paradoxes programs such as our face.

Visual Abstract Reasoning in Computational Imagery

Despite current AI’s human-like behavior, super efficiency, and unbelievable ability to handle complex games, we still complain that it shows no sign of creativity, originality, or novelty outside its training set, and that it fails to develop new insights into old experience or establish understanding of new experience. In short, it generates content from its training set, but does not invent content. A fundamental reason for this is that current AI is incapable of abstraction and reasoning in an abstract, generalizable, and systematic way. Think, for instance, of what AI systems we can build if we have a base system that can answer this simple question—when two things are the same. Instead of studying these high-level questions, I put my thesis in the context of visual abstract reasoning (VAR), a task widely used in human intelligence tests. A classical example of this task is Raven’s Progressive Matrices (RPM, see Figure 1), a family of intelligence tests that was designed to measure eductive ability, i.e., the ability to make meaning out of confusion and generate high-level, usually nonverbal, schemata which make it easy to handle complexity. A similar concept to eductive ability is fluid intelligence, or the ability to discriminate and perceive complex relationships when no recourse to answers is stored in memory. Whether eductive ability or fluid intelligence, RPM points to the qualities that have been lacking in AI. To explore these qualities in AI, I propose the following research questions.

LEARNING STYLES OF A STORY ABOUT SUSTAINABILITY: THEIR EFFECT ON THE LEVEL OF QUESTIONING OF STUDENTS IN PRIMARY EDUCATION

The use of stories in science education can make science more interesting and engaging, illustrate concepts, and provide opportunities for contextual and meaningful learning. Also, integrating a scientific story in which the sustainability goals are incorporated can lead to increasing students’ motivation to learn as well as improve and facilitate the learning process. Having children ask questions allows the educator to learn about the child's insights, views, level of interest, motivation for learning, and knowledge. Therefore, this study focuses on the relationship between science storytelling, questioning, and sustainability among elementary school students. It compares the levels of questioning among primary students who learned a sustainability story in segments versus those who learned the story in its entirety. A quantitative study was conducted involving 120 second graders from the Arab sector in Israel. The results revealed a significant difference in the two groups’ level of questions: the students in the experimental group, who learned the story in segments, asked higher-level questions than those in the control group. The study concluded that learning a scientific story in segments proved effective in enhancing primary students' questioning ability, and that there was a notable preference for this segmented storytelling approach over traditional whole unit learning methods. Keywords: asking questions, science story, storytelling, sustainability education, text learning

Review on Neural Question Generation for Education Purposes

AbstractQuestioning plays a vital role in education, directing knowledge construction and assessing students’ understanding. However, creating high-level questions requires significant creativity and effort. Automatic question generation is expected to facilitate the generation of not only fluent and relevant but also educationally valuable questions. While rule-based methods are intuitive for short inputs, they struggle with longer and more complex inputs. Neural question generation (NQG) has shown better results in this regard. This review summarizes the advancements in NQG between 2016 and early 2022. The focus is on the development of NQG for educational purposes, including challenges and research opportunities. We found that although NQG can generate fluent and relevant factoid-type questions, few studies focus on education. Specifically, there is limited literature using context in the form of multi-paragraphs, which due to the input limitation of the current deep learning techniques, require key content identification. The desirable key content should be important to specific topics or learning objectives and be able to generate certain types of questions. A further research opportunity is controllable NQG systems, which can be customized by taking into account factors like difficulty level, desired answer type, and other individualized needs. Equally important, the results of our review also suggest that it is necessary to create datasets specific to the question generation tasks with annotations that support better learning for neural-based methods.

TRAINING AND ASSISTANCE IN THE DEVELOPMENT OF LEARNING OUTCOME ASSESSMENT MATERIALS HIGH-ORDER THINKING SKILLS FOR HIGH SCHOOL MATHEMATICS TEACHERS IN SOUTH TANGERANG

High Order Thinking Skill (HOTS) is students' ability to process knowledge and ideas in a certain way to give them new knowledge and implications. The general perception regarding HOTS questions is that they are high-level questions. Most teachers adopt olympic questions as HOTS questions due to a misperception that HOTS questions are difficult, even though HOTS questions can be made in various difficulty levels: low, medium, and high. In addition, the substantive aspect of Mathematics, especially the application of Mathematics in life, is also one of the problems in developing HOTS questions. The method of implementing community service (PkM) is by training and assisting in preparing SMA-level Mathematics HOTS questions. The results of this PkM activity are: (1) Increased knowledge and understanding of teachers in the technique of developing HOTS questions which can be seen in an increase in the average pre-test and post-test results by 9%, (2) Improved mathematical skills in the use of concepts and applied Mathematics on HOTS questions. This can be seen from the questions made by the teachers in this PkM activity, and (3) the participants' satisfaction with this PkM activity is shown by the questionnaire results with a satisfaction range of 85.71% - 96.43%.

Evaluating the Current Ability of ChatGPT to Assist in Professional Otolaryngology Education.

To quantify ChatGPT's concordance with expert Otolaryngologists when posed with high-level questions that require blending rote memorization and critical thinking. Cross-sectional survey. OpenAI's ChatGPT-3.5 Platform. Two board-certified otolaryngologists (HZ, RS) input 2 sets of 30 text-based questions (open-ended and single-answer multiple-choice) into the ChatGPT-3.5 model. Responses were rated on a scale (correct, partially correct, incorrect) by each Otolaryngologist working simultaneously with the AI model. Interrater agreement percentage was based on binomial distribution for calculating the 95% confidence intervals and performing significance tests. Statistical significance was defined as P < .05 for 2-sided tests. In testing open-ended questions, the ChatGPT model had 56.7% of initially answering questions with complete accuracy, and 86.7% chance of answer with some accuracy (corrected agreement = 80.1%; P < .001). For repeat questions, ChatGPT improved to 73.3% with complete accuracy and 96.7% with some accuracy (corrected agreement = 88.8%; P < .001). For multiple-choice questions, the ChatGPT model performed substantially worse (43.3% correct). ChatGPT currently does not provide reliably accurate responses to sophisticated questions in Otolaryngology. Professional societies must be aware of the potential of this tool and prevent unscrupulous use during test-taking situations and consider guidelines for clinical scenarios. Expert clinical oversight is still necessary for myriad use cases (eg, hallucination).

Autonomous Expansion of Grasshopper Wings Reveals External Forces Contribute to Final Adult Wing Shape.

Ecdysis, transformation from juvenile to adult form in insects, is time-consuming and leaves insects vulnerable to predation. For winged insects, the process of wing expansion during ecdysis, unfurling and expanding the wings, is a critical bottleneck in achieving sexual maturity. Internal and external forces play a role in wing expansion. Vigorous abdominal pumping during wing expansion allows insects to pressurize and inflate their wings, filling them with hemolymph. In addition, many insects adopt expansion-specific postures and, if inhibited, do not expand their wings normally, suggesting that external forces such as gravity may play a role. However, two previous studies over 40years ago, reported that the forewings of swarming locusts can expand autonomously when removed from the emerging insect and laid flat on a saline solution. Termed "autoexpansion," we replicated previous experiments of autoexpansion on flat liquid media, documenting changes in both wing length and area over time while also focusing on the role of gravity in autoexpansion. Using the North American bird grasshopper Schistocerca americana, we tested four autoexpansion treatments of varying surface tension and hydrophobicity (gravity, deionized water, buffer, and mineral oil) while simultaneously observing and measuring intact, normal wing expansion. Finally, we constructed a simple model of a viscoelastic expanding wing subjected to gravity, to determine whether it could capture aspects of wing expansion. Our data confirmed that wing autoexpansion does occur in S. americana, but autoexpanding wings, especially hindwings, failed to increase to the same final length and area as intact wings. We found that gravity plays an important role in wing expansion, early in the expansion process. Combined with the significant mass increase we documented in intact wings, it suggests that hydraulic pumping of hemolymph into the wings plays an important role in increasing the area of expanding wings, especially in driving expansion of the large, pleated hindwings. Autoexpansion in a non-swarming orthopteran suggests that local cues driving wing autoexpansion may serve a broader purpose, reducing total expansion time and costs by shifting some processes from central to local control. Documenting wing autoexpansion in a widely studied model organism and demonstrating a mathematical model provides a tractable new system for exploring higher level questions about the mechanisms of wing expansion and the implications of autoexpansion, as well as potential bioinspiration for future technologies applicable to micro-air vehicles, space exploration, or medical and prosthetic devices.

A Taxonomic Analysis of the Questions Prepared by Prospective Primary Teachers for Primary School Mathematics and Turkish Language Courses

The aim of this study is to investigate the cognitive process dimension of the revised Bloom’s taxonomy in the question preparation of prospective primary school teachers in fourth grade Turkish and mathematics courses. This research is a descriptive study carried out following the survey model, one of the quantitative research methods. The study group consisted of 85 prospective teachers in their final year of the undergraduate programme in primary education at a state and foundation university in Turkey. The data were obtained through questions prepared by the prospective teachers through question preparation forms on Turkish and mathematics courses. These data were analyzed using the descriptive analysis method. The study concluded that the majority of questions prepared by prospective primary teachers in both courses were in the lower levels of the cognitive process dimension of revised Bloom’s Taxonomy (remember, understand and apply). It was found that the questions in the Turkish language course were mostly prepared in the remember and understand dimensions and in the mathematics course were mostly prepared in the apply and understand dimensions. In light of these results, it can be suggested that the preparation of high-level questions in terms of the revised Bloom’s taxonomy should be included more in undergraduate education.

‘Words are Flowing Out Like Endless Rain Into a Paper Cup’: ChatGPT &amp; Law School Assessments

ChatGPT is a sophisticated large-language model able to answer high-level questions in a way that is does not trigger conventional plagiarism detectors. Concerns have been raised that and similar forms of ‘generative AI’ pose a significant threat to academic integrity in higher education. To evaluate this risk in the context of legal education specifically, this project had ChatGPT (using the GPT3.5 model available in January 2023) generate answers to twenty-four different exams from an English-language law school based in a common law jurisdiction. It found that the system performed best on exams that were essay-based and asked students to discuss international legal instruments or general legal principles not necessarily specific to any jurisdiction. It performed worst on exams that featured problem-style or “issue spotting” questions asking students to apply an invented factual scenario to local legislation or jurisprudence. While the project suggests that for the most part conventional law school assessments are for the time being relatively immune from the threat generative AI brings, the project provides only a baseline snapshot of how large-language models tackle assessment in higher education. As both the technology improves and students learn how to harness it, increasingly fewer forms of assessment will be beyond its reach. However, rather than attempt to block students from using AI as part of learning and assessment, this paper instead proposes three ways students may be taught to use it in appropriate and ethical ways. While it is clear that generative AI will change how universities teach and assess (across disciplines), a solution of prevention or denial is no solution at all.

Developing Thinking Skills through Project-based Learning

The article describes the role of the project-based learning method, which is a trend of updated educational content, in the formation of thinking skills. Project-based learning or a project method is seen as a means of developing critical, deeper, or higher order thinking skills than standard thinking models. It shows that it is possible to improve the quality of teaching by Kazakhstani school teachers by establishing interdisciplinary communication with other subjects with the help of a foreign language, by presenting various projects to the attention of students in the school context, and by aiming to teach based on the interests and needs of students. The effectiveness of the CLIL method in organizing project-based learning in foreign language classes is enormous. By organizing interdisciplinary integrations, it is possible to form connections between linguistic disciplines and science lessons. In addition, by presenting small researches and projects to the attention of the students, it allows to define the research abilities and focuses of the students. In order to improve the quality of the lesson, it should be noted that during the question-and-answer session with students, high-level questions not only think deeply, critically and creatively, but also create conditions for forming their own opinions. Also, in the process of working with various projects, students' knowledge of the topics is not only superficial, but fully covered. At the end of each section, in accordance with the subject and goals of the lesson, information is provided on how Kazakhstani students can develop thinking skills by introducing work with projects. In the process of using project-based learning methods, Kazakhstani and world practices were used.

High-Order Questions Improve Students' Critical Thinking Skills In Elementary Schools

Critical thinking is an essential and well-established skill for meeting workers' demands in the 21st century. In today's era, teaching and training students in critical thinking skills, even from elementary school, is essential. However, there are still many students who have low critical thinking skills. This study aims to analyze and describe the effect of the effectiveness of high-level questions in developing students' critical thinking skills in elementary schools. The research method used a one-group pretest and posttest experimental design, which was carried out in three elementary schools. The population of this study was three teachers and 102 students. Instruments in this quantitative research include questionnaires, observations, and interview guidelines. Data analysis techniques in this study are descriptive qualitative and quantitative analysis and inferential statistics. The study results showed a significant difference between the pretest and posttest scores of the students obtained from the results of studying the high-level questions. Second, high-level questions are practical for developing elementary school students critical thinking skills. It is concluded that high-order questions can improve critical thinking skills in elementary school students.

ChatGPT Performance on the American Urological Association Self-assessment Study Program and the Potential Influence of Artificial Intelligence in Urologic Training

To assess chat generative pre-trained transformer's (ChatGPT) performance onthe AmericanUrological Association Self-Assessment Study Program (AUA SASP) and stratify performance by question stem complexity. Questions from the 2021-2022 AUA SASP programwere administered to ChatGPT version 3 (ChatGPT-3). Questions were administered to the model utilizing a standardized prompt. The answer choice selected by ChatGPT was then used to answer the question stem in the AUA SASP program. ChatGPT was then prompted to assign a question stem order (first, second, third) to each question. The percentage of correctly answered questions was determined for each order level. All responses provided by ChatGPT were qualitatively assessed for appropriate rationale. A total of 268 questions were administered to ChatGPT. ChatGPT performed better on 2021 compared to the2022 AUA SASP question set, answering 42.3% versus 30.0% of questions correctly (P<.05). Hundred percentof answer explanations provided appropriate, relevant rationale regardless of whether the answer was correct. Further stratification included assessment by question order level. ChatGPT performed progressively better on the 2021 question set with decreasing order levels, with first-order questions reaching 53.8% (n=14). However, differences in proportions did not reach statistical significance (P>.05). ChatGPT answered many high-level questions correctly and provided a reasonable rationale for each answer choice. While ChatGPT was unable to answer numerous first-order questions, future language processing modellearning may lead to the optimization of its fund of knowledge. This may lead to the utilization of artificial intelligence like ChatGPT as an educational tool for urology trainees and professors.