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Artificial intelligence is more present than ever, both in our society in general and in science. At the center of this development has been the concept of deep learning, the use of artificial neural networks that are many layers deep and can often reproduce human‐like behavior much better than other machine‐learning techniques. The articles in this collection are some recent examples of its application for X‐ray photon science and crystallography that have been published in Journal of Applied Crystallography. [ABSTRACT FROM AUTHOR]

Background: Malnutrition is associated with increased morbidity, mortality, and healthcare costs. Early detection is important for timely intervention. This paper assesses the ability of a machine learning screening tool (MUST‐Plus) implemented in registered dietitian (RD) workflow to identify malnourished patients early in the hospital stay and to improve the diagnosis and documentation rate of malnutrition. Methods: This retrospective cohort study was conducted in a large, urban health system in New York City comprising six hospitals serving a diverse patient population. The study included all patients aged ≥ 18 years, who were not admitted for COVID‐19 and had a length of stay of ≤ 30 days. Results: Of the 7736 hospitalisations that met the inclusion criteria, 1947 (25.2%) were identified as being malnourished by MUST‐Plus‐assisted RD evaluations. The lag between admission and diagnosis improved with MUST‐Plus implementation. The usability of the tool output by RDs exceeded 90%, showing good acceptance by users. When compared pre‐/post‐implementation, the rate of both diagnoses and documentation of malnutrition showed improvement. Conclusion: MUST‐Plus, a machine learning‐based screening tool, shows great promise as a malnutrition screening tool for hospitalised patients when used in conjunction with adequate RD staffing and training about the tool. It performed well across multiple measures and settings. Other health systems can use their electronic health record data to develop, test and implement similar machine learning‐based processes to improve malnutrition screening and facilitate timely intervention. Key points/Highlights: Malnutrition is prevalent among hospitalised patients and frequently goes unrecognised, with the potential for severe sequelae. Accurate diagnosis, documentation and treatment of malnutrition have the potential of having a positive impact on morbidity rate, mortality rate, length of inpatient stay, readmission rate and hospital revenue. The tool's successful application highlights its potential to optimise malnutrition screening in healthcare systems, offering potential benefits for patient outcomes and hospital finances. [ABSTRACT FROM AUTHOR]

Formative assessment is essential for improving teaching and learning, and AI and visualization techniques provide great potential for its design and delivery. Using NLP, cognitive diagnostic and visualization techniques designed to analyse and present students' monthly exam data, we developed an AI‐enabled visual report tool comprising six modules and conducted an empirical study of its effectiveness in a high school biology classroom. A total of 125 students in a ninth‐grade biology course were assigned to a treatment group (n = 63) receiving AI‐enabled visual reports as the intervention and a control group (n = 62) receiving overall oral feedback from the teacher. We present the main statistical results of the within‐subjects design and the between‐subjects design respectively, to better capture the main findings. Repeated measures ANOVA revealed a significant interaction effect of intervention and time on learning achievement, and the paired‐sample Wilcoxon test indicated that the treatment group had experienced increasing learning anxiety (Cohen's d = 0.203, p = 0.046) and self‐efficacy (Cohen's d = 1.793, p = 0.000) over time. Moreover, we conducted a series of non‐parametric tests to compare the effects of AI‐enabled visual reports and teacher feedback, but found no significant differences except for an increased self‐efficacy (Cohen's d = 0.312, p = 0.046). Additionally, we had the students in the treatment group rate their favourable modules in the AI‐enabled visual report and provide evaluative feedback. The study results provide important insights into the design and implementation of effective formative assessment supported by artificial AI and visualization techniques. Practitioner notesWhat is already known about this topic Formative assessment is essential for improving teaching and learning.Traditional formative assessment tools lack accurate data‐oriented assessment and usability.AI and visualization techniques have great potential for formative assessment.What this paper adds This study designs and implements an AI‐enabled visual report tool that generates data‐driven, user‐friendly reports.The AI‐enabled visual report can not only enhance students' learning achievement and self‐regulated learning over time but also increase their test anxiety.The AI‐enabled visual report has a comparable effect with teacher feedback but leads to increased self‐efficacy.Implications for practice and/or policy We recommend using the AI‐enabled visual report in large‐size classes for its overall positive effects on both learning achievement and self‐regulated learning.We recommend using the AI‐enabled visual report over teacher feedback for its capacity to enhance students' self‐efficacy.We recommend prioritizing the modules of Performance Ranking, Personal Mastery and Knowledge Alert when designing the AI‐enabled visual report. [ABSTRACT FROM AUTHOR]

Background: The increasing prevalence of Artificial Intelligence (AI) language models, exemplified by ChatGPT, has sparked inquiries into their influence on creative writing skills in educational contexts. This study aims to quantitatively investigate whether ChatGPT's use negatively affects university students' creative writing abilities, focusing on originality, content presentation, accuracy, and elaboration in essays. The research adopts an experimental approach to shed light on this concern. Objective: This study aims to quantitatively investigate whether the utilization of ChatGPT, an AI chatbot, adversely affects specific dimensions of creative writing skills among university students, with an emphasis on originality, content presentation, accuracy, and elaboration. Method: The experimental study involves 600 students from 10 universities, divided into a control and an experimental group (EGp). The EGp incorporates ChatGPT in their creative writing process as an intervention. The study evaluates originality, content presentation, accuracy, and elaboration, utilizing the Wilcoxon Signed‐Rank Test for analysis. Results and Conclusion: The findings reveal a detrimental association between ChatGPT use and university students' creative writing abilities. Analysing both machine‐based and human‐based assessments substantiates earlier qualitative observations regarding ChatGPT's adverse impact on creative writing. This study highlights the necessity of approaching AI integration, particularly in creative writing disciplines, with caution. While AI tools have merits, their integration should be thoughtful, considering the potential drawbacks. These insights inform future research and educational practices, guiding the effective incorporation of AI while nurturing students' writing skills. Lay Description: What is already known about this topic: ChatGPT poses an ethical dilemma regarding its use in the field of academiaQualitative claims and opinions have been raised in prior studies regarding its use in the creative writing processPrior studies have both supported and opposed its use but with very limited quantitative approaches while most of the opinions remain qualitativeSome prior studies opine in support of ChatGPT's ability as an authorSeveral factors measuring creativity has been identified by previous studies but a constructive approach in the light of advanced Artificial Intelligence (AI) based chatbots like ChatGPT is missing in such literature What this paper adds: An experimental approach to provide a valid quantitative proof of the qualitative claims over ChatGPT's detrimental effect towards creativity in writing, which was absent in prior studiesA multifactor‐based formula to measure creativity in a quantitative formA quantitative view of the factors that are affected in either a positive way or a negative way in a user by ChatGPT, providing a holistic picture to determine its extent of useA statistical and theoretical understanding over an unexplored topic like creative writing in the light of ChatGPTA quantitative proof why ChatGPT should not be considered as an author Implications for practice and/or policy: Educators may implement changes in assigning tasks to students compared to their earlier practices, based on the identified factors that are being affected negatively, to ensure ChatGPT does not hinder a student's creativity at a greater extentThe extent of using ChatGPT should be limited to self‐learning as positive effect was experienced through the experimentPolicymakers may use the findings of the study to impose strict policies in academia for ensuring academic integrity (Example: must use of plagiarism detecting software for checking scripts, assigning tasks to students which require more analytical abilities, providing tasks which are not properly readable by LLM's like ChatGPT such as image‐based questions, case studies etc.) [ABSTRACT FROM AUTHOR]

Data extraction is a time‐consuming and resource‐intensive task in the systematic review process. Natural language processing (NLP) artificial intelligence (AI) techniques have the potential to automate data extraction saving time and resources, accelerating the review process, and enhancing the quality and reliability of extracted data. In this paper, we propose a method for using Bing AI and Microsoft Edge as a second reviewer to verify and enhance data items first extracted by a single human reviewer. We describe a worked example of the steps involved in instructing the Bing AI Chat tool to extract study characteristics as data items from a PDF document into a table so that they can be compared with data extracted manually. We show that this technique may provide an additional verification process for data extraction where there are limited resources available or for novice reviewers. However, it should not be seen as a replacement to already established and validated double independent data extraction methods without further evaluation and verification. Use of AI techniques for data extraction in systematic reviews should be transparently and accurately described in reports. Future research should focus on the accuracy, efficiency, completeness, and user experience of using Bing AI for data extraction compared with traditional methods using two or more reviewers independently. [ABSTRACT FROM AUTHOR]

Levi Strauss & Co., a popular fashion label commonly known as Levi's, was involved in a crisis situation in March 2023 as a result of their partnership with Lalaland.ai, an artificial intelligence (AI) company. The partnership was created with the intention of using AI-generated models to show more diversity in Levi's modelling. However, the brand received intense backlash and criticism following the partnership's announcement for cheapening diversity by failing to use real models. In the format of a case study, this paper describes the situation and evaluates Levi's crisis response in this relevant and dynamic dilemma. [ABSTRACT FROM AUTHOR]