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Background: Deep learning methods, where models do not use explicit features and instead rely on implicit features estimated during model training, suffer from an explainability problem. In text classification, saliency maps that reflect the importance of words in prediction are one approach toward explainability. However, little is known about whether the salient words agree with those identified by humans as important. Objectives: The current study examines in‐line annotations from human annotators and saliency map annotations from a deep learning model (ELECTRA transformer) to understand how well both humans and machines provide evidence for their assigned label. Methods: Data were responses to test items across a mix of United States subjects, states, and grades. Humans were trained to annotate responses to justify a crisis alert label and two model interpretability methods (LIME, Integrated Gradients) were used to obtain engine annotations. Human inter‐annotator agreement and engine agreement with the human annotators were computed and compared. Results and Conclusions: Human annotators agreed with one another at similar rates to those observed in the literature on similar tasks. The annotations derived using the integrated gradients (IG) agreed with human annotators at higher rates than LIME on most metrics; however, both methods underperformed relative to the human annotators. Implications: Saliency map‐based engine annotations show promise as a form of explanation, but do not reach human annotation agreement levels. Future work should examine the appropriate unit for annotation (e.g., word, sentence), other gradient based methods, and approaches for mapping the continuous saliency values to Boolean annotations. Lay Description: What is known about subject matter: Explainability of deep learning models is complicated by their large size, design, and complexity.Explainability methods in deep learning exist but have not been rigorously tested.Saliency maps, as a form of explainability, can be used to generate engine annotations.Human annotations can serve as basis for comparison. What paper adds to knowledge: How well human annotations work in one domain (crisis paper identification).How well two engine annotation methods match human annotations. Implications of study findings: Human annotations have reasonable reliability.Engine annotations have lower reliability than humans.Model‐based engine annotations perform better than model‐agnostic engine annotations. [ABSTRACT FROM AUTHOR]

Purpose: To analyze the AI research in the field of nursing, to explore the current situation, hot topics, and prospects of AI research in the field of nursing, and to provide a reference for researchers to carry out related studies. Methods: We used the VOSviewer 1.6.17, SciMAT, and CiteSpace 5.8.R3 to generate visual cooperation network maps for the country, organizations, authors, citations, and keywords and perform burst detection, theme evolution, and so forth. Findings A total of 9318 articles were obtained from the Web of Science Core Collection database. Four hundred and thirty‐one AI research related to the field of nursing was published by 855 institutions from 54 countries. CIN‐Computers Informatics Nursing was the top productive journal. The United States was the dominant country. The transnational cooperation between authors from developed countries was closer than that between authors from developing countries. The main hot topics included nurse rostering, nursing diagnosis, nursing decision support, disease risk factor prediction, nursing big data management, expert system, support vector machine, decision tree, deep learning, natural language processing, and nursing education. Machine learning represented one of the cutting‐edge and most applicable branches of artificial intelligence in the field of nursing, and deep learning was the hottest technology among many machine learning methods in recent years. One of the most cited papers was published by Burke in 2004 and cited 500 times, which critically evaluated AI methods to deal with nurse scheduling problems. Conclusions: Although AI has been paid more and more attention to the field of nursing, there is still a lack of high‐yielding authors who have been engaged in this field for a long time. Most of the high contribution authors and institutions came from developed countries; therefore, more transnational and multi‐disciplinary cooperation is needed to promote the development of AI in the nursing field. This bibliometric analysis not only provided a comprehensive overview to help researchers to understand the important articles, journals, potential collaborators, and institutions in this field but also analyzed the history, hot spots, and future trends of the research topic to provide inspiration for researchers to choose research directions. [ABSTRACT FROM AUTHOR]

Background: In fact, most schools around the world are not well equipped to have discussions and keep current on the expansion of artificial intelligence (AI) in many aspects of society and economy. They either ignore this conversation, or simply criticize technology, but these resistances are not stopping wide spread of various types of AI projects in schools, mainly driven by corporations, and fueled by incentives that might not match well with long term educational objectives of student success, diversity, equity, and inclusivity. Objectives: The overall purpose is to address the rising gap between the ultrafast development of AI and the meticulous technological application of education, and to suggest the important bridge of building technological leadership in teacher preparation to get ready for the grow of AI in education. Methods: The contextual review examines the field of AI development in education through three lenses: conceptual context, practice context, and research context. Results and Conclusions: The paper provides educators and policy makers an overall background of the phenomenon of AI in education. The study has revealed there is an urgent need for research and development in teacher preparation as well as in the philosophy of technology in education to bridge the gap between AI and education. Lay Description: The initiative addresses the rising gap between the ultrafast development of artificial intelligence and the meticulous technological application of education.To get ready for AIED, the project advises educators, technologists, and policy makers to focus on educational leadership, and philosophy of technology in education to overcome the ethical bottleneck hindering AI progress at the philosophical level.The study highlights the critical need for more research on learning and classroom management, teacher preparation programs, school administration, educational philosophy, and educational policy. [ABSTRACT FROM AUTHOR]

Computational objects (eg, algorithms, bots, surveillance technology and data) have become increasingly present in our daily lives and are consequential for our changing relations to texts, multimodality and identity. Yet, our current theories of literacy, and especially the prevalence of mediational and representational perspectives, are inadequate to account for these changing relations. What are the implications for critical literacy education when it takes seriously computational agents that interact, produce and process texts? While such work is only beginning in education, scholars in other fields are increasingly writing about how AI and algorithmic mediation are changing the landscape of online intra‐action, and business strategies and tactics for working with AI are advancing far ahead of critical literacy education. Drawing on our own and others' research into non‐human actors online, and building on posthuman theories of networks, heterogeneous actants and the assemblage, in this conceptual paper, we sketch some of the forms of critical consciousness that media education might provide in this new mixed landscape. Practitioner NotesWhat is already known about this topic AI is a hot topic in education and in public discourse, but critical literacy theories have not sufficiently accounted for how AI and computational agents change what it means to be "critically literate."Technology is an important force in shaping (and is also shaped by) literacy practices and identity.Corporate actors have an enormous influence on the texts we read and write, but this influence is often hidden.What this paper adds We bridge between critical literacy studies and posthumanist theory to conceptualize critical posthuman literacy.We argue for re‐imagining what texts, multimodality and identity are and do in the age of AI.We pose new questions of our texts and ourselves, informed by posthuman critical literacy.Implications for practice and/or policy Today's readers and composers must be able to identify and interrogate networks of computational and human agents that permeate literacy practices.Beyond identifying and understanding computational agents, posthuman critical literacy necessitates that people can actively build more ethical assemblages with computational agents. [ABSTRACT FROM AUTHOR]