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Copyright of JMD: Journal für Mathematik-Didaktik is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper establishes requirements for assessing the usability of Explainable Artificial Intelligence (XAI) methods, focusing on non-AI experts like healthcare professionals. Through a synthesis of literature and empirical findings, it emphasizes achieving optimal cognitive load, task performance, and task time in XAI explanations. Key components include tailoring explanations to user expertise, integrating domain knowledge, and using non-propositional representations for comprehension. The paper highlights the critical role of relevance, accuracy, and truthfulness in fostering user trust. Practical guidelines are provided for designing transparent and user-friendly XAI explanations, especially in high-stakes contexts like healthcare. Overall, the paper's primary contribution lies in delineating clear requirements for effective XAI explanations, facilitating human-AI collaboration across diverse domains. [ABSTRACT FROM AUTHOR]

In the digital era, recommender systems (RS) have become an integral part of our daily interactions, exerting a significant impact on users and society. However, this also raises ethical challenges related to RS that should be considered. Addressing these challenges requires the application of explainable artificial intelligence (XAI) models to make RS more understandable. Based on the current state-of-the-art literature, this paper aims to provide a comprehensive overview of XAI for RS and its ethical implications, with the aim of proposing a research agenda for ethical RS based on XAI. The findings of the literature review show that neural network-based RS have received much attention in terms of offering explanations, while there is a research gap in explaining context-based RS and in evaluating explanations. In addition, a set of ethical challenges for RS are discussed by exploring how explanations for recommendations can contribute to the ethical use of RS. [ABSTRACT FROM AUTHOR]

Medical training is a key element in maintaining and improving today's healthcare standards. Given the nature of medical work, students must master not only theory but also develop their hands-on abilities and skills in clinical practice. Medical simulators play an increasing role in supporting the active learning of these students due to their ability to present a large variety of tasks allowing students to train and experiment indefinitely without causing any patient harm. While the criticality of explainable AI systems has been extensively discussed in the literature, the medical training context presents unique user needs for explanations. In this paper, we explore the potential gap of current limitations within simulation-based training, and the role Artificial Intelligence (AI) holds in supporting the needs of medical students in training. Through contextual inquiries and interviews with clinicians in training (N = 9) and subsequent validation with medical experts (N = 4), we obtain an understanding of the shortcomings in current simulation-based training and offer recommendations for future AI-driven training. Our results stress the need for continuous and actionable feedback that resembles the interaction between clinical supervisor and resident in real-world training scenarios while adjusting training material to the residents' skills and prior performance. [ABSTRACT FROM AUTHOR]

Copyright of Biology Teaching is the property of East China Normal University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

When justifying a mathematical statement, textbooks often interweave several instances of justification, creating, what we term, paths of justification. This study aims to characterize the paths of justification of mathematical statements that are offered in eight 7th grade Israeli mathematics textbooks. The study attended to context and compared paths of justification in different mathematical domains (algebra and geometry) and in textbooks intended for students with different achievement levels. Analysis focused on the number of instances of justification in a path, the types of justification of those instances, and their order. Comparing paths of justification between algebra and geometry, and between textbooks intended for students with different levels of achievements, revealed several similarities and differences. The findings portray a complex picture of textbook justifications, underlining the need to be sensitive to context when studying textbook justifications. The study results are discussed in light of relevant literature, highlighting potential factors involved in the way a textbook path of justification of a specific mathematical statement is structured. [ABSTRACT FROM AUTHOR]

Background: Deep learning has demonstrated significant advancements across various domains. However, its implementation in specialized areas, such as medical settings, remains approached with caution. In these high-stake environments, understanding the model's decision-making process is critical. This study assesses the performance of different pretrained Bidirectional Encoder Representations from Transformers (BERT) models and delves into understanding its decision-making within the context of medical image protocol assignment. Methods: Four different pre-trained BERT models (BERT, BioBERT, ClinicalBERT, RoBERTa) were fine-tuned for the medical image protocol classification task. Word importance was measured by attributing the classification output to every word using a gradient-based method. Subsequently, a trained radiologist reviewed the resulting word importance scores to assess the model's decision-making process relative to human reasoning. Results: The BERT model came close to human performance on our test set. The BERT model successfully identified relevant words indicative of the target protocol. Analysis of important words in misclassifications revealed potential systematic errors in the model. Conclusions: The BERT model shows promise in medical image protocol assignment by reaching near human level performance and identifying key words effectively. The detection of systematic errors paves the way for further refinements to enhance its safety and utility in clinical settings. [ABSTRACT FROM AUTHOR]