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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]

Traditionally, nature has often been thought to be structured by essential forms providing the generic features of natural things and thus the foundations for scientific explanations. In contrast, human history and the social domain have been thought to be the realm of ever‐changing appearances, where contingency prevails. The paper argues that the existence of essential forms is compatible with the contingent, mind‐dependent and historical character of the social world, and that essential forms can also be found in the social domain. Two categories of entities are discussed that suggest themselves to be identified as social forms, namely social kinds and social identities. To this end, it is shown that standard arguments for the existence of essential forms also apply to the social domain, and objections to the existence of social forms are rebutted. Particular attention is paid to the explanatory appeal of social forms. The paper concludes by suggesting that, far from being oppressive, essential social forms are presupposed by liberating social practices. [ABSTRACT FROM AUTHOR]

Leibniz is often cited as an authority when it comes to the formulation and answer strategy of the question "Why is there something rather than nothing?" Yet much current research assumes that Leibniz advocates an unambiguous question and strategy for the answer. In this respect, one repeatedly finds the argument in the literature that alternative explanatory approaches to this question violate Leibniz's intention, since he derives the question from the principle of sufficient reason and also demands a causal explanation to the question. In particular, the new research on non-causal explanatory strategies to the Leibniz question seems to concern this counter-argument. In this paper, however, I will argue that while Leibniz raises the question by means of the principle of sufficient reason, he even favours a non-causal explanatory strategy to the question. Thus, a more accurate Leibniz interpretation seems not only to legitimise but also to support non-causal explanations to the Leibniz question. [ABSTRACT FROM AUTHOR]

The automotive industry has witnessed an increasing level of development in the past decades; from manufacturing manually operated vehicles to manufacturing vehicles with a high level of automation. With the recent developments in Artificial Intelligence (AI), automotive companies now employ blackbox AI models to enable vehicles to perceive their environment and make driving decisions with little or no input from a human. With the hope to deploy autonomous vehicles (AV) on a commercial scale, the acceptance of AV by society becomes paramount and may largely depend on their degree of transparency, trustworthiness, and compliance with regulations. The assessment of the compliance of AVs to these acceptance requirements can be facilitated through the provision of explanations for AVs’ behaviour. Explainability is therefore seen as an important requirement for AVs. AVs should be able to explain what they have ‘seen’, done, and might do in environments in which they operate. In this paper, we provide a comprehensive survey of the existing work in explainable autonomous driving. First, we open by providing a motivation for explanations by highlighting the importance of transparency, accountability, and trust in AVs; and examining existing regulations and standards related to AVs. Second, we identify and categorise the different stakeholders involved in the development, use, and regulation of AVs and elicit their AV explanation requirements. Third, we provide a rigorous review of previous work on explanations for the different AV operations (i.e., perception, localisation, planning, vehicle control, and system management). Finally, we discuss pertinent challenges and provide recommendations including a conceptual framework for AV explainability. This survey aims to provide the fundamental knowledge required of researchers who are interested in explanation provisions in autonomous driving. [ABSTRACT FROM AUTHOR]

People often offer an excuse or an apology after they do something wrong in an attempt to mitigate any potential negative consequences. In this paper, we examine how individuals employ social accounts when explaining their interpersonal transgression of meeting lateness to others in actual work settings. We examined the different combinations of social accounts and the social outcomes (forgiveness, helping behaviors, and intentions to continue interaction) of being late to a meeting. Across two studies using complementary experimental and survey methods, we found that a majority of late arrivers' explanations included remorse and that including remorse significantly influences helping behaviors. Furthermore, we found no interaction between excuses and offering remorse. Implications of these findings and future directions are discussed. [ABSTRACT FROM AUTHOR]

Copyright of Larq Journal for Philosophy, Linguistics & Social Sciences is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) 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.)

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]

In this paper, we explore different possible explanations for research misconduct (especially falsification and fabrication), and investigate whether they are compatible. We suggest that to explain research misconduct, we should pay attention to three factors: (1) the beliefs and desires of the misconductor, (2) contextual affordances, (3) and unconscious biases or influences. We draw on the three different narratives (individual, institutional, system of science) of research misconduct as proposed by Sovacool to review six different explanations. Four theories start from the individual: Rational Choice theory, Bad Apple theory, General Strain Theory and Prospect Theory. Organizational Justice Theory focuses on institutional factors, while New Public Management targets the system of science. For each theory, we illustrate the kinds of facts that must be known in order for explanations based on them to have minimal plausibility. We suggest that none can constitute a full explanation. Finally, we explore how the different possible explanations interrelate. We find that they are compatible, with the exception of explanations based on Rational Choice Theory and Prospect Theory respectively, which are incompatible with one another. For illustrative purposes we examine the case of Diederik Stapel. [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]

This paper discusses a class of mechanistic explanations employed in engineering science where the activities and organization of nonstandard entities (voids, cracks, pits…) are cited as core factors responsible for failures. Given the use of mechanistic language by engineers and the manifestly mechanistic structure of these explanations, I consider several interpretations of these explanations within the new mechanical framework (among others: voids should be considered as shorthand expressions for other entities, voids should be reduced to lower-level mechanisms, or the explanations are simply abstract mechanistic explanations). I argue that these interpretations fail to solve several philosophical problems and propose an account of fictional mechanism explanations instead. According to this account, fictional mechanism explanations provide descriptions of fictional mechanisms that enable the tracking of counterfactual dependencies of the physical system they model by capturing system constraints. Engineers use these models to learn about and understand properties of materials, to build computational simulations of their behaviour, and to design new materials. [ABSTRACT FROM AUTHOR]

The paper examines teacher pragmatic acts (verbal and embodied) in response to children's crying in a Russian preschool. It focuses on crying episodes associated with children's inability or unwillingness to follow the norms and expectations surrounding children's conduct (e.g., crying when not allowed to do something). The paper employs discourse analysis; the data are 40 h of video- and audio-recorded interactions. The analysis shows that teachers used directives and other pragmatic acts (e.g., assessments, explanations) that encouraged children to stop crying. The directives were produced in multi-party participation frameworks where peers were referred to or recruited as active participants. Teachers used positive and negative assessments of crying. Crying was presented in a negative light in contrast with positive non-crying behaviors; positive assessments (praise and affectionate touch) were offered when crying stopped. The consequences of crying were presented as negatively affecting others. It is argued that such discursive treatment of crying contributes to socializing children to conformity. The paper contributes to our understanding of how through the use of pragmatic acts, child crying is discursively treated in ways that invoke norms of pragmatic conduct (e.g., not crying when asked to participate in a planned activity) and social values regarding group membership. • Analysis examines teachers' pragmatic acts (verbal and embodied) in response to crying in a Russian preschool. • Methods utilize discourse analysis. • Findings explore teachers' use of directive trajectories and other pragmatic acts that encouraged children to stop crying. • Conclusions are discussed in relation to norms of pragmatic conduct and social values. [ABSTRACT FROM AUTHOR]

This article reviews conversation analytic research on explanations in pedagogical interaction, particularly in language learning classrooms. In reviewing this literature, this paper aims to provide a comprehensive account of what is interactionally involved when giving pedagogical explanations so that future research investigating the effectiveness of these kinds of explanations can be appropriately measured. The paper first discusses characteristics of explanation as interactional phenomena, namely that they are sequentially organized, either planned or unplanned, and either monologically or dialogically organized. Then, the paper details how explanations in three particular linguistic domains (i.e., pronunciation, grammar, and vocabulary) are accomplished interactionally. In doing so, this paper highlights similarities and differences across linguistic domains that are frequently found in language learning classrooms. The paper ends by identifying patterns across pedagogical explanations and by suggesting directions for future research.

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]

In this paper, we present the formalization and implementation of a decision framework in which a group of agents can express their preferences among a set of alternatives by using several comparison criteria. We define special kind of argument to support agents' preferences, and the set of generated arguments is used both to obtain the set of selected alternatives and to explain why they were selected. We introduce a software application that implements our proposed approach and provides interactive tools for obtaining different kinds of explanations for the computed results. Also, we formally study the proposed argumentation process and we prove that the set of warranted conclusions, which support the obtained results, is consistent. We also show that our framework works properly on choice scenarios with incomplete evidence, and we propose how to optimize the computation process for dynamic environments. [ABSTRACT FROM AUTHOR]

Dung's Argumentation Framework (AF) has been extended in several directions, including the possibility of representing uncertainty about the existence of arguments and attacks. In this regard, two main proposals have been introduced in the literature: Probabilistic Argumentation Framework (PrAF) and Incomplete Argumentation Framework (iAF). PrAF is an extension of AF with probability theory, thus representing quantified uncertainty. In contrast, iAF represents unquantified uncertainty, that is it can be seen as a special case where we only know that some elements (arguments or attacks) are uncertain. In this paper, we first address the problem of computing the probability that a given argument is accepted in PrAF. This is carried out by introducing the concept of probabilistic explanation for any given (probabilistic) extension. We show that the complexity of the problem is FP # P -hard and propose polynomial approximation algorithms with bounded additive error for PrAFs where odd-length cycles are forbidden. We investigate the approximate complexity of the related FP # P -hard problems of credulous and skeptical acceptance in PrAF, showing that they are generally harder than the problem of computing the probability that a given argument is accepted. Next we consider iAF and, after showing some equivalence properties among classes of iAFs, we study iAF as a special case of PrAF where uncertain elements have associated a probability equal to 1/2. Finally, given this result, we investigate the relationships between iAF acceptance problems and probabilistic acceptance in PrAF. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has posed numerous legal--ethical challenges. These challenges are particularly acute when dealing with AI demonstrating substantial computational prowess, which is then correlated with agency or autonomy. A common response to considering this issue is to inquire whether an AI system is "conscious" or not. If it is, then it could constitute an agent, actor, or person. This framing is, however, unhelpful since there are many unresolved questions about consciousness. Instead, a practical approach is proposed, which could be used to better regulate new AI technologies. The value of the practical approach in this study is that it (1) provides an empirically observable, testable framework that contains predictive value; (2) is derived from a data-science framework that uses semantic information as amarker; (3) relies on a self-referential logic which is fundamental to agency; (4) enables the "grading" or "ranking" of AI systems, which provides an alternative method (as opposed to current risk-tiering approaches) and measure to determine the suitability of an AI system within a specific domain (e.g., such as social domains or emotional domains); (5) presents consistent, coherent, and higher informational content as opposed to other approaches; (6) fits within the conception of what informational content "laws" are to contain and maintain; and (7) presents a viable methodology to obtain "agency", "agent", and "personhood", which is robust to current and future developments in AI technologies and society. [ABSTRACT FROM AUTHOR]

Few empirical studies have examined how people understand counterfactual explanations for other people's decisions, for example, "if you had asked for a lower amount, your loan application would have been approved". Yet many current Artificial Intelligence (AI) decision support systems rely on counterfactual explanations to improve human understanding and trust. We compared counterfactual explanations to causal ones, i.e., "because you asked for a high amount, your loan application was not approved", for an AI's decisions in a familiar domain (alcohol and driving) and an unfamiliar one (chemical safety) in four experiments (n = 731). Participants were shown inputs to an AI system, its decisions, and an explanation for each decision; they attempted to predict the AI's decisions, or to make their own decisions. Participants judged counterfactual explanations more helpful than causal ones, but counterfactuals did not improve the accuracy of their predictions of the AI's decisions more than causals (Experiment 1). However, counterfactuals improved the accuracy of participants' own decisions more than causals (Experiment 2). When the AI's decisions were correct (Experiments 1 and 2), participants considered explanations more helpful and made more accurate judgements in the familiar domain than in the unfamiliar one; but when the AI's decisions were incorrect, they considered explanations less helpful and made fewer accurate judgements in the familiar domain than the unfamiliar one, whether they predicted the AI's decisions (Experiment 3a) or made their own decisions (Experiment 3b). The results corroborate the proposal that counterfactuals provide richer information than causals, because their mental representation includes more possibilities. [ABSTRACT FROM AUTHOR]

People often offer an excuse or an apology after they do something wrong in an attempt to mitigate any potential negative consequences. In this paper, we examine how individuals employ social accounts when explaining their interpersonal transgression of meeting lateness to others in actual work settings. We examined the different combinations of social accounts and the social outcomes (forgiveness, helping behaviors, and intentions to continue interaction) of being late to a meeting. Across two studies using complementary experimental and survey methods, we found that a majority of late arrivers’ explanations included remorse and that including remorse significantly influences helping behaviors. Furthermore, we found no interaction between excuses and offering remorse. Implications of these findings and future directions are discussed.

Reciprocal Recommender Systems (RRS) aim to recommend relevant matches to users based on the mutual agreement of their preferences. Explainability of reciprocal recommendations is important for developing a persuasive reciprocal recommender system, since it can improve the effectiveness and credibility of the reciprocal recommendation results. Explainable RRS provide an explanation highlighting why a recommendation would be relevant to the user. Explaining the rationale behind predictions with textual or visual artifacts help in increasing trustworthiness and transparency of the system which is crucial especially for models that are used in critical decision making. In this work, XSiameseBiGRU-UCB, a deep learning contextual bandits framework with post-hoc argumentation based explanations for RRS is proposed. XSiameseBiGRU-UCB is an explainable Siamese neural network-based framework that provides explanations to justify the generated reciprocal recommendations for both the parties involved. In RRS, dilemma between exploitation and exploration requires identifying the best possible recommendation from known information or collecting more information about the environment while generating reciprocal recommendations. To tackle this, we propose to use a contextual bandit policy with upper confidence bound, which adaptively exploits and explores user interests to achieve increased rewards in the long run. Experimental studies conducted with four real-world datasets validate the efficacy of the proposed approach. [ABSTRACT FROM AUTHOR]

Deep networks have shown strong performance in glioma grading; however, interpreting their decisions remains challenging due to glioma heterogeneity. To address these challenges, the proposed solution is the Causal Segmentation Framework (CSF). This framework aims to accurately predict high- and low-grade gliomas while simultaneously highlighting key subregions. Our framework utilizes a shrinkage segmentation method to identify subregions containing essential decision information. Moreover, we introduce a glioma grading module that combines deep learning and traditional approaches for precise grading. Our proposed model achieves the best performance among all models, with an AUC of 96.14%, an F1 score of 93.74%, an accuracy of 91.04%, a sensitivity of 91.83%, and a specificity of 88.88%. Additionally, our model exhibits efficient resource utilization, completing predictions within 2.31s and occupying only 0.12 GB of memory during the test phase. Furthermore, our approach provides clear and specific visualizations of key subregions, surpassing other methods in terms of interpretability. In conclusion, the Causal Segmentation Framework (CSF) demonstrates its effectiveness at accurately predicting glioma grades and identifying key subregions. The inclusion of causality in the CSF model enhances the reliability and accuracy of preoperative decision-making for gliomas. The interpretable results provided by the CSF model can assist clinicians in their assessment and treatment planning. [ABSTRACT FROM AUTHOR]

In recent years the use of complex machine learning has increased drastically. These complex black box models trade interpretability for accuracy. The lack of interpretability is troubling for, e.g., socially sensitive, safety-critical, or knowledge extraction applications. In this paper, we propose a new explanation method, SLISE, for interpreting predictions from black box models. SLISE can be used with any black box model (model-agnostic), does not require any modifications to the black box model (post-hoc), and explains individual predictions (local). We evaluate our method using real-world datasets and compare it against other model-agnostic, local explanation methods. Our approach solves shortcomings in other related explanation methods by only using existing data instead of sampling new, artificial data. The method also generates more generalizable explanations and is usable without modification across various data domains.

Income inequality is growing in many parts of the world and, for the poorest children in a society, is associated with multiple, negative, developmental outcomes. This review of the research literature considers how childrens' and adolescents' understanding of economic inequality changes with age. It highlights shifts in conceptual understanding (from 'having and not having', to social structural and moral explanations), moral reasoning and the impact of the agents of socialization from parents to the media and cultural norms and discourses. It also examines how social processes affect judgements and the importance of an emerging sense of self in relation to questions of economic inequality. Finally, the review covers methodological considerations and suggests pathways for future research. [ABSTRACT FROM AUTHOR]

For critical operational decisions (e.g. consulting services), explanations and interpretable results of powerful Artificial Intelligence (AI) systems are becoming increasingly important. Knowledge graphs possess a semantic model that integrates heterogeneous information sources and represents knowledge elements in a machine-readable form. The integration of knowledge graphs and machine learning methods represents a new form of hybrid intelligent systems that benefit from each other's strengths. Our research aims at an explainable system with a specific knowledge graph architecture that generates human-understandable results even when no suitable domain experts are available. Against this background, the interpretability of a knowledge graph-based explainable AI approach for business process analysis is focused. We design a framework of interpretation, show how interpretable models are generated by a single case study and evaluate the applicability of our approach by different expert interviews. Result paths on weaknesses and improvement measures related to a business process are used to produce stochastic decision trees, which improve the interpretability of results. This can lead to interesting consulting self-services for clients or be applied as a device for accelerating classical consulting projects. [ABSTRACT FROM AUTHOR]

This article introduces a network approach to describe the quality of written scientific explanations. Existing approaches evaluate explanations mainly on the level of sentences or as a whole but not on the elementary level of single terms. Moreover, evaluation of explanations is often based on highly inferential scoring techniques. We addressed both issues by converting the elementary structure of terms in explanations into networks (so-called element maps) and analysing these with mathematical measures, thus extracting the size and complexity of an explanation, adequacy, coherence, and use of key terms. A total of 65 explanations of experts and students were analysed quantitatively and qualitatively. Differences between expert and student maps' measures can be interpreted meaningfully against the background of existing research findings. Thus, we argue that our approach using network analysis provides a precise, fine-grained, and low-inferential tool that complements and refines existing approaches. Element maps have the potential to improve teaching and research by precisely revealing the strengths and weaknesses of explanations. [ABSTRACT FROM AUTHOR]

We propose answer-set programs that specify and compute counterfactual interventions on entities that are input on a classification model. In relation to the outcome of the model, the resulting counterfactual entities serve as a basis for the definition and computation of causality-based explanation scores for the feature values in the entity under classification, namely responsibility scores. The approach and the programs can be applied with black-box models, and also with models that can be specified as logic programs, such as rule-based classifiers. The main focus of this study is on the specification and computation of best counterfactual entities, that is, those that lead to maximum responsibility scores. From them one can read off the explanations as maximum responsibility feature values in the original entity. We also extend the programs to bring into the picture semantic or domain knowledge. We show how the approach could be extended by means of probabilistic methods, and how the underlying probability distributions could be modified through the use of constraints. Several examples of programs written in the syntax of the DLV ASP-solver, and run with it, are shown. [ABSTRACT FROM AUTHOR]

In this paper, we present a fundamental framework for defining different types of explanations of AI systems and the criteria for evaluating their quality. Starting from a structural view of how explanations can be constructed, i.e., in terms of an explanandum (what needs to be explained), multiple explanantia (explanations, clues, or parts of information that explain), and a relationship linking explanandum and explanantia, we propose an explanandum-based typology and point to other possible typologies based on how explanantia are presented and how they relate to explanandia. We also highlight two broad and complementary perspectives for defining possible quality criteria for assessing explainability: epistemological and psychological (cognitive). These definition attempts aim to support the three main functions that we believe should attract the interest and further research of XAI scholars: clear inventories, clear verification criteria, and clear validation methods. • We propose a framework for defining different types of explanations of AI systems. • We contextualize current XAI discourses within the proposed framework. • We highlight two broad perspectives for defining quality criteria for explainability. • We discuss the relevance of our framework in light of current and upcoming AI regulation. • We confer fundamental aspects for future research of XAI scholars. [ABSTRACT FROM AUTHOR]

Learning from instructional explanations is one of the most established, prevalent, and obvious ways of learning--but it carries the risk of shallow processing. Unlike previous research that focused on providing digital just-in-time support measures for learning with explanations, we strived to prepare learners on how to make the most of upcoming explanations. We thus developed a short-term computer-based training intervention on the focused processing of instructional explanations. In two experiments (N1 = 47, N2 = 42), we tested its effects on learning processes and outcomes of a subsequent learning phase. Our results revealed that the training intervention fostered domain-general knowledge about explanations. Furthermore, it enabled learners to benefit from future instructional explanations in other domains (inter-domain transfer for university students, Experiment 1) or at least on other topics (intra-domain transfer for primary school fourth graders, Experiment 2). The digital training intervention did not trigger more cognitive load in the subsequent learning phase. All in all, we describe an initial promising step toward a generic training effect that has the potential advantage of enhancing learning from explanations without altering the actual learning material. [ABSTRACT FROM AUTHOR]