Explicit Reasoning Research Articles

A graphical model to make explicit pharmacist clinical reasoning during medication review

Pharmacists’ roles have evolved substantially from traditional drug compounding and dispensing to encompass patient-centred clinical services. Pharmacist clinical reasoning, though fundamental to these new roles, generally remains implicit and understudied, particularly compared with that of other healthcare professionals, such as physicians. However, teaching and supervising the clinical services provided by pharmacists require a thorough understanding of the reasoning process involved. Several models describing pharmacist clinical reasoning have been developed, but they lack unified mapping.Here, we used an instrumental case study approach to develop a model of pharmacist clinical reasoning during medication review. Our model is adapted from a previously published modelling-using-typified-objects model of physician clinical reasoning in all its cognitive complexity. Our pharmacist model, validated after iterative development and expert consultation, aligns components of pharmacist clinical reasoning with those of physician clinical reasoning. The clinical case contains drug-related problems of variable clinical relevance, as well as numerous key elements (e.g., laboratory results, vital signs) necessary for conducting a medication review. The case serves both as the foundation for model development and as an illustrative step-by-step example within this article.Our model delineates the subprocesses of pharmacist clinical reasoning during medication review, offering a flexible, multipath structure that underscores the dynamic, nonlinear nature of the reasoning. The model might be able to clarify implicit cognitive processes, thus furthering the overarching objective of promoting reflective skill development among learners rather than relying solely on tacit knowledge gained through practice experience.

Human reasoning on social interactions in ecological contexts: insights from the theory of mind brain circuits.

The relationship between neural social cognition patterns and performance on social cognition tasks in daily life is a topic of debate, with key consideration given to the extent to which theory of mind (ToM) brain circuits share properties reflecting everyday social functioning. To test the efficacy of ecological stimuli in eliciting brain activation within the ToM brain circuits, we adapted the Edinburgh Social Cognition test social scenarios, consisting of dynamic ecological contextually embedded social stimuli, to a fMRI paradigm. Forty-two adults (21 men, mean age ± SD = 34.19 years ±12.57) were enrolled and underwent an fMRI assessment which consisted of a ToM task using the Edinburgh Social Cognition test scenarios. We used the same stimuli to prompt implicit (movie viewing) and explicit (silent and two-choice answers) reasoning on cognitive and affective mental states. The fMRI analysis was based on the classical random effect analysis. Group inferences were complemented with supplemental analyses using overlap maps to assess inter-subject variability. We found that explicit mentalizing reasoning yielded wide neural activations when two-choice answers were used. We also observed that the nature of ToM reasoning, that is, affective or cognitive, played a significant role in activating different neural circuits. The ESCoT stimuli were particularly effective in evoking ToM core neural underpinnings and elicited executive frontal loops. Future work may employ the task in a clinical setting to investigate ToM network reorganization and plasticity.

Explicit Knowledge Graph Reasoning for Conversational Recommendation

Traditional recommender systems estimate user preference on items purely based on historical interaction records, thus failing to capture fine-grained yet dynamic user interests and letting users receive recommendation only passively. Recent conversational recommender systems (CRSs) tackle those limitations by enabling recommender systems to interact with the user to obtain her/his current preference through a sequence of clarifying questions. Recently, there has been a rise of using knowledge graphs (KGs) for CRSs, where the core motivation is to incorporate the abundant side information carried by a KG into both the recommendation and conversation processes. However, existing KG-based CRSs are subject to two defects: (1) there is a semantic gap between the learned representations of utterances and KG entities, hindering the retrieval of relevant KG information; (2) the reasoning over KG is mostly performed with the implicitly learned user interests, overlooking the explicit signals from the entities actually mentioned in the conversation. To address these drawbacks, we propose a new CRS framework, namely, the Knowledge Enhanced Conversational Reasoning (KECR) model. As a user can reflect her/his preferences via both attribute- and item-level expressions, KECR jointly embeds the structured knowledge from two levels in the KG. A mutual information maximization constraint is further proposed for semantic alignment between the embedding spaces of utterances and KG entities. Meanwhile, KECR utilizes the connectivity within the KG to conduct explicit reasoning of the user demand, making the model less dependent on the user’s feedback to clarifying questions. As such, the semantic alignment and explicit KG reasoning can jointly facilitate accurate recommendation and quality dialogue generation. By comparing with strong baselines on two real-world datasets, we demonstrate that KECR obtains state-of-the-art recommendation effectiveness, as well as competitive dialogue generation performance.

Event Causality Identification via Competitive-Cooperative Cognition Networks

Identifying the causal relations between events is an important task in natural language processing (NLP). However, existing methods mainly leverage human empirical information about causality from limited labeled corpus or pseudo-data, which leads to an insufficient understanding of the causal mechanism. Inspired by the dual process theory (DPT) in cognitive science, we propose Competitive-Cooperative Cognition Networks (C3Net), including an intuitive causal reasoner, a logical causal reasoner, and a competitive selection mechanism, which directly learn and imitate the framework of the causal identification process in human brains. On the one hand, our model can inject external causal knowledge into the intuitive causal reasoner to improve the performance of intuitive reasoning. On the other hand, it can make explicit logical reasoning in a three-stage paradigm, mimicking human reasoning behavior: association→review→inference. Furthermore, we devise a novel competitive selection mechanism based on private evidence and historical patterns of each reasoner to determine which reasoner’s inference is more reliable. Experiments demonstrate that our approach can achieve better performance than previous baseline methods.

Explicit Image Caption Reasoning: Generating Accurate and Informative Captions for Complex Scenes with LMM.

The rapid advancement of sensor technologies and deep learning has significantly advanced the field of image captioning, especially for complex scenes. Traditional image captioning methods are often unable to handle the intricacies and detailed relationships within complex scenes. To overcome these limitations, this paper introduces Explicit Image Caption Reasoning (ECR), a novel approach that generates accurate and informative captions for complex scenes captured by advanced sensors. ECR employs an enhanced inference chain to analyze sensor-derived images, examining object relationships and interactions to achieve deeper semantic understanding. We implement ECR using the optimized ICICD dataset, a subset of the sensor-oriented Flickr30K-EE dataset containing comprehensive inference chain information. This dataset enhances training efficiency and caption quality by leveraging rich sensor data. We create the Explicit Image Caption Reasoning Multimodal Model (ECRMM) by fine-tuning TinyLLaVA with the ICICD dataset. Experiments demonstrate ECR's effectiveness and robustness in processing sensor data, outperforming traditional methods.

APEX: affordance-based plan executor for indoor robotic navigation

ABSTRACT The concept of affordance is commonly described as the set of possibilities an environment offers an animal to select and execute actions. In the context of autonomous robot navigation, determining which affordances are available to a mobile agent provides crucial information to perform navigation tasks such as executing navigation plans or exploring new environments. In this work, we provide a robot with the capability of detecting changes in the set of navigation affordances that it has available as it moves through indoor spaces. Specifically, given a navigation plan represented as a sequence of high-level behaviors such as ‘turn left’ or ‘follow a corridor’, we propose the Affordance-Based Plan Executor (APEX) as a new learning-based method to execute this plan based on the identification of navigation affordances. As a relevant fact, the proposed method does not require a map of the environment at execution time. To the best of our knowledge, our work is one of the first to explicitly consider the identification of navigation affordances as a central element to implement the execution of a behavior-based navigation plan in an indoor environment. Our experiments using the Gibson simulator and environments from the Stanford 2D-3D-S dataset suggest that our approach performs well in previously seen environments in comparison to baselines, exceeding alternative approaches' success rate by up to 43%. Additionally, our approach shows promise at generalizing to previously unseen environments, exceeding the success rate of alternative approaches by up to 26.3%. Finally, our experiments confirm that explicit reasoning about affordances is key to APEX's performance.

Utilizing ChatGPT as a scientific reasoning engine to differentiate conflicting evidence and summarize challenges in controversial clinical questions.

Synthesizing and evaluating inconsistent medical evidence is essential in evidence-based medicine. This study aimed to employ ChatGPTas a sophisticated scientific reasoning engine to identify conflicting clinical evidence and summarize unresolved questions to inform further research. We evaluated ChatGPT's effectiveness in identifying conflicting evidence and investigated its principles of logical reasoning. An automated framework was developed to generate a PubMed dataset focused on controversial clinical topics. ChatGPT analyzed this dataset to identify consensus and controversy, and to formulate unsolved research questions. Expert evaluations were conducted 1) on the consensus and controversy for factual consistency, comprehensiveness, and potential harm and, 2) on the research questions for relevance, innovation, clarity, and specificity. The gpt-4-1106-preview model achieved a 90% recall rate in detecting inconsistent claim pairs within a ternary assertions setup. Notably, without explicit reasoning prompts, ChatGPT provided sound reasoning for the assertions between claims and hypotheses, based on an analysis grounded in relevance, specificity, and certainty. ChatGPT's conclusions of consensus and controversies in clinical literature were comprehensive and factually consistent. The research questions proposed by ChatGPT received high expert ratings. Our experiment implies that, in evaluating the relationship between evidence and claims, ChatGPT considered more detailed information beyond a straightforward assessment of sentimental orientation. This ability to process intricate information and conduct scientific reasoning regarding sentiment is noteworthy, particularly as this pattern emerged without explicit guidance or directives in prompts, highlighting ChatGPT's inherent logical reasoning capabilities. This study demonstrated ChatGPT's capacity to evaluate and interpret scientific claims. Such proficiency can be generalized to broader clinical research literature. ChatGPT effectively aids in facilitating clinical studies by proposing unresolved challenges based on analysis of existing studies. However, caution is advised as ChatGPT's outputs are inferences drawn from the input literature and could be harmful to clinical practice.

PENGARUH MODEL REACT TERHADAP BERPIKIR KRITIS PENDIDIKAN AGAMA ISLAM SMA IT AL AMRI

PENGARUH MODEL REACT TERHADAP BERPIKIR KRITIS PENDIDIKAN AGAMA ISLAM SMA IT AL AMRI

NavGPT: Explicit Reasoning in Vision-and-Language Navigation with Large Language Models

Trained with an unprecedented scale of data, large language models (LLMs) like ChatGPT and GPT-4 exhibit the emergence of significant reasoning abilities from model scaling. Such a trend underscored the potential of training LLMs with unlimited language data, advancing the development of a universal embodied agent. In this work, we introduce the NavGPT, a purely LLM-based instruction-following navigation agent, to reveal the reasoning capability of GPT models in complex embodied scenes by performing zero-shot sequential action prediction for vision-and-language navigation (VLN). At each step, NavGPT takes the textual descriptions of visual observations, navigation history, and future explorable directions as inputs to reason the agent's current status, and makes the decision to approach the target. Through comprehensive experiments, we demonstrate NavGPT can explicitly perform high-level planning for navigation, including decomposing instruction into sub-goals, integrating commonsense knowledge relevant to navigation task resolution, identifying landmarks from observed scenes, tracking navigation progress, and adapting to exceptions with plan adjustment. Furthermore, we show that LLMs is capable of generating high-quality navigational instructions from observations and actions along a path, as well as drawing accurate top-down metric trajectory given the agent's navigation history. Despite the performance of using NavGPT to zero-shot R2R tasks still falling short of trained models, we suggest adapting multi-modality inputs for LLMs to use as visual navigation agents and applying the explicit reasoning of LLMs to benefit learning-based models. Code is available at: https://github.com/GengzeZhou/NavGPT.

Document-level relation extraction with global and path dependencies

Document-level relation extraction (RE) focuses on extracting relations for each entity pair in the same sentence or across different sentences of a document. Several existing methodologies aim to capture the intricate interactions among entities across a document by constructing diverse document graphs. However, these graphs frequently cannot sufficiently model the intricate global interactions and concurrent explicit path reasoning. Therefore, we introduce a distinctive graph-based model designed to assimilate global and path dependencies within a document for document-level RE, termed graph-based global and path dependencies (GGP). Specifically, the global dependency component captures interactions between mentions, entities, sentences and, the document through two interconnected graphs: the mention-level graph and the entity-level graph (ELG). To integrate relevant paths essential for the designated entity pair, the path dependency component consolidates information from various multi-hop paths of the target entity pair through an attention mechanism on the ELG. In addition, we devised an innovative method for learning path representation, which encapsulates relations and intermediate entities within the multi-hop path in the ELG. Comprehensive experiments conducted on standard document-level RE and CDR datasets reveal the following key findings: (i) GGP achieves an Ign F1 score of 59.98%, surpassing baselines by 0.61% on the test set; and (ii) the integration of various features derived from entities, sentences, documents, and paths enhances GGP's performance in document-level RE.

Contractual AI: Toward More Aligned, Transparent, and Robust Dialogue Agents

We present a new framework for AI alignment called Contractual AI, and apply it to the setting of dialogue agents chatting with humans. This framework incorporates and builds on previous approaches to alignment, such as Constitutional AI. We propose that fully aligned systems may need both a "think fast" and a "think slow" systems for approximating complex human judgements. Fast thinking (System 1) is computationally cheap but rigid and brittle in novel situations, while slow thinking (System 2) is more expensive but more flexible and robust. System 1 makes judgements by asking whether a rule or principle is violated. System 2 does the explicit reasoning that produces the rules, explicitly tallying costs and benefits for all stakeholders. Rule-based systems like Constitutional AI correspond roughly to System 1. Here, we implement a prototype of System 2, and lay out a road-map for enabling the system to make more thorough and accurate considerations for all stakeholder groups, including those underrepresented in the training data (e.g. racial minorities). For initial testing, we guided the decision process through the steps of: 1) identifying all stakeholders, 2) listing their individual concerns, 3) soliciting the projected opinions of various experts, and 4) combining the expert opinions into a final moral judgement. The resulting text was less generic, more aware of complex stakeholder needs, and ultimately more actionable.

Clinical reasoning during dysphagia assessment and management in acute care: A longitudinal qualitative study.

Competent clinical reasoning forms the foundation for effective and efficient clinical swallowing examination (CSE) and consequent dysphagia management decisions. While the nature of initial CSEs has been evaluated, it remains unclear how new information gathered by speech-language therapists (SLTs) throughout a patient's acute-care journey is integrated into their initial clinical reasoning and management processes and used to review and revise initial management recommendations. To understand how SLTs' clinical reasoning and decision-making regarding dysphagia assessment and management evolve as patients transition through acute hospital care from referral to discharge. A longitudinal, qualitative approach was employed to gather information from two SLTs who managed six patients at a metropolitan acute-care hospital. A retrospective 'think-aloud' protocol was utilized to prompt SLTs regarding their clinical reasoning and decision-making processes during initial and subsequent CSEs and patient interactions. Three types of concept maps were created based on these interviews: a descriptive concept map, a reasoning map and a hypothesis map. All concept maps were evaluated regarding their overall structure, facts gathered, types of reasoning engaged in (inductive versus deductive), types of hypotheses generated, and the diagnosis and management recommendations made following initial CSE and during subsequent dysphagia management. Initial CSEs involved a rich process of fact-gathering, that was predominantly led by inductive reasoning (hypothesis generation) and some application of deductive reasoning (hypothesis testing), with the primary aims of determining the presence of dysphagia and identifying the safest diet and fluid recommendations. During follow-up assessments, SLTs engaged in increasingly more deductive testing of initial hypotheses, including fact-gathering aimed at determining the tolerance of current diet and fluid recommendations or the suitability for diet and/or fluid upgrade and less inductive reasoning. Consistent with this aim, SLTs' hypotheses were focused primarily on airway protection and medical status during the follow-up phase. Overall, both initial and follow-up swallowing assessments were targeted primarily at identifying suitable management recommendations, and less so on identifying and formulating diagnoses. None of the patients presented with adverse respiratory and/or swallowing outcomes during admission and following discharge from speech pathology. Swallowing assessment and management across the acute-care journey was observed as a high-quality, patient-centred process characterized by iterative cycles of inductive and deductive reasoning. This approach appears to maximize efficiency without compromising the quality of care. The outcomes of this research encourage further investigation and translation to tertiary and post-professional education contexts as a clear understanding of the processes involved in reaching diagnoses and management recommendations can inform career-long refinement of clinical skills. What is already known on the subject SLTs' clinical reasoning processes during initial CSE employ iterative cycles of inductive and deductive reasoning, reflecting a patient-centred assessment process. To date it is unknown how SLTs engage in clinical reasoning during follow-up assessments of swallowing function, how they assess the appropriateness of initial management recommendations and how this relates to patient outcomes. What this paper adds to the existing knowledge Our longitudinal evaluation of clinical reasoning and decision-making patterns related to swallowing management in acute care demonstrated that SLTs tailored their processes to each patient's presentation. There was an emphasis on monitoring the suitability of the initial management recommendations and the potential for upgrade of diet or compensatory swallowing strategies. The iterative cycles of inductive and deductive reasoning reflect efficient decision-making processes that maintain high-quality clinical care within the acute environment. What are the potential or actual clinical implications of this work? Employing efficient and high-quality clinical reasoning is a hallmark of good dysphagia practice in maximizing positive patient outcomes. Developing approaches to understanding and making explicit clinical reasoning processes of experienced clinicians may assist SLTs of all developmental stages to provide high standards of care.

NeSyKHG: Neuro-Symbolic Knowledge Hypergraphs

In the rapidly evolving landscape of artificial intelligence, there is an escalating demand for enhanced reasoning capabilities and robust representation of higher-order relationships. Despite their prowess in pattern identification and generalization, Neural networks often grapple with challenges linked to interpretability and the absence of explicit relational reasoning. Symbolic logic, on the other hand, excels in formal knowledge representation and inference yet remains limited in deriving knowledge directly from data. We introduce the Neuro-Symbolic Knowledge Hypergraphs (NeSyKHG) framework to bridge this gap. This innovative model synergistically merges the strengths of neural networks and symbolic logic, offering an optimized representation and reasoning system for higher-order relationships using knowledge hypergraphs. Our empirical evaluations on the Chinese Medical High-order Relational (CMHR) dataset revealed that NeSyKHG significantly outperforms several established baseline models. Specifically, when compared to models such as Hypergraph Neural Networks (HGNN), Hypergraph Convolutional Networks (HyperGCN), Logical Hypergraph Link Prediction (LHP), and Universal Graph Convolutional Networks (UniGCN), NeSyKHG achieved an F1-score of 0.845, an accuracy (ACC) of 0.947, and an area under the curve (AUC) of 0.965. These metrics underscore NeSyKHG's exceptional ability in higher-order relational learning and reasoning.

Automated multimodal sensemaking: Ontology-based integration of linguistic frames and visual data

Frame evocation from visual data is an essential process for multimodal sensemaking, due to the multimodal abstraction provided by frame semantics. However, there is a scarcity of data-driven approaches and tools to automate it. We propose a novel approach for explainable automated multimodal sensemaking by linking linguistic frames to their physical visual occurrences, using ontology-based knowledge engineering techniques. We pair the evocation of linguistic frames from text to visual data as “framal visual manifestations”. We present a deep ontological analysis of the implicit data model of the Visual Genome image dataset, and its formalization in the novel Visual Sense Ontology (VSO). To enhance the multimodal data from this dataset, we introduce a framal knowledge expansion pipeline that extracts and connects linguistic frames – including values and emotions – to images, using multiple linguistic resources for disambiguation. It then introduces the Visual Sense Knowledge Graph (VSKG), a novel resource. VSKG is a queryable knowledge graph that enhances the accessibility and comprehensibility of Visual Genome’s multimodal data, based on SPARQL queries. VSKG includes frame visual evocation data, enabling more advanced forms of explicit reasoning, analysis and sensemaking. Our work represents a significant advancement in the automation of frame evocation and multimodal sense-making, performed in a fully interpretable and transparent way, with potential applications in various fields, including the fields of knowledge representation, computer vision, and natural language processing.

Ipsilateral Lesion Detection Refinement for Tomosynthesis.

Computer-aided detection (CAD) frameworks for breast cancer screening have been researched for several decades. Early adoption of deep-learning models in CAD frameworks has shown greatly improved detection performance compared to traditional CAD on single-view images. Recently, studies have improved performance by merging information from multiple views within each screening exam. Clinically, the integration of lesion correspondence during screening is a complicated decision process that depends on the correct execution of several referencing steps. However, most multi-view CAD frameworks are deep-learning-based black-box techniques. Fully end-to-end designs make it very difficult to analyze model behaviors and fine-tune performance. More importantly, the black-box nature of the techniques discourages clinical adoption due to the lack of explicit reasoning for each multi-view referencing step. Therefore, there is a need for a multi-view detection framework that can not only detect cancers accurately but also provide step-by-step, multi-view reasoning. In this work, we present Ipsilateral-Matching-Refinement Networks (IMR-Net) for digital breast tomosynthesis (DBT) lesion detection across multiple views. Our proposed framework adaptively refines the single-view detection scores based on explicit ipsilateral lesion matching. IMR-Net is built on a robust, single-view detection CAD pipeline with a commercial development DBT dataset of 24675 DBT volumetric views from 8034 exams. Performance is measured using location-based, case-level receiver operating characteristic (ROC) and case-level free-response ROC (FROC) analysis.

Newman and Quasi‐Fideism: A Reply to Duncan Pritchard

Abstract: In recent years, Duncan Pritchard has developed a position in religious epistemology called quasi‐fideism that he claims traces back to John Henry Newman's treatment of the rationality of religious belief. In this paper, we give three reasons to think that Pritchard's reading of Newman as a quasi‐fideist is mistaken. First, Newman's parity argument does not claim that religious and non‐religious beliefs are on a par because both are groundless; instead, for Newman, they are on a par because both often stem from implicit rather than explicit reasoning. Second, pace Pritchard, Newman's distinction between simple and complex assent does not map onto the Wittgensteinian distinction between groundless hinge commitments and beliefs that flow from these hinges. For Newman, simple and complex assent differ in terms of the believer's level of awareness of their grounds. Third, and finally, Newman does not reject Locke's evidentialism in toto. Instead, he argues that certitude is not restricted to beliefs stemming from intuition and demonstration but often rightly includes probabilistically supported (or fallibly evidenced) beliefs.

The effects of explicit reasoning on moral judgements.

We report four experiments that investigate explicit reasoning and moral judgements. In each experiment, some subjects responded to the "footbridge" version of the trolley problem (which elicits stronger moral intuitions), whereas others responded to the "switch" version (which elicits weaker moral intuitions). Experiments 1-2 crossed the type of trolley problem with four reasoning conditions: control, counter-attitudinal, pro-attitudinal, and mixed reasoning (both types of reasoning). Experiments 3-4 examine whether moral judgements vary based on (a) when reasoners engage in counter-attitudinal reasoning, (b) when they make the moral judgement, and (c) by the type of moral dilemma. These two experiments comprised five conditions: control (judgement only), delay-only (2-minute wait then judgement), reasoning-only (reasoning then judgement), reasoning-delay (reasoning, then 2-minute delay, then judgement), and delayed-reasoning (2-minute delay, then reasoning, then judgement). These conditions were crossed with the type of trolley problem. We find that engaging in some form of counter-attitudinal reasoning led to less typical judgements (regardless of when it occurs), but this effect was mostly restricted to the switch version of the dilemma (and was strongest in the reasoning-delay conditions). Furthermore, neither pro-attitudinal reasoning nor delayed judgements on their own impacted subjects' judgements. Reasoners therefore seem open to modifying their moral judgements when they consider opposing perspectives but might be less likely to do so for dilemmas that elicit relatively strong moral intuitions.

I Did You a Favor By Taking Your Work: Reconsidering the Harm-Based Approach To the Fourth Fair Use Factor

American copyright law is predominantly utilitarian. Fair use—codified to promote copyright’s utilitarian goals—has emerged as one of the most nebulous doctrines in American law. This is partially by design. Passing the Copyright Act of 1976 (the “Act”), Congress sought to endorse “the purpose and general scope of the judicial doctrine of fair use,” but made clear that “there is no disposition to freeze the doctrine in the statute, especially during a period of rapid technological change.” This judicial deference—combined with the case-by-case nature of fair use determinations—has led to evolving and inconsistent applications of section 107.
 Interpretation of section 107(4), which instructs courts to consider “the effect of the use upon the potential market for or value of the copyrighted work,” is illustrative. Though a plain reading of section 107(4) leaves room for consideration of all market effects, most courts and commentators agree that the inquiry into this factor concerns only deleterious effects on the copyright owner’s actual or potential markets. Some have expressed hostility toward consideration of positive market effects on the ground that an infringing use that might improve sales of a copyrighted work is also likely to invade the market for derivative works.
 In limited circumstances, courts and commentators have recognized the relevance of an unauthorized use providing financial gain for a copyright holder. In Authors Guild, Inc. v. Google Inc., Judge Chin indicated that Google Books increased exposure and sales for the copyright holders, concluding that “the fourth factor weighs strongly in favor of a finding of fair use.” While the Second Circuit declined to adopt Judge Chin’s explicit reasoning, Authors Guild remains a powerful endorsement of the plain reading of section 107(4). In Google LLC v. Oracle Am., Inc., the Supreme Court indicated that analysis of the fourth factor should weigh public benefits against private harms, endorsing a more holistic balancing of interests. In the wake of a reinvigorated fourth factor, it is worth reassessing whether economic benefits from infringement have any place in the market impact inquiry.
 This Note addresses the current status of market benefits: monetary gains conferred upon a copyright holder by a third party’s unauthorized use of a copyrighted work. While courts are still unwilling to treat market benefits as dispositive, they are moving away from the view that the fourth factor concerns only market harm. Expanded analysis of a fourth factor now balances private economic effects, public benefits, and in some cases, non-monetary concerns related to the value of a work. Market benefit proponents assert that their inclusion is more in line with the text and history 1976 Act, as well as the overall goals of copyright. Further, weighing market benefits against market harms helps to address problems related to circularity.
 This Note considers the viability of market benefits as both a dispositive and a relevant component of fourth factor interpretation. Part I provides a basic overview of the traditional approach to the fourth factor before defining and distinguishing between different types of market benefits. Part II analyzes recent caselaw acknowledging market benefits and proposes a framework for proving market benefits as part of a reinvigorated fourth factor. Part III addresses the viability of market benefit arguments across different sectors of copyrighted works and the possible implications of increased acceptance. It concludes that, despite enhanced recognition, market benefits still lack widespread applicability and are unlikely to change the outcome of any present fair use determination.

Developing a European longitudinal and interprofessional curriculum for clinical reasoning.

Clinical reasoning is a complex and crucial ability health professions students need to acquire during their education. Despite its importance, explicit clinical reasoning teaching is not yet implemented in most health professions educational programs. Therefore, we carried out an international and interprofessional project to plan and develop a clinical reasoning curriculum with a train-the-trainer course to support educators in teaching this curriculum to students. We developed a framework and curricular blueprint. Then we created 25 student and 7 train-the-trainer learning units and we piloted 11 of these learning units at our institutions. Learners and faculty reported high satisfaction and they also provided helpful suggestions for improvements. One of the main challenges we faced was the heterogeneous understanding of clinical reasoning within and across professions. However, we learned from each other while discussing thesedifferent views and perspectives on clinical reasoning and were able to come to a shared understanding as the basis for developing the curriculum. Our curriculum fills an important gap in the availability of explicit clinical reasoning educational materials both for students and faculty and is unique with having specialists from different countries, schools, and professions. Faculty time and time for teaching clinical reasoning in existing curricula remain important barriers for implementation of clinical reasoning teaching.

A Meta-Analysis and Systematic Review of the Effect of Chatbot Technology Use in Sustainable Education

The development of artificial intelligence in recent years has promoted the use of chatbot technology in sustainable education. Many studies examined the effect of chatbots on learning outcomes. However, scant studies summarized the effectiveness of chatbots in education. The aim of the study is to investigate the effect of chatbot-assisted learning on various components and how different moderator variables influenced the effectiveness. This study, through a meta-analysis, reviewed 32 empirical studies with 2201 participants published between 2010 and 2022. The results showed that chatbot technology exerted a medium-to-high effect on overall learning outcomes regardless of moderator variables, i.e., intervention duration, chatbot roles, and learning content. In terms of learning components, chatbots could significantly improve explicit reasoning, learning achievement, knowledge retention, and learning interest despite negative findings in critical thinking, learning engagement, and motivation. Future research could expand chatbot research by including different learning components.