Frontiers in Artificial Intelligence最新文献

Artificial Intelligence in Education (AIEd) offers advanced tools that can personalize learning experiences and enhance teachers' research capabilities. This paper explores the beliefs of 425 university teachers regarding the integration of generative AI in educational settings, utilizing the UTAUT2 model to predict their acceptance and usage patterns through the Partial Least Squares (PLS) method. The findings indicate that performance expectations, effort expectancy, social influence, facilitating conditions, and hedonic motivation all positively impact the intention and behavior related to the use of AIEd. Notably, the study reveals that teachers with constructivist pedagogical beliefs are more inclined to adopt AIEd, underscoring the significance of considering teachers' attitudes and motivations for the effective integration of technology in education. This research provides valuable insights into the factors influencing teachers' decisions to embrace AIEd, thereby contributing to a deeper understanding of technology integration in educational contexts. Moreover, the study's results emphasize the critical role of teachers' pedagogical orientations in their acceptance and utilization of AI technologies. Constructivist educators, who emphasize student-centered learning and active engagement, are shown to be more receptive to incorporating AIEd tools compared to their transmissive counterparts, who focus on direct instruction and information dissemination. This distinction highlights the need for tailored professional development programs that address the specific beliefs and needs of different teaching philosophies. Furthermore, the study's comprehensive approach, considering various dimensions of the UTAUT2 model, offers a robust framework for analyzing technology acceptance in education.

Introduction: The growing competitiveness and the importance of data availability for organizations have created a demand for intelligent information systems capable of analyzing data to support strategy and decision-making. Organizations are generating more and more data due to new technologies associated with Industry 4.0 and Logistics 4.0, making it essential to transform this data into relevant information to streamline decision-making processes. This paper examines the influence of these technologies on gaining a competitive advantage, specifically in a logistics company, which is scarce in the literature.

Methods: A case study was conducted in a Portuguese company using the Delphi method with 61 participants-employees who use the company's integrated BI tool daily. The participants were presented with a questionnaire via the online platform Welphi, requiring qualitative responses to various statements based on the literature review and the results of semi-structured meetings with the company.

Results: The study aimed to identify areas where employees believe more investment/ development is needed to optimize processes and improve the use of the BI tool in the future. The results indicate that BI is a crucial technology when aligned with a company's objectives and needs, highlighting the necessity of top management's involvement in optimizing the BI tool. Encouraging employees to use the BI tool emerged as a significant factor, underscoring the importance of leadership in innovative projects to achieve greater competitive advantage for the company.

Discussion: This study aims to understand the importance of Business Intelligence (BI) and how its functionalities should be adapted according to a company's strategy and objectives to optimize decision-making processes. Thereby, the discussion focused on the essential role of BI technologies in leveraging the company's competitive advantage.

Studies on reinforcement learning have developed the representation of curiosity, which is a type of intrinsic motivation that leads to high performance in a certain type of tasks. However, these studies have not thoroughly examined the internal cognitive mechanisms leading to this performance. In contrast to this previous framework, we propose a mechanism of intrinsic motivation focused on pattern discovery from the perspective of human cognition. This study deals with intellectual curiosity as a type of intrinsic motivation, which finds novel compressible patterns in the data. We represented the process of continuation and boredom of tasks driven by intellectual curiosity using "pattern matching," "utility," and "production compilation," which are general functions of the adaptive control of thought-rational (ACT-R) architecture. We implemented three ACT-R models with different levels of thinking to navigate multiple mazes of different sizes in simulations, manipulating the intensity of intellectual curiosity. The results indicate that intellectual curiosity negatively affects task completion rates in models with lower levels of thinking, while positively impacting models with higher levels of thinking. In addition, comparisons with a model developed by a conventional framework of reinforcement learning (intrinsic curiosity module: ICM) indicate the advantage of representing the agent's intention toward a goal in the proposed mechanism. In summary, the reported models, developed using functions linked to a general cognitive architecture, can contribute to our understanding of intrinsic motivation within the broader context of human innovation driven by pattern discovery.

Anxiety disorders are psychiatric conditions characterized by prolonged and generalized anxiety experienced by individuals in response to various events or situations. At present, anxiety disorders are regarded as the most widespread psychiatric disorders globally. Medication and different types of psychotherapies are employed as the primary therapeutic modalities in clinical practice for the treatment of anxiety disorders. However, combining these two approaches is known to yield more significant benefits than medication alone. Nevertheless, there is a lack of resources and a limited availability of psychotherapy options in underdeveloped areas. Psychotherapy methods encompass relaxation techniques, controlled breathing exercises, visualization exercises, controlled exposure exercises, and cognitive interventions such as challenging negative thoughts. These methods are vital in the treatment of anxiety disorders, but executing them proficiently can be demanding. Moreover, individuals with distinct anxiety disorders are prescribed medications that may cause withdrawal symptoms in some instances. Additionally, there is inadequate availability of face-to-face psychotherapy and a restricted capacity to predict and monitor the health, behavioral, and environmental aspects of individuals with anxiety disorders during the initial phases. In recent years, there has been notable progress in developing and utilizing artificial intelligence (AI) based applications and environments to improve the precision and sensitivity of diagnosing and treating various categories of anxiety disorders. As a result, this study aims to establish the efficacy of AI-enabled environments in addressing the existing challenges in managing anxiety disorders, reducing reliance on medication, and investigating the potential advantages, issues, and opportunities of integrating AI-assisted healthcare for anxiety disorders and enabling personalized therapy.

In recent years, large language models (LLMs) have seen rapid advancement and adoption, and are increasingly being used in educational contexts. In this perspective article, we explore the open challenge of leveraging LLMs to create personalized learning environments that support the "whole learner" by modeling and adapting to both cognitive and non-cognitive characteristics. We identify three key challenges toward this vision: (1) improving the interpretability of LLMs' representations of whole learners, (2) implementing adaptive technologies that can leverage such representations to provide tailored pedagogical support, and (3) authoring and evaluating LLM-based educational agents. For interpretability, we discuss approaches for explaining LLM behaviors in terms of their internal representations of learners; for adaptation, we examine how LLMs can be used to provide context-aware feedback and scaffold non-cognitive skills through natural language interactions; and for authoring, we highlight the opportunities and challenges involved in using natural language instructions to specify behaviors of educational agents. Addressing these challenges will enable personalized AI tutors that can enhance learning by accounting for each student's unique background, abilities, motivations, and socioemotional needs.

Optimizing warehouse layouts is crucial due to its significant impact on efficiency and productivity. We present an AI-driven framework for automated warehouse layout generation. This framework employs constrained beam search to derive optimal layouts within given spatial parameters, adhering to all functional requirements. The feasibility of the generated layouts is verified based on criteria such as item accessibility, required minimum clearances, and aisle connectivity. A scoring function is then used to evaluate the feasible layouts considering the number of storage locations, access points, and accessibility costs. We demonstrate our method's ability to produce feasible, optimal layouts for a variety of warehouse dimensions and shapes, diverse door placements, and interconnections. This approach, currently being prepared for deployment, will enable human designers to rapidly explore and confirm options, facilitating the selection of the most appropriate layout for their use-case.

Introduction: In this study, we address the challenge of accurate time series forecasting of air passenger demand using historical market demand data from the U.S. commercial aviation industry in the 21st century. Commercial aviation is a major contributor to the U.S. economy, directly or indirectly generating ~US$1.37 trillion annually, or 5% of annual GDP, and supporting more than 10 million jobs (Airlines for America, 2024). Over 1 billion passengers flew through U.S. airports in 2023 (Bureau of Transportation Statistics, 2024a). Using multiple correlated time series inputs predicts future values of multiple interrelated time series and leverages their mutual dependencies to enhance accuracy.

Methods: In this study, we introduce a two-stage algorithm employing a deep neural network for correlated time series forecasting, addressing scenarios where multiple input variables are interrelated. This approach is designed to capture the influence that one time series can exert on another, thereby enhancing prediction accuracy by leveraging these interdependencies. In the first stage, we fit four Recurrent Neural Network (RNN) models to generate accurate univariate forecasts, each functioning as a single input-output model to predict aggregated market demand. The Gated Recurrent Unit (GRU) model was the top performer for our dataset overall. In the second stage, we apply the best fitted model (GRU Model) from Stage 1 to each individual competitor (disaggregated from the market) and then merge all input tensors using the Concatenate function.

Results and discussion: We hope to contribute to the relevant body of knowledge with a deep neural network framework for forecasting market share among competitors in the U.S. commercial aviation industry, as no similar approach has been documented in the literature. Given the importance of the industry, there is potentially great value in applying sophisticated forecasting techniques to achieve accurate predictions of air passenger demand. Moreover, these techniques may have wider applications and can potentially be employed in other contexts.

Preclinical models are ubiquitous and essential for drug discovery, yet our understanding of how well they translate to clinical outcomes is limited. In this study, we investigate the translational success of treatments for Clostridium difficile infection from animal models to human patients. Our analysis shows that only 36% of the preclinical and clinical experiment pairs result in translation success. Univariate analysis shows that the sustained response endpoint is correlated with translation failure (SRC = -0.20, p-value = 1.53 × 10^-54), and explainability analysis of multi-variate random forest models shows that both sustained response endpoint and subject age are negative predictors of translation success. We have developed a recommendation system to help plan the right preclinical study given factors such as drug dosage, bacterial dosage, and preclinical/clinical endpoint. With an accuracy of 0.76 (F1 score of 0.71) and by using only 7 features (out of 68 total), the proposed system boosts translational efficiency by 25%. The method presented can extend to any disease and can serve as a preclinical to clinical translation decision support system to accelerate drug discovery and de-risk clinical outcomes.

Introduction: Artificial Intelligence (AI) is increasingly being integrated into anesthesiology to enhance patient safety, improve efficiency, and streamline various aspects of practice.

Objective: This study aims to evaluate whether AI-generated images accurately depict the demographic racial and ethnic diversity observed in the Anesthesia workforce and to identify inherent social biases in these images.

Methods: This cross-sectional analysis was conducted from January to February 2024. Demographic data were collected from the American Society of Anesthesiologists (ASA) and the European Society of Anesthesiology and Intensive Care (ESAIC). Two AI text-to-image models, ChatGPT DALL-E 2 and Midjourney, generated images of anesthesiologists across various subspecialties. Three independent reviewers assessed and categorized each image based on sex, race/ethnicity, age, and emotional traits.

Results: A total of 1,200 images were analyzed. We found significant discrepancies between AI-generated images and actual demographic data. The models predominantly portrayed anesthesiologists as White, with ChatGPT DALL-E2 at 64.2% and Midjourney at 83.0%. Moreover, male gender was highly associated with White ethnicity by ChatGPT DALL-E2 (79.1%) and with non-White ethnicity by Midjourney (87%). Age distribution also varied significantly, with younger anesthesiologists underrepresented. The analysis also revealed predominant traits such as "masculine, ""attractive, "and "trustworthy" across various subspecialties.

Conclusion: AI models exhibited notable biases in gender, race/ethnicity, and age representation, failing to reflect the actual diversity within the anesthesiologist workforce. These biases highlight the need for more diverse training datasets and strategies to mitigate bias in AI-generated images to ensure accurate and inclusive representations in the medical field.

Introduction: Mentoring is crucial to the success of STEM higher education. The Individual Development Plan (IDP) is a common career development tool in STEM graduate education that facilitates structured mentor-mentee interactions and goal setting. This study examined the integration of AI mentors into the myIDP framework to provide real-time support and career insights.

Methods: Using Google Gemini as an AI mentor, this study developed and assessed AI prompts within the myIDP framework. Eighteen STEM graduate students, primarily from underrepresented groups, were trained to engage with the AI mentor. Their interactions, feedback, and comments were analyzed using sentiment and thematic analysis.

Results: Participants reported positive experiences with AI mentors, noting benefits, such as immediate responses, up-to-date information, access to multiple AI mentors, enhanced ownership of career development, and time savings. However, concerns about misinformation, bias, privacy, equity, and algorithmic influences have also been raised. The study identified two hybrid human-AI mentoring models-Sequential Integration and Concurrent Collaboration-that combine the unique strengths of human and AI mentors to enhance the mentoring process.

Discussion: This study underscores the potential of AI mentors to enhance IDP practices by providing timely feedback and career information, thereby empowering students in their STEM career development. The proposed human-AI mentoring models show promise in supporting underrepresented minorities, potentially broadening participation in STEM fields.