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Journal articles on the topic 'Cognitive Modeling in AI'

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1

Islam, Md Mafiqul. "Cognitive Frameworks for Mitigating Antiblack Bias: Advancing Ethical AI Design and Development." Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 4, no. 1 (2024): 1–12. http://dx.doi.org/10.60087/jaigs.vol4.issue1.p12.

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This paper explores the utilization of cognitive modeling to address the influence of antiblackness and racism on the design and development of AI systems. Through the lens of the ACT-R/Φ cognitive architecture and ConceptNet, an existing knowledge graph system, we investigate this issue from cognitive, sociocultural, and physiological perspectives. We propose an approach that not only examines how antiblackness may permeate AI system design and development, particularly within the realm of software engineering, but also establishes links between antiblackness, human cognition, and computational cognitive modeling. We contend that overlooking sociocultural factors in cognitive architectures perpetuates a colorblind approach to modeling, obscuring the inherent sociocultural context that shapes human behavior and cognitive processes.
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Islam, Md Mafiqul. "Cognitive Frameworks for Mitigating Antiblack Bias: Advancing Ethical AI Design and Development." Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 4, no. 1 (2024): 1–12. http://dx.doi.org/10.60087/jaigs.v4i1.77.

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This paper explores the utilization of cognitive modeling to address the influence of antiblackness and racism on the design and development of AI systems. Through the lens of the ACT-R/Φ cognitive architecture and ConceptNet, an existing knowledge graph system, we investigate this issue from cognitive, sociocultural, and physiological perspectives. We propose an approach that not only examines how antiblackness may permeate AI system design and development, particularly within the realm of software engineering, but also establishes links between antiblackness, human cognition, and computational cognitive modeling. We contend that overlooking sociocultural factors in cognitive architectures perpetuates a colorblind approach to modeling, obscuring the inherent sociocultural context that shapes human behavior and cognitive processes.
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Hoffman, Robert R., Gary Klein, and Shane T. Mueller. "Explaining Explanation For “Explainable Ai”." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 62, no. 1 (2018): 197–201. http://dx.doi.org/10.1177/1541931218621047.

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What makes for an explanation of “black box” AI systems such as Deep Nets? We reviewed the pertinent literatures on explanation and derived key ideas. This set the stage for our empirical inquiries, which include conceptual cognitive modeling, the analysis of a corpus of cases of "naturalistic explanation" of computational systems, computational cognitive modeling, and the development of measures for performance evaluation. The purpose of our work is to contribute to the program of research on “Explainable AI.” In this report we focus on our initial synthetic modeling activities and the development of measures for the evaluation of explainability in human-machine work systems.
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Fletcher, Charles R. "AI Programming Techniques: Good Medicine for Cognitive Modeling." Contemporary Psychology: A Journal of Reviews 33, no. 11 (1988): 973–74. http://dx.doi.org/10.1037/026221.

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Gammal, Yasser. "The “Cognitive” Architectural Design Process and Its Problem with Recent Artificial Intelligence Applications." Engineering and Applied Sciences 9, no. 5 (2024): 83–105. http://dx.doi.org/10.11648/j.eas.20240905.11.

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The connection between cognitive science and the architectural design process reveals significant gaps that limit the full potential of creating effective built environments. A key issue is the insufficient integration of cognitive principles into design workflows. Architects frequently rely on traditional methods and aesthetic considerations without fully understanding how spatial configurations influence human cognition and behavior. While recent AI applications in architecture, such as Computer-Aided Drafting (CAD), Building Information Modeling (BIM), and interactive web and VR presentations, show promising advancements, AI still struggles with complex architectural functions. AI lacks the creativity and imagination inherent in human cognition. It operates based on fixed programming, producing specific outcomes and requiring human oversight to apply insights from one dataset to another. The primary challenge in using AI for architectural design is ensuring minimal design flaws, as replicating human cognitive abilities with AI and various machine learning techniques remains difficult. This research paper aims to explore the relationship between cognitive science, artificial intelligence, and the architectural design process through four main objectives: First, to investigate the integration of AI with current architectural software applications. Second, to examine potential connections between AI and major architectural design trends. Third, to define two frameworks for the Cognitive Architectural Design Process to guide the development of AI systems in architectural design by analyzing key cognitive design theories. Finally, to create a proposed "Architectural Design Process-Cognitive Pilot Map" from an architectural perspective to aid AI programmers in developing architecture design software applications.
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Greif, Hajo. "Exploring Minds: Modes of Modeling and Simulation in Artificial Intelligence." Perspectives on Science 29, no. 4 (2021): 409–35. http://dx.doi.org/10.1162/posc_a_00377.

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Abstract The aim of this paper is to grasp the relevant distinctions between various ways in which models and simulations in Artificial Intelligence (AI) relate to cognitive phenomena. In order to get a systematic picture, a taxonomy is developed that is based on the coordinates of formal versus material analogies and theory-guided versus pre-theoretic models in science. These distinctions have parallels in the computational versus mimetic aspects and in analytic versus exploratory types of computer simulation. The proposed taxonomy cuts across the traditional dichotomies between symbolic and embodied AI, general intelligence and symbol and intelligence and cognitive simulation and human/non-human-like AI. According to the taxonomy proposed here, one can distinguish between four distinct general approaches that figured prominently in early and classical AI, and that have partly developed into distinct research programs: first, phenomenal simulations (e.g., Turing’s “imitation game”); second, simulations that explore general-level formal isomorphisms in pursuit of a general theory of intelligence (e.g., logic-based AI); third, simulations as exploratory material models that serve to develop theoretical accounts of cognitive processes (e.g., Marr’s stages of visual processing and classical connectionism); and fourth, simulations as strictly formal models of a theory of computation that postulates cognitive processes to be isomorphic with computational processes (strong symbolic AI). In continuation of pragmatic views of the modes of modeling and simulating world affairs, this taxonomy of approaches to modeling in AI helps to elucidate how available computational concepts and simulational resources contribute to the modes of representation and theory development in AI research—and what made that research program uniquely dependent on them.
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Beka, Dadeshkeliani. "The Psychology of Artificial Intelligence: Analyzing Cognitive and Emotional Characteristics, Human-Ai Interaction, and Ethical Considerations." International Journal of Social Science and Human Research 08, no. 03 (2025): 1508–14. https://doi.org/10.5281/zenodo.15030137.

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This academic paper explores key issues in the psychology of artificial intelligence (AI) from an interdisciplinary perspective. The study examines the cognitive and emotional characteristics of AI and compares them to human psychology, while also analyzing how human-AI interactions are formed from a psychological standpoint. The paper critically evaluates theoretical frameworks that link cognitive sciences and AI modeling, including cognitive modeling and neural networks, which enable the computational simulation of human cognitive processes. Special attention is given to ethical issues, such as the risks of algorithmic bias, the application of emotional AI, and its psychological effects. Additionally, the paper addresses the socio-ethical challenges that arise in human-artificial agent relationships. Finally, the study reviews ongoing debates on the potential consciousness of AI, questioning whether machines can attain subjective awareness and the philosophical and ethical implications of such a possibility. The research is based on a literature review methodology, integrating the analysis of academic sources to present the current state of knowledge in this field and outline future research prospects.
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Yang, Songyu. "A Comprehensive Comparative Study of Intuitive Physics Modeling in Machine Learning Trained with Cartoon and Realistic Data." Applied and Computational Engineering 104, no. 1 (2024): 165–70. http://dx.doi.org/10.54254/2755-2721/104/20241147.

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Abstract. This study delves into the influence of training data typesspecifically cartoon versus realistic visual datasetson the development of intuitive physics modeling in machine learning. Intuitive physics, the inherent human ability to understand and predict the physical properties and dynamics of objects, presents a significant challenge for current AI systems to replicate accurately. Leveraging YOLOv5, a cutting-edge object detection model, this research systematically evaluates the cognitive understanding and performance of AI models trained on distinct types of visual data. The findings reveal that the visual complexity inherent in the training datasets plays a crucial role in shaping the model's ability to generalize and accurately perform intuitive physics tasks. Models trained on cartoon datasets exhibited different learning patterns and generalization capabilities compared to those trained on realistic data, providing valuable insights into the role of data representation in AI training. This research offers both theoretical advancements in understanding AI's cognitive limitations and practical guidance for designing AI systems that can interact with the physical world more effectively. Ultimately, the study contributes to bridging the gap between human cognition and machine learning, pushing the boundaries of what AI can achieve in modeling complex, real-world phenomena.
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Vislova, Aminat. "From Metaphor in Humanitarian Paradigm to Computer Metaphor in Cognitive Science." Artificial societies 16, no. 4 (2021): 0. http://dx.doi.org/10.18254/s207751800017558-2.

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The article presents an analysis of metaphor in the humanities and computer metaphor in cognitive psychology and cognitive science in general. Issues related to the emergence and role of computer metaphor in the development of cognitive psychology are discussed. The emphasis is placed on the symbolic approach, which was of paramount importance at the initial stage of the development of cognitive psychology. Particular attention is paid to the computer metaphor in solving urgent problems of modeling the brain and cognitive abilities in the field of artificial intelligence (AI). It is concluded that the appeal to metaphors located at the intersection of the humanities and cognitive sciences and denoting various issues of AI modeling are due to the historical contexts of the development of modern science focused on the integration of knowledge of various orientations.
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Sohaib Mohanna. "Consumer’s Cognitive and Affective Perceptions of Artificial Intelligence (AI) in Social Media: Topic Modelling Approach." Journal of Electrical Systems 20, no. 3 (2024): 1317–26. http://dx.doi.org/10.52783/jes.3539.

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This study aimed to examine consumer perceptions of artificial intelligence (AI) by analyzing YouTube comments using topic modeling. A natural language processing model called BERTopic was employed to identify latent topics in a total of 93,729 English-language comments. These comments were posted on 20 popular AI-related videos, each of which had over 1 million views, between January and December 2021. The analysis of the comment data yielded five major themes, each with its corresponding percentage representation. The first theme, accounting for 27.2% of the comments, revolved around the fear of AI superiority, reflecting concerns about AI surpassing human control. The second theme, comprising 13.4% of the comments, focused on anthropomorphism and the uncanny valley effect, indicating mixed emotions towards human-like robots. The third theme, representing 31.7% of the comments, highlighted AI bias and hallucinations, shedding light on the impact of AI inaccuracies on trust. The fourth theme, accounting for 17.2% of the comments, addressed ethical and moral concerns, particularly concerning AI development and oversight. Lastly, the fifth theme, encompassing 10.5% of the comments, explored AI trustworthiness, emphasizing the reliability of AI in decision-making processes. Based on the findings, this study challenges previous research that primarily reported positive AI experiences. It quantitatively identifies complex negative emotions towards AI technologies across diverse user demographics. Furthermore, it expands our understanding of how anthropomorphism and AI "hallucinations" influence trust, which are critical issues as AI becomes increasingly integrated into society. The application of BERTopic enabled nuanced modeling of online discourse, bridging gaps in our knowledge regarding consumer perceptions of emerging technologies on a large scale. In terms of novelty, this study is the first to utilize BERTopic modeling to uncover consumer experiences with AI by analyzing social media comments on a large scale. The insights obtained from this approach offer compelling new perspectives compared to previous research in this field.
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Pasupuleti, Murali Krishna. "Neural Rationality: Modeling Decision Logic in Deep Neural Architectures." International Journal of Academic and Industrial Research Innovations(IJAIRI) 05, no. 05 (2025): 355–67. https://doi.org/10.62311/nesx/rp05ai3.

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Abstract: This paper introduces the concept of Neural Rationality, a framework that aims to model logical, interpretable decision-making within deep neural architectures. Traditional deep learning excels at pattern recognition but often lacks transparent decision logic. By integrating attention mechanisms, symbolic logic modules, and cognitive constraints, neural models can emulate rational decision-making observed in humans. Researcher conducted regression and predictive analyses using benchmark datasets (DecisionQA, LogicalNLI) to quantify logical consistency and interpretability. Results shown that rational neural architectures outperform conventional models in both decision traceability and accuracy. This work contributed to the growing body of explainable AI by fusing cognitive rationality with computational precision. Keywords: Neural rationality, decision logic, deep neural networks, cognitive modeling, interpretable AI, attention mechanisms, symbolic reasoning, statistical analysis, regression modeling, rational decision-making
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Pandey, Palima, and Alok Kumar Rai. "Modeling Consequences of Brand Authenticity in Anthropomorphized AI-Assistants: A Human-Robot Interaction Perspective." PURUSHARTHA - A journal of Management, Ethics and Spirituality 17, no. 1 (2025): 116–35. https://doi.org/10.21844/16202117108.

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The emergence of anthropomorphized AI Assistants can be linked to the advanced convergence of machine learning and natural language processing algorithms that could mimic human brains. Conversational-AI has led users to expect a sense of authenticity in their anthropomorphized assistants, more so, in a social context; which creates newer avenues for brands to better connect with their consumers. The present study aimed to develop a consequential model of AI-authenticity while drawing inferences from a series of human-robot interaction based theories, viz. “Computers as Social Actors” (CASA); “Media Equation” (ME), “Stereotype Content Model” (SCM) and “Socio-Cognitive Computational Trust” (SCCT) theory. Partial-Least-Square based Structural-Equation-Modeling was performed to examine the hypothesized framework; while, bootstrapping technique was utilized to better assess the effect of mediation analysis. The predictive relevance of the developed model was evaluated based on cross-validated redundancy approach. The findings designated ‘Emotional Attachment’, ‘Customer Engagement’ and ‘Cognitive Trust’ as major consequences of brand authenticity; while ‘warmth’was accounted as a positive, but weak mediator in authenticity-cognitive trust relationship, due to probable effects of uncanny valley phenomenon. ‘Cognitive Trust’remained a significant predictor of ‘continuous usage intentions’and ‘word-of[1]mouth’ behaviour. The proposed AI-authenticity framework could aid underpinning effective customer retention and extension strategies.
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Pasupuleti, Murali Krishna. "Algorithmic Sentience: Ethical and Technological Challenges in AI Consciousness." International Journal of Academic and Industrial Research Innovations(IJAIRI) 05, no. 05 (2025): 368–81. https://doi.org/10.62311/nesx/rp05ai4.

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Abstract: The development of AI systems that exhibit features of sentience—self-awareness, emotional simulation, and subjective experience—poses profound ethical and technological challenges. This research explores "algorithmic sentience," a frontier concept in artificial intelligence that addresses the potential emergence of machine consciousness through neural simulation and cognitive architectures. Drawing on interdisciplinary sources in cognitive science, neuroinformatics, and machine learning, we propose a model to evaluate dimensions of artificial awareness. Using regression and predictive analytics, Researcher explored correlates between cognitive modularity, feedback loops, and emergent behaviors across datasets simulating affective responses. Researcher findings highlighted critical design principles, ethical fault lines, and scientific constraints in creating sentient-like AI systems, offering insights into governance and design for future conscious machines. Keywords: Algorithmic sentience, AI consciousness, ethical AI, artificial awareness, cognitive architectures, machine sentience, neural simulation, moral reasoning, statistical modeling, predictive analysis
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S. Meenakshi. "Artificial Intelligence for Dynamical Systems in Wireless Communications, Financial Markets, and Engineering: Modeling for the Future." Journal of Information Systems Engineering and Management 10, no. 22s (2025): 841–52. https://doi.org/10.52783/jisem.v10i22s.3628.

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Artificial Intelligence (AI) has found use in modeling and optimization dynamical systems over multiple domains including wireless communications, financial markets, and engineering. The focus of this research is on the use of the AI driven approaches for improving the predictive accuracy, decision making, and system optimization. The four AI algorithms that were analyzed for their effectiveness in handling complex, dynamic environments are deep learning, reinforcement learning, harmony search optimization, and fuzzy cognitive maps. To experiment, deep learning models were shown to improve spectrum allocation efficiency by 27.8%, reinforcement learning was illustrated to enhance financial risk prediction accuracy by 31.4%, harmony search optimization was found to reduce engineering system faults by 24.6% and fuzzy cognitive maps were shown to increase decision making reliability by 29.2%. It was confirmed that adaptive and computational efficient techniques have always been better suited than traditional ones to the AI approaches. Nevertheless, there were challenges like computational complexity and timing implementation. The outcome of this study highlights the necessity of hybrid AI models that combine several approaches for enhancing performance and adaptability. The future research should concentrate on improving the model interpretability and incorporate AI with newly emerging technologies such as quantum computing and edge computing to enhance dynamical system modeling. These findings highlight the importance of AI in largely transforming business decision-making and predictive modeling in various industries.
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Ismail Y and Vijaya Kumar Voleti. "A Review on Usage of Artificial Intelligence for Early Detection and Management of Alzheimer's Disease." Journal of Pharma Insights and Research 2, no. 5 (2024): 007–13. http://dx.doi.org/10.69613/06tz7453.

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Artificial Intelligence (AI) has emerged as a powerful tool in Alzheimer's disease (AD) research and clinical practice. This review discusses about the recent advances in AI applications for AD, focusing on neuroimaging analysis, biomarker discovery, cognitive assessment, and predictive modeling. AI techniques, particularly deep learning algorithms, have significantly improved the accuracy and efficiency of brain imaging interpretation, enabling earlier detection of AD-related structural and functional changes. In biomarker research, AI has accelerated the identification of novel blood-based and CSF markers, potentially leading to less invasive and more cost-effective diagnostic methods. AI-driven cognitive assessment tools, including computerized tests and speech analysis, offer more sensitive measures of cognitive decline. Additionally, AI-based predictive models integrating multiple data types show promise in personalized risk assessment and disease progression forecasting. Despite these advancements, challenges remain in data standardization, model interpretability, and ethical considerations. This review explains about the current state of AI in AD research, its potential impact on patient care, and areas requiring further investigation to fully realize the benefits of AI in combating Alzheimer's disease
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Mahniza, Melda, Resti Elma Sari, Puji Hujria Suci, Indra Saputra, and Elviza Yeni Putri. "AI-Driven Learning: Mediating and Moderating Dynamics in Self-Regulated Learning." Journal of Educational Science and Technology (EST) 10, no. 3 (2024): 229. https://doi.org/10.26858/est.v10i3.68254.

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The rapid integration of artificial intelligence (AI) in education has transformed how students learn, particularly in fostering self-regulated learning (SRL). However, understanding the mechanisms and conditions under which AI adoption influences SRL remains underexplored. This study investigates the roles of achievement goals, cognitive load, personalized learning, students' adaptability, and AI competence in shaping SRL within an AI-enhanced educational framework. The research employs Structural Equation Modeling (SEM) with the Partial Least Squares (PLS) approach to analyze direct, mediating, and moderating effects while accounting for demographic controls such as age, gender, internet access, and environment. The findings reveal a complex interplay of factors. Direct effect testing showed that five hypothesized relationships, including the influence of achievement goals, cognitive load, personalized learning, and students’ adaptability on SRL, were unsupported. Mediation analysis confirmed that AI adoption significantly mediates the effects of achievement goals, cognitive load, and personalized learning on SRL, emphasizing the role of technology acceptance in enhancing learning autonomy. Moderation analysis identified that AI competence strengthens the relationship between achievement goals and SRL but does not moderate other interactions, such as those involving AI adoption or cognitive load. These results underscore the nuanced dynamics between cognitive, technological, and motivational factors in AI-enhanced learning. The study contributes to the growing literature on AI-driven education by highlighting the pivotal role of mediating variables like AI adoption and the limited yet strategic influence of AI competence. Future research should explore broader contextual and pedagogical factors to optimize the integration of AI tools in fostering self-regulated learning
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Luo, Yun. "Revolutionizing education with AI: The adaptive cognitive enhancement model (ACEM) for personalized cognitive development." Applied and Computational Engineering 82, no. 1 (2024): 71–76. http://dx.doi.org/10.54254/2755-2721/82/20240929.

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Abstract. The integration of artificial intelligence (AI) into education has opened doors to personalized learning experiences. This paper introduces the Adaptive Cognitive Enhancement Model (ACEM), a cutting-edge AI-driven framework designed to personalize cognitive development for students. Leveraging advanced machine learning algorithms and quantitative analysis, ACEM adapts educational content and learning strategies to individual cognitive needs. The model encompasses five key components: cognitive profiling, adaptive learning paths, intelligent feedback, motivational strategies, and longitudinal tracking. Through quantitative analysis and mathematical modeling, the paper demonstrates how ACEM significantly enhances learning outcomes compared to traditional education models. The discussion section provides a detailed exploration of each model component, its architecture, and its role in optimizing personalized cognitive development. Furthermore, challenges such as data privacy, scalability, and model interpretability are examined, alongside potential solutions. The conclusion underscores the transformative potential of ACEM in revolutionizing education.
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Anil Kumar. "Neuro Symbolic AI in personalized mental health therapy: Bridging cognitive science and computational psychiatry." World Journal of Advanced Research and Reviews 19, no. 2 (2023): 1663–79. https://doi.org/10.30574/wjarr.2023.19.2.1516.

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Personalized mental health therapy has gained increasing attention as advancements in artificial intelligence (AI) enable tailored treatment strategies based on individual cognitive and emotional profiles. Neuro-symbolic AI, a hybrid approach combining symbolic reasoning and neural networks, offers a promising solution for bridging cognitive science and computational psychiatry. Unlike conventional AI models that rely solely on deep learning, neuro-symbolic AI integrates human-interpretable knowledge representations with data-driven learning, enhancing the adaptability and explainability of AI-driven mental health interventions. This study explores the role of neuro-symbolic AI in revolutionizing personalized mental health care by integrating cognitive theories, structured knowledge graphs, and deep learning-based predictive modeling. By leveraging structured symbolic reasoning alongside probabilistic inference, neuro-symbolic systems enhance diagnostic accuracy, facilitate adaptive therapy recommendations, and improve patient-clinician interactions. Applications include AI-assisted cognitive behavioral therapy (CBT), personalized mood stabilization strategies, and early detection of mental health disorders through multimodal data fusion from speech, facial expressions, and physiological biomarkers. Furthermore, we examine the advantages of neuro-symbolic AI in addressing key challenges in computational psychiatry, including model interpretability, causal reasoning in mental health diagnosis, and the integration of psychological theories into AI frameworks. A comparative analysis of neuro-symbolic AI versus purely neural-based models highlights its superior capacity for reasoning, transparency, and personalized therapeutic adaptation. Future directions focus on refining hybrid AI architectures, integrating real-time patient feedback for dynamic therapy adjustment, and addressing ethical concerns related to AI-driven mental health interventions.
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Mahanty, Prosant Kumar, and Anoop Sharma. "Enhancing Predictive Modeling with Human-Like Intelligence: A Deep Feature Synthesis Approach." Journal of Advances and Scholarly Researches in Allied Education 21, no. 5 (2024): 681–88. https://doi.org/10.29070/cjnvh988.

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In this work, we create the Data Science Machine, an automated tool for extracting predictive models from unprocessed data. We initially propose and build the Deep Feature Synthesis method for automatically creating features for relational datasets in order to accomplish this automation. The pursuit of Human-Like Intelligence (HLI) in AI systems, the creation of emotionally intelligent AI, and the possible convergence of XAI with cognitive sciences are all further explored in this study. The advancement of artificial intelligence (AI) towards Artificial General Intelligence (AGI) raises important questions about consciousness, ethics, and social consequences.
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Isparan, Shanthi, and Seow Ai-Na. "When AI intervene Clinical Decision-Making: The influence of Organisational Support, Cognitive Load, and Perceived Autonomy." Malaysia Journal of Invention and Innovation 4, no. 3 (2025): 33–39. https://doi.org/10.5281/zenodo.14854569.

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The integration of Artificial Intelligence (AI) in healthcare holds the potential to optimise clinical decision-making.<strong> </strong>However, the effectiveness of AI intervention in clinical decision-making can influence the ability of healthcare professionals to effectively process and apply AI-generated recommendations. This research examines the influence of organisational support (OS) on cognitive load (CL) and its impact on the effectiveness of AI-assisted clinical decision-making. The study further investigates the mediating role of cognitive load and explores the moderating effect of perceived autonomy (PA). Organisational Support Theory (OST), Cognitive Load Theory (CLT), and Self-Determination Theory (SDT) are used to support these dynamics. The targeted respondents are medical doctors in Malaysia, and data are analysed using Partial Least Squares Structural Equation Modeling (PLS-SEM). It is expected that the increased OS will reduce CL, leading to improved AI-assisted clinical decision-making, with PA strengthening this relationship. The findings offer actionable insights for healthcare institutions, suggesting strategies to strengthen AI implementation, streamline workflows, and enhance clinical decision-making.
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Lim, Chong-U., and D. Fox Harrell. "The Marginal: A Game for Modeling Players' Perceptions of Gradient Membership in Avatar Categories." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 11, no. 3 (2021): 49–55. http://dx.doi.org/10.1609/aiide.v11i3.12819.

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We encounter the results of category formation every day, from demographic categories like race and gender, to role-playing-game classes like "fighter" or "mage". Category membership is often not simply based on the possession of discrete properties but instead constructed from and reflect the highly nuanced relationships (gradience) between members and best-example individuals called "prototypes". In this paper, we present The Marginal, an artificial intelligence (AI)-driven game that (1) computationally models the cognitive categories that players develop when customizing videogame avatars and (2) generates challenges for players to use their perception of visual, textual, and numerical data to progress in a game created using these models. We use archetypal analysis, an AI clustering approach for identifying boundary points in data, to generate tasks in The Marginal for its gameplay. It shows how AI can be combined with games to model and evaluate cognitive categorization phenomena.
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Xia, Tiansheng, Xiaoqi Shen, and Linli Li. "Is AI Food a Gimmick or the Future Direction of Food Production?—Predicting Consumers’ Willingness to Buy AI Food Based on Cognitive Trust and Affective Trust." Foods 13, no. 18 (2024): 2983. http://dx.doi.org/10.3390/foods13182983.

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In recent years, artificial intelligence (AI) has been developing rapidly and has had a broad impact on the food industry, with food produced from AI-generated recipes already appearing to actually go on sale. However, people’s trust and willingness to purchase AI food are still unclear. This study builds an integrated theoretical model based on cognitive trust and affective trust, taking into account consumers’ quality value orientations, social norms, and perceived risks of AI food, with the aim of predicting and exploring consumers’ trust and acceptance of AI food. This study utilized the questionnaire method and 315 questionnaires were collected. The results of structural equation modeling (PLS-SEM) indicated that food quality orientation, subjective norms, perceived trust, and affective trust all had a significant positive effect on consumers’ purchase intentions. Perceived risk had a negative effect on affective trust and consequently on consumers’ purchase intention, but the effect on cognitive trust was not significant. The results also suggest that cognitive trust is the basis of affective trust and that consumer trust and acceptance of AI food can be enhanced by augmenting two antecedents of cognitive trust (food quality orientation and subjective norms). Possible practical implications and insights from the current findings are discussed.
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Boomisha, S. D.* Jemmy Christy H. "AI in Drug Discovery." International Journal of Pharmaceutical Sciences 3, no. 2 (2025): 1800–1810. https://doi.org/10.5281/zenodo.14907864.

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Artificial intelligence (AI) is the technology and science of creating intelligent machines by using algorithms which the machine adheres to in order to mimic human cognitive functions like learning and problem solving. Artificial intelligence (AI) is the term used to describe computer programs that simulate the mechanisms that support the intellect of humans, including as engagement, deep learning, reasoning, adaptation, and sensory comprehension. It aims to mimic human cognitive functions. This article examines the prospective applications of AI in drug discovery, emphasizing significant developments and their possible effects. Target identification is being expedited by AI-driven predictive modeling, which is also expediting the identification of prospective medication candidates. An expedient and economical substitute for conventional drug development is provided by AI's capacity to mine data for drug repurposing. Artificial Intelligence enhances patient recruiting and trial management in clinical trials, leading to better efficiency and results. The combination of AI with large data and omics technology is yielding new insights, and in silico testing is predicting the safety and effectiveness of pharmaceuticals. Collaborative platforms driven by AI are also accelerating research and promoting open innovation. This paper highlights the enormous influence artificial intelligence (AI) is expected to have on drug discovery, with the potential to produce novel and efficient treatments that would significantly improve global healthcare.
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Lushyn, Pavlo, and Yana Sukhenko. "Metacommunications and Artificial Intelligence: The ECPF Approach for International Management." Організаційна психологія Економічна психологія 35, no. 2 (2025): 161–73. https://doi.org/10.31108/2.2025.2.35.15.

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Introduction. The article addresses the development of metacommunicative competence among international management professionals amid rapid AI integration. Based on analysis of contemporary human-AI interaction research, it establishes theoretical foundations of metacommunication in cognitive heterogeneous environments and substantiates the eco-facilitative approach as a methodological basis for developing this competence in hybrid management contexts. Aim. The aim is to conceptualize metacommunication as a key mechanism for interacting with AI agents within interface-based reality, and to justify the application of ECPF approach for cultivating management competencies in international contexts and enabling interactions with agents of different nature. Methods. Interdisciplinary theoretical analysis (psychology, management, post-humanist philosophy, communication studies) combined with logical-semantic modeling, comparative analysis of classical and contemporary approaches to metacommunication, interpretive reconstruction to adapt eco-facilitative principles, conceptual modeling of managerial metacommunicative competence integrating scholarly source analysis, practice synthesis, AI interaction interpretation, and insights from educational experiments. Results. Human-AI interaction reveals the need for inter-agent sensitivity as capacity to coordinate hybrid dialogue. A metacommunicative competence model encompassing cognitive, communicative, and ethical components was developed, metacommunication as a meaning-making mechanism for responsible multi-agent management was established and tools for ECPF sessions and AI partnership simulations were proposed. Conclusions. Metacommunication as inter-agent sensitivity enables adaptive AI interaction through meaning-generation. ECPF approach provides ecologically balanced framework for facilitative leadership in hybrid organizations. The development creates methodological foundation for educational programs and next-generation management tools in human-AI environments.
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Lin, Tianqi, Jianyang Zhang, and Bin Xiong. "Effects of Technology Perceptions, Teacher Beliefs, and AI Literacy on AI Technology Adoption in Sustainable Mathematics Education." Sustainability 17, no. 8 (2025): 3698. https://doi.org/10.3390/su17083698.

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Artificial intelligence has significantly transformed educational practices across disciplines. This study investigated the cognitive–behavioral mechanisms underpinning mathematics teachers’ engagement with AI teaching tools through an extended technology acceptance model. Utilizing structural equation modeling with data from 500 mathematics educators, we delineated psychological pathways connecting perceptual variables to technology engagement and pedagogical outcomes. Results revealed that perceived usefulness functioned as the primary determinant of AI engagement, while perceived ease of use operated exclusively through sequential mediational pathways, challenging conventional technology acceptance paradigms. Domain-specific factors, such as teacher AI literacy and mathematics teaching beliefs, emerged as significant mediators that conditioned technology-related behavioral responses. The mediators in this study illustrated differential attitudinal mechanisms through which perceptual variables transformed into engagement behaviors. These findings extended technology acceptance theories in educational contexts by demonstrating how domain-specific cognitive structures modulated perception–behavior relationships in professional technology adoption in mathematics education.
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S., Chaitra, Pallavi S. Kumar, and Shyam R. "AI-Powered Education: Enhancing Teaching, Learning, and Administrative Efficiency." Journal of Research and Development 17, no. 1 (2025): 37–43. https://doi.org/10.5281/zenodo.14948612.

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<strong><em>Abstract:&nbsp; </em></strong> <em>In recent years, artificial intelligence (AI) has emerged as a transformative force in the education sector, reshaping traditional teaching and learning models and paving the way for enhanced educational outcomes. This paper examines the profound influence of AI on the education sector, analyzing its contributions to personalized learning, administrative efficiency, and cognitive skill development. Through a comprehensive review of ten influential studies, the paper presents a synthesis of findings on AI&rsquo;s role in adaptive learning systems, real-time feedback mechanisms, and student assessment. The methodology employed includes a quantitative analysis of AI-driven tools in classroom settings, supported by statistical modeling to measure improvement in student engagement and retention. Results indicate a significant positive impact on educational efficiency, cognitive engagement, and knowledge retention. The paper concludes by exploring potential advancements in AI for education, such as fully automated grading systems and AI-guided personalized career counseling, offering a roadmap for future research in this transformative field.</em> &nbsp;
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Hardini, Marviola, Hetilaniar Hetilaniar, Semaria Eva Elita Girsang, Souza Nurafrianto Windiartono Putra, and Ihsan Nuril Hikam. "Advancing Higher Education: Longitudinal Study on AI Integration and Its Impact on Learning." International Journal of Cyber and IT Service Management 5, no. 1 (2025): 23–30. https://doi.org/10.34306/ijcitsm.v5i1.185.

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The integration of Artificial Intelligence (AI) into higher education is reshaping the learning landscape. This longitudinal study explores AI impact on learning outcomes, student engagement, and the overall educational experience, focusing on five key variables: AI-enabled Personalized Learning (AIPL), Student Engagement Metrics, Cognitive Skill Development (CSD), Digital Literacy Advancement (DLA), and Educational Resource Optimization (ERO). Using Structural Equation Modeling (SEM) with SmartPLS, the study analyzes these variables to assess AI effectiveness as a pedagogical tool. By evaluating how AI influences student engagement and academic performance, this research highlights the potential of AI to enhance cognitive skills and digital literacy, optimize educational resources, and improve learning efficiency. The study spans multiple academic terms to track the evolving impact of AI, ensuring its long-term sustainability in modern education. The findings aim to inform future strategies for AI integration in higher education, contributing to the broader discourse on AI transformative role in education and providing actionable insights for educators and policymakers. By focusing on the core principles of AI-driven innovation and learning optimization, this research seeks to offer evidence-based recommendations to improve the quality and accessibility of education.
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Atamanchuk, Viktoriia, and Petro Atamanchuk. "Modelling the personage’s fictional consciousness in the play by Igor Kostetskyi “The twins will meet again”." Revista Amazonia Investiga 10, no. 45 (2021): 42–51. http://dx.doi.org/10.34069/ai/2021.45.09.4.

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The objective of the article is to outline the correlations between the usage of the elements of the absurdist aesthetics, different artistic paradoxes and methods of modelling the fictional consciousness of personage. The aim of research is to define internal and external dimensions of personage’s fictional consciousness construction with the help of the cognitive literary studies methodology. The methodology of cognitive literary criticism is the basis for the analysis of modelling principles, applied in the research of personage’s fictional consciousness in I. Kostetskyi’s play “The Twins Will Meet Again”. Thus, the study of the play is based on actualization of cognitive phenomena and establishing their correlations with forms of artistic reflection. The cognitive method is used to determine the theoretical foundations of the functioning of the character’s fictional consciousness in the dramatic work. The poetics of the absurd in a drama defines agglutinative forms of reflection of the personages’ fictional consciousness.
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29

Pelumi Oladokun. "Synthetic cognition in care pathways: Evaluating AI's influence on human-machine collaboration in medicine." International Journal of Science and Research Archive 7, no. 2 (2022): 777–97. https://doi.org/10.30574/ijsra.2022.7.2.0275.

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The advent of synthetic cognition—defined as the capacity of artificial intelligence (AI) systems to simulate human-like reasoning, learning, and decision-making—has begun to profoundly reshape medical care pathways. From diagnostics and prognosis to personalized treatment planning and robotic surgery, AI-driven tools are no longer peripheral but integral collaborators in clinical environments. This paper adopts a broad-to-narrow analytical framework to critically examine how synthetic cognition is influencing human-machine collaboration across the continuum of care. At a broader level, the integration of AI systems into healthcare infrastructures challenges conventional assumptions about medical authority, clinical expertise, and the epistemology of care. AI systems are increasingly capable of real-time data interpretation, pattern recognition, and predictive modeling, contributing to decision-making processes in ways that blur the lines between human judgment and machine output. As AI becomes more embedded in clinical routines, the need to recalibrate the roles and relationships between healthcare professionals and intelligent systems becomes urgent. Narrowing the focus, this study evaluates specific instances of human-AI interaction within care pathways—such as in radiology, oncology, and intensive care—highlighting both the benefits and ethical challenges. It explores the implications for clinical responsibility, trust-building, cognitive delegation, and shared accountability. Special attention is given to the tensions between algorithmic opacity and the need for transparent, explainable AI systems that support human oversight rather than replace it. By engaging with interdisciplinary perspectives from medical ethics, cognitive science, and systems theory, this paper offers a nuanced assessment of how synthetic cognition redefines collaboration in medicine. It ultimately argues for the development of hybrid governance frameworks that enable safe, effective, and ethically aligned human-machine partnerships in healthcare.
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Jang, Kyung Bae, Chang Hyun Baek, and Tae Ho Woo. "Risk Analysis of Nuclear Power Plant (NPP) Operations by Artificial Intelligence (AI) in Robot." Journal of Robotics and Control (JRC) 3, no. 2 (2022): 153–59. http://dx.doi.org/10.18196/jrc.v3i2.13984.

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The cognitive architecture is investigated for the management in the nuclear power plant (NPP) site in which artificial intelligence (AI) is incorporated. The normal operation and accident are modeled for the simulations incorporated with the robot intelligence algorithm, where random sampling plays a major role in the quantifications. The Accident Dynamics Simulator paired with the Information, Decision, and Action in a Crew context cognitive model (ADS-IDAC) and the Cognitive skill for plant operations are calculated for the study. Simulations show the ADS-IDAC modeling and simulation results of two peaks in 21st and 21.75th sequences. Otherwise, there are several peaks with one big peak in 13.25th sequences. The big peak is in the 25.75th sequence in Mental State, Circumstances, and Identity. The accident situation is related to actions through the cognitive systems. In the operation case, a variety of signals are shown in which the operations of the plant could show several kinds of actions to be done by the robot. The figure shows the procedure of nuclear cognitive architecture. A nuclear accident is investigated by the designed modeling in which the actions of robots are quantified by the artificial brain. The developed algorithm of this paper could be applied to the other kinds of complex industrial systems like airplane operations and safety systems, spacecraft systems, and so on.
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31

Ramaswamy, Mithilesh. "Cognitive Companion AI for Vulnerable Demographics: Fraud Prevention and Real-Time Protection for the Elderly." International Scientific Journal of Engineering and Management 03, no. 12 (2024): 1–6. https://doi.org/10.55041/isjem02132.

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Online fraud is a growing threat, particularly for demographics such as the elderly, who are more susceptible to manipulative tactics and phishing attempts. This paper introduces "Cognitive Companion AI," a novel Agentic AI system designed to provide real-time fraud prevention and education for vulnerable users. Leveraging advanced natural language processing (NLP), emotion-aware analytics, contextual threat recognition, and human-in-the-loop monitoring, the Cognitive Companion analyzes online interactions for fraudulent indicators and provides live alerts with actionable explanations. The framework includes behavioral modeling and adaptive learning algorithms to stay ahead of evolving fraud tactics. The inclusion of human oversight enhances trust and ensures a failsafe mechanism for particularly high-risk scenarios, such as safeguarding elderly users. This system aims to empower users with protection and awareness, significantly reducing the risk of fraud while maintaining transparency and accessibility. This paper explores the framework, use cases, and human-in-the-loop mechanisms and addresses challenges in its implementation. Keywords Cognitive Companion AI, online fraud prevention, real-time threat detection, vulnerable demographics, natural language processing, explainable AI, human-in-the-loop systems, digital guardianship.
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32

Orkin, Jeff, Tynan Smith, and Deb Roy. "Behavior Compilation for AI in Games." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 6, no. 1 (2010): 162–67. http://dx.doi.org/10.1609/aiide.v6i1.12406.

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In order to cooperate effectively with human players, characters need to infer the tasks players are pursuing and select contextually appropriate responses. This process of parsing a serial input stream of observations to infer a hierarchical task structure is much like the process of compiling source code. We draw an analogy between compiling source code and compiling behavior, and propose modeling the cognitive system of a character as a compiler, which tokenizes observations and infers a hierarchical task structure. An evaluation comparing automatically compiled behavior to human annotation demonstrates the potential for this approach to enable AI characters to understand the behavior and infer the tasks of human partners.
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Javed, Rana Tallal, Osama Nasir, Melania Borit, et al. "Get out of the BAG! Silos in AI Ethics Education: Unsupervised Topic Modeling Analysis of Global AI Curricula." Journal of Artificial Intelligence Research 73 (March 26, 2022): 933–65. http://dx.doi.org/10.1613/jair.1.13550.

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The domain of Artificial Intelligence (AI) ethics is not new, with discussions going back at least 40 years. Teaching the principles and requirements of ethical AI to students is considered an essential part of this domain, with an increasing number of technical AI courses taught at several higher-education institutions around the globe including content related to ethics. By using Latent Dirichlet Allocation (LDA), a generative probabilistic topic model, this study uncovers topics in teaching ethics in AI courses and their trends related to where the courses are taught, by whom, and at what level of cognitive complexity and specificity according to Bloom’s taxonomy. In this exploratory study based on unsupervised machine learning, we analyzed a total of 166 courses: 116 from North American universities, 11 from Asia, 36 from Europe, and 10 from other regions. Based on this analysis, we were able to synthesize a model of teaching approaches, which we call BAG (Build, Assess, and Govern), that combines specific cognitive levels, course content topics, and disciplines affiliated with the department(s) in charge of the course. We critically assess the implications of this teaching paradigm and provide suggestions about how to move away from these practices. We challenge teaching practitioners and program coordinators to reflect on their usual procedures so that they may expand their methodology beyond the confines of stereotypical thought and traditional biases regarding what disciplines should teach and how.&#x0D; This article appears in the AI &amp; Society track.
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34

Bailin, Alan. "Artificial Intelligence and Computer-Assisted Language Instruction: A Perspective." CALICO Journal 5, no. 3 (2013): 25–45. http://dx.doi.org/10.1558/cj.v5i3.25-45.

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The article attempts to outline the major components of CALI-AI (computer-assisted language instruction incorporating artificial intelligence techniques). The article begins by discussing briefly the central assumption on which CALI-AI work is based, that is, that human cognitive abilities can be reproduced by mechanical means. It then proceeds to examine the following components of CALI-AI: (1) natural language processing, problem solving, (3) language learning, and (4) modeling teacher behavior. The article concludes with a discussion of the ways in which language teachers can participate in the development of the field.
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35

Shiller, Alexandra V., and Oleg E. Petrunya. "Architectural Approach to Design of Emotional Intelligent Systems." Russian Journal of Philosophical Sciences 64, no. 1 (2021): 102–15. http://dx.doi.org/10.30727/0235-1188-2021-64-1-102-115.

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Over the past decades, due to the course towards digitalization of all areas of life, interest in modeling and creating intelligent systems has increased significantly. However, there are now a stagnation in the industry, a lack of attention to analog and bionic approaches as alternatives to digital, numerous speculations on “neuro” issues for commercial and other purposes, and an increase in social and environmental risks. The article provides an overview of the development of artificial intelligence (AI) conceptions toward increasing the human likeness of machines: from the key ideas of A. Turing and J. von Neumann, who initiated the digitalization of society, to discussions about the definition of AI and the emergence of conceptions of strong and weak AI. Special attention is paid to the approach of A. Sloman, to ideas about the architecture and design of complex artificial systems are considered, which make it possible to “emotionally” expand the idea of weak/strong AI. In the article's section on the necessity and possibility of incorporating emotions into the architecture of AI, the authors reveal the goals and methodological limitations for creating an emotional artificial agent. In addition, the article briefly presents the main principles of the authors' architectural approach to the creation of emotional intellectual systems on the example of the cognitive-affective model of architecture, which allow modeling the impact of emotions on the cognitive processes involved in decision-making processes. The described architectural approach to modeling intelligent systems can be used as a conceptual basis for discussing and formulating a strategy for the development of neurocomputing, philosophy of artificial intelligence, and experimental philosophy, for developing innovative research programs, formulating and solving theoretical and methodological problems.
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36

Raschka, Tamara, Meemansa Sood, Bruce Schultz, Aybuge Altay, Christian Ebeling, and Holger Fröhlich. "AI reveals insights into link between CD33 and cognitive impairment in Alzheimer’s Disease." PLOS Computational Biology 19, no. 2 (2023): e1009894. http://dx.doi.org/10.1371/journal.pcbi.1009894.

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Modeling biological mechanisms is a key for disease understanding and drug-target identification. However, formulating quantitative models in the field of Alzheimer’s Disease is challenged by a lack of detailed knowledge of relevant biochemical processes. Additionally, fitting differential equation systems usually requires time resolved data and the possibility to perform intervention experiments, which is difficult in neurological disorders. This work addresses these challenges by employing the recently published Variational Autoencoder Modular Bayesian Networks (VAMBN) method, which we here trained on combined clinical and patient level gene expression data while incorporating a disease focused knowledge graph. Our approach, called iVAMBN, resulted in a quantitative model that allowed us to simulate a down-expression of the putative drug target CD33, including potential impact on cognitive impairment and brain pathophysiology. Experimental validation demonstrated a high overlap of molecular mechanism predicted to be altered by CD33 perturbation with cell line data. Altogether, our modeling approach may help to select promising drug targets.
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37

Bendell, Rhyse, Jessica Williams, Stephen M. Fiore, and Florian Jentsch. "Supporting Social Interactions In Human-Ai Teams: Profiling Human Teammates From Sparse Data." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 65, no. 1 (2021): 665–69. http://dx.doi.org/10.1177/1071181321651354b.

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Artificial intelligence has been developed to perform all manner of tasks but has not gained capabilities to support social cognition. We suggest that teams comprised of both humans and artificially intelligent agents cannot achieve optimal team performance unless all teammates have the capacity to employ social-cognitive mechanisms. These form the foundation for generating inferences about their counterparts and enable execution of informed, appropriate behaviors. Social intelligence and its utilization are known to be vital components of human-human teaming processes due to their importance in guiding the recognition, interpretation, and use of the signals that humans naturally use to shape their exchanges. Although modern sensors and algorithms could allow AI to observe most social cues, signals, and other indicators, the approximation of human-to-human social interaction -based upon aggregation and modeling of such cues is currently beyond the capacity of potential AI teammates. Partially, this is because humans are notoriously variable. We describe an approach for measuring social-cognitive features to produce the raw information needed to create human agent profiles that can be operated upon by artificial intelligences.
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38

Li, Xianfeng. "Artificial intelligence in teacher education: Examining critical thinking and creativity through AI usage." Forum for Education Studies 3, no. 2 (2025): 2727. https://doi.org/10.59400/fes2727.

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The integration of Artificial Intelligence (AI) in teacher education has raised important questions about its impact on higher-order cognitive skills, particularly critical thinking and creativity. This study investigates the mediating role of critical thinking in the relationship between AI usage and creativity among pre-service teachers. Grounded in Cognitive Load Theory (CLT) and Dual-Process Theory, the research conceptualizes critical thinking as a second-order reflective construct comprising adaptive exploration (AE) and systematic validation (SV). Using a cross-sectional survey design, data were collected from 107 pre-service teachers at a teacher training institution in Southwest China. Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed to analyze the relationships among AI usage, critical thinking, and creativity. Results indicate that AI usage significantly enhances critical thinking (β = 0.560, p &lt; 0.001), which, in turn, has a strong positive effect on creativity (β = 0.707, p &lt; 0.001). Moreover, critical thinking serves as a partial mediator, amplifying the effect of AI usage on creativity (β = 0.397, p &lt; 0.001, VAF = 65.89%). These findings underscore the necessity of fostering AI-supported critical thinking skills in teacher education. As generative AI technologies become increasingly prevalent in digital learning environments, integrating adaptive exploration and systematic validation into pedagogical strategies is essential for cultivating both critical thinking and creativity. The study contributes to the discourse on digital learning innovations and teacher education by providing empirical evidence on AI’s role in enhancing cognitive development.
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Xiao, Ting, Sisi Yi, and Shamim Akhter. "AI-Supported Online Language Learning: Learners’ Self-Esteem, Cognitive-Emotion Regulation, Academic Enjoyment, and Language Success." International Review of Research in Open and Distributed Learning 25, no. 3 (2024): 77–96. http://dx.doi.org/10.19173/irrodl.v25i3.7666.

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The consideration of students’ emotional and psychological health is crucial to facilitate effective teaching and grading practices. This study set out to shed light on the interplay between self-esteem (S-E), cognitive-emotion regulation (CER), academic enjoyment (AE), and language success (LS) in artificial intelligence (AI)-supported online language learning. To this end, the foreign language learning self-esteem scale, the Cognitive Emotion Control Questionnaire, the foreign language enjoyment scale, and a researcher-made test were distributed to 389 English as a foreign language learners in China. Screening the data with confirmatory factor analysis and structural equation modeling, the effects of S-E, CER, AE, and LS were identified and quantified. These results highlighted the important function that online courses assisted by AI perform in enhancing students’ CER and AE. This implied that students who have cultivated a robust sense of self-efficacy are adept at effectively regulating their cognitive and affective processes in AI-supported language learning. Possible improvements in language education are discussed, as are the study’s broader implications.
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40

Reddy, Chandan K., and Parshin Shojaee. "Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 27 (2025): 28601–9. https://doi.org/10.1609/aaai.v39i27.35084.

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Scientific discovery is a complex cognitive process that has driven human knowledge and technological progress for centuries. While artificial intelligence (AI) has made significant advances in automating aspects of scientific reasoning, simulation, and experimentation, we still lack integrated AI systems capable of performing autonomous long-term scientific research and discovery. This paper examines the current state of AI for scientific discovery, highlighting recent progress in large language models and other AI techniques applied to scientific tasks. We then outline key challenges and promising research directions toward developing more comprehensive AI systems for scientific discovery, including the need for science-focused AI agents, improved benchmarks and evaluation metrics, multimodal scientific representations, and unified frameworks combining reasoning, theorem proving, and data-driven modeling. Addressing these challenges could lead to transformative AI tools to accelerate progress across disciplines towards scientific discovery.
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41

Bhanu, Prakash Manjappasetty Masagali. "The Role of AI and ML in Predicting Cognitive Decline and Dementia Progression." International Journal of Innovative Science and Research Technology (IJISRT) 10, no. 1 (2025): 844–54. https://doi.org/10.5281/zenodo.14737726.

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As the global population ages, the prevalence of cognitive decline and dementia, including Alzheimer's disease, continues to rise, impacting millions of individuals and placing a significant burden on healthcare systems. Early prediction and accurate monitoring of dementia progression are critical for timely intervention, personalized care, and slowing disease advancement. However, traditional diagnostic approaches face challenges, such as reliance on late-stage biomarkers, limited sensitivity of cognitive assessments, and inconsistencies in neuroimaging. This review explores how artificial intelligence (AI) and machine learning (ML) are transforming the field of dementia prediction, offering a paradigm shift toward earlier and more accurate assessments. This paper systematically examines recent advancements in AI and ML applications in predicting cognitive decline and tracking dementia progression. Key technologies discussed include deep learning for neuroimaging analysis, natural language processing (NLP) for speech and language pattern identification, and time-series analysis for continuous monitoring through wearable devices. The role of multimodal data integration, encompassing genetic, behavioral, clinical, and imaging data, is highlighted as a critical advancement that AI can facilitate, allowing for a comprehensive and personalized approach to risk prediction. Despite AI's potential, significant challenges remain, including data quality and diversity, ethical concerns in predictive diagnostics, and the "black-box" nature of many AI models that make clinical interpretability difficult. The review also discusses the regulatory and ethical landscape, underscoring the need for transparent, unbiased, and privacy-conscious AI applications in healthcare. Future directions are proposed, such as advancements in explainable AI (XAI), integration of precision medicine approaches, and the role of AI in supporting drug development and clinical trials. In conclusion, while AI and ML offer promising tools for enhancing dementia prediction and management, a collaborative approach involving researchers, clinicians, policymakers, and patients is essential to harness AI's potential responsibly and equitably. This paper calls for continued research, interdisciplinary partnerships, and regulatory guidance to ensure AI's ethical and effective integration into dementia care and management. Keywords: Artificial Intelligence (AI),Machine Learning (ML), Dementia Prediction, Cognitive Decline, Alzheimer&rsquo;s Disease, Early Diagnosis, Predictive Modeling, Neuroimaging, Genetic Biomarkers, Multi-modal Data Integration, Wearable Devices, Digital Biomarkers, Explainable AI (XAI), Federated Learning, Synthetic Data, Data Privacy in Healthcare, AI in Healthcare, Clinical Decision Support Systems (CDSS), Real-time Monitoring, Personalized Care, Neurodegenerative Diseases, Patient-Centered Care, AI Ethics, Bias in AI, Privacy-Preserving AI, Longitudinal Data Analysis, Cognitive Assessment, Patient Outcomes, Regulatory Standards for AI, AI in Dementia Care, Proactive Healthcare, Dementia Progression Monitoring, AI-Driven Healthcare Innovations, Clinical Applications of AI, Ethical AI in Healthcare.
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42

Researcher. "NEURO-AI CONVERGENCE: BRIDGING THE GAP BETWEEN NEUROSCIENCE AND ARTIFICIAL INTELLIGENCE." International Journal of Computer Engineering and Technology (IJCET) 15, no. 5 (2024): 938–46. https://doi.org/10.5281/zenodo.13960969.

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This comprehensive article explores the burgeoning field of neuro-AI convergence, examining the intricate relationship between neuroscience and artificial intelligence. The article traces the historical context of this interdisciplinary domain, highlighting key milestones that have led to the current synergy between brain science and machine learning. It delves into how neuroscientific insights have informed AI development, particularly in neural network architectures, learning mechanisms, and memory systems. Conversely, the article discusses the significant contributions of AI to neuroscience, including advanced computational modeling of brain functions, sophisticated data analysis techniques for neuroimaging, and cutting-edge brain-computer interfaces. The article also addresses the field's critical challenges, such as the persistent differences between biological and artificial neural networks, ethical considerations, and technological constraints. The article explores emerging research areas, potential applications in healthcare and cognitive enhancement, and the profound implications for our understanding of consciousness and cognition. By synthesizing current knowledge and pointing toward future directions, this review underscores the transformative potential of neuro-AI convergence in revolutionizing our understanding of the brain and the development of intelligent systems.
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43

Caplin, Andrew. "Data Engineering for Cognitive Economics." Journal of Economic Literature 63, no. 1 (2025): 164–96. https://doi.org/10.1257/jel.20241351.

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Cognitive economics studies imperfect information and decision-making mistakes. A central scientific challenge is that these can’t be identified in standard choice data. Overcoming this challenge calls for data engineering, in which new data forms are introduced to separately identify preferences, beliefs, and other model constructs. I present applications to traditional areas of economic research, such as wealth accumulation, earnings, and consumer spending. I also present less traditional applications to assessment of decision-making skills, and to human–AI interactions. Methods apply both to individual and to collective decisions. I make the case for broader application of data engineering beyond cognitive economics. It allows symbiotic advances in modeling and measurement. It cuts across existing boundaries between disciplines and styles of research. (JEL C45, C80, D15, D80, D91, G50, J24)
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44

Finn, Donal P. "A physical modeling assistant for the preliminary stages of finite element analysis." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 7, no. 4 (1993): 275–86. http://dx.doi.org/10.1017/s0890060400000366.

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This paper describes work in progress aimed at developing an interactive modeling tool that assists engineers with the task of physical modeling in finite element analysis. Physical modeling precedes the numerical simulation phase of finite element analysis and involves applying modeling idealizations to real world physical systems so that complex engineering problems are more amenable to numerical computation. In the paper, the nature of physical modeling is explored, a cognitive model of how engineers are thought to model complex problems is described and based on this model a knowledge-based modeling assistant is proposed. The AI approach taken is based on Chandrasekaran's propose-critique-modify design model adapted for the task of physical modeling. Within this framework, the AI paradigms of case-based reasoning, derivational analogy and model-based reasoning are exploited. By representing fundamental thermal modeling scenarios as cases, complex physical systems can be modeled in a piecewise fashion. Derivational analogy permits generative adaptation of retrieved cases by using model-based engineering traces thereby providing a basis for critiquing case solutions. An initial prototype is described which has been implemented for the domain of convection heat transfer analysis.
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Yang, Humin, Achyut Shankar, and Velliangiri S. "Artificial Intelligence-Enabled Interactive System Modeling for Teaching and Learning Based on Cognitive Web Services." International Journal of e-Collaboration 19, no. 2 (2023): 1–18. http://dx.doi.org/10.4018/ijec.316655.

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The future of modern education and web-based learning is inherently associated with the advancement in modern technologies and computing capacities of new smart machines, such as artificial intelligence (AI). AI is a high-performance computing environment powered by special processors that use cognitive computing for machine learning and data analytics. There are major challenges in online or web-based learning, such as flexibility, student support, classification of teaching, and learning activities. Hence, this paper proposes smart web-based interactive system modeling (SWISM)based on artificial intelligence for teaching and learning. The paper aimed to categorize learners according to their learning skills and discover how to enable learners with machine learning techniques to have appropriate, quality learning objects. Furthermore, local weight, linear regression, and linear regression methods have been introduced to predict the student learning performance in a cloud platform.
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46

Lawal, Oluwatoyin O., Nawari O. Nawari, and Omobolaji Lawal. "AI-Enabled Cognitive Predictive Maintenance of Urban Assets Using City Information Modeling—Systematic Review." Buildings 15, no. 5 (2025): 690. https://doi.org/10.3390/buildings15050690.

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Predictive maintenance of built assets often relies on scheduled routine practices that are disconnected from real-time stress assessment, degradation and defects. However, while Digital Twin (DT) technology within building and urban studies is maturing rapidly, its use in predictive maintenance is limited. Traditional preventive and reactive maintenance strategies that are more prevalent in facility management are not intuitive, not resource efficient, cannot prevent failure and either underserve the asset or are surplus to requirements. City Information Modeling (CIM) refers to a federation of BIM models in accordance with real-world geospatial references, and it can be deployed as an Urban Digital Twin (UDT) at city level, like BIM’s deployment at building level. This study presents a systematic review of 105 Scopus-indexed papers to establish current trends, gaps and opportunities for a cognitive predictive maintenance framework in the architecture, engineering, construction and operations (AECO) industry. A UDT framework consisting of the CIM of a section of the University of Florida campus is proposed to bridge the knowledge gap highlighted in the systematic review. The framework illustrates the potential for CNN-IoT integration to improve predictive maintenance through advance notifications. It also eliminates the use of centralized information archiving.
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47

Okronipa, Amanda Quist, and Lucy Ewuresi Eghan. "A theoretical investigation of students’ adoption of artificial intelligence chatbots using social cognitive theory and uses and gratification theory." Scientific Temper 16, no. 02 (2025): 3747–57. https://doi.org/10.58414/scientifictemper.2025.16.2.07.

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Prior studies widely acknowledge artificial intelligence as a facilitator of digital transformation in the educational sector. Yet, research on the determinants of AI chatbot adoption among students in low – and middle-income countries, particularly Ghana, is scarce. This study addresses this gap in the literature by investigating the motivational and behavioral antecedents that influence students’ use of AI chatbots in Ghana. Using Chat GPT – a type of AI chatbot, this research adopts the Uses and Gratification and social cognitive theories. Based on survey data from 249 study participants, this study employed the partial least square structural equation modeling approach. The study’s findings reveal that confidence, convenience, control, and enjoyment significantly affect students’ satisfaction. Also, satisfaction affects the use of AI chatbots among students in Ghana. Furthermore, some findings of our study diverge from previous research by revealing that identity does not significantly affect students’ satisfaction in the context of AI chatbot adoption.
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48

Gkintoni, Evgenia, Stephanos P. Vassilopoulos, Georgios Nikolaou, and Apostolos Vantarakis. "Neurotechnological Approaches to Cognitive Rehabilitation in Mild Cognitive Impairment: A Systematic Review of Neuromodulation, EEG, Virtual Reality, and Emerging AI Applications." Brain Sciences 15, no. 6 (2025): 582. https://doi.org/10.3390/brainsci15060582.

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Background/Objectives: Mild Cognitive Impairment (MCI) represents a clinical syndrome characterized by cognitive decline greater than expected for an individual’s age and education level but not severe enough to significantly interfere with daily activities, with variable trajectories that may remain stable, progress to dementia, or occasionally revert to normal cognition. This systematic review examines neurotechnological approaches to cognitive rehabilitation in MCI populations, including neuromodulation, electroencephalography (EEG), virtual reality (VR), cognitive training, physical exercise, and artificial intelligence (AI) applications. Methods: A systematic review following PRISMA guidelines was conducted on 34 empirical studies published between 2014 and 2024. Studies were identified through comprehensive database searches and included if they employed neurotechnological interventions targeting cognitive outcomes in individuals with MCI. Results: Evidence indicates promising outcomes across multiple intervention types. Neuromodulation techniques showed beneficial effects on memory and executive function. EEG analyses identified characteristic neurophysiological markers of MCI with potential for early detection and monitoring. Virtual reality enhanced assessment sensitivity and rehabilitation engagement through ecologically valid environments. Cognitive training demonstrated the most excellent efficacy with multi-domain, adaptive approaches. Physical exercise interventions yielded improvements through multiple neurobiological pathways. Emerging AI applications showed potential for personalized assessment and intervention through predictive modeling and adaptive algorithms. Conclusions: Neurotechnological approaches offer promising avenues for MCI rehabilitation, with the most substantial evidence for integrated interventions targeting multiple mechanisms. Neurophysiological monitoring provides valuable biomarkers for diagnosis and treatment response. Future research should focus on more extensive clinical trials, standardized protocols, and accessible implementation models to translate these technological advances into clinical practice.
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49

El Boustani, Sami, and Alain Destexhe. "A Master Equation Formalism for Macroscopic Modeling of Asynchronous Irregular Activity States." Neural Computation 21, no. 1 (2009): 46–100. http://dx.doi.org/10.1162/neco.2009.02-08-710.

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Many efforts have been devoted to modeling asynchronous irregular (AI) activity states, which resemble the complex activity states seen in the cerebral cortex of awake animals. Most of models have considered balanced networks of excitatory and inhibitory spiking neurons in which AI states are sustained through recurrent sparse connectivity, with or without external input. In this letter we propose a mesoscopic description of such AI states. Using master equation formalism, we derive a second-order mean-field set of ordinary differential equations describing the temporal evolution of randomly connected balanced networks. This formalism takes into account finite size effects and is applicable to any neuron model as long as its transfer function can be characterized. We compare the predictions of this approach with numerical simulations for different network configurations and parameter spaces. Considering the randomly connected network as a unit, this approach could be used to build large-scale networks of such connected units, with an aim to model activity states constrained by macroscopic measurements, such as voltage-sensitive dye imaging.
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Tarisayi, Kudzayi. "Preparing For AI's Transformational Potential: Rethinking Teacher Education In South Africa." International Education Trend Issues 2, no. 1 (2024): 31–40. http://dx.doi.org/10.56442/ieti.v2i1.364.

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As artificial intelligence transforms education, rethinking teacher training is crucial to leverage AI positively. This paper analyzes strategies for integrating AI competencies into South African teacher education. With over 420,000 teachers facing challenges, AI's personalized capabilities could assist, but require aligned reforms. Grounded in competency and social cognitive theories, recommendations include hands-on workshops modeling differentiation with AI, scaffolded online learning, teacher sharing networks, and expert mentoring using digital credentials. These approaches develop integration competencies through applied, collaborative learning, upholding humanistic aims. Focusing on competency applying AI for pedagogies like differentiation promotes teacher agency. Online modules build knowledge before practice. Communities enable observational learning. Expert coaching scaffolds contextualized skills mastery. This competency emphasis recognizes teacher knowledge within sustained professionalization policies. With teachers guiding integration, AI can enrich learning. The paper offers principles and evidence-based strategies for human-centred teacher education reform.
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