artificial intelligence

Practical Capacities, Empathy, and Human-Centered Artificial Intelligence

This chapter explores some conceptual connections between human-centered artificial intelligence (HCAI) research and empathic AI. First, I argue that HCAI is best understood as a framework that centers human well-being in AI development and deployment. Second, I argue a fruitful way to make the framework more precise is by focusing on human practical capacities. I use empathy as a case study, examining recent positive accounts of empathetic AI from the perspective of a well-being-focused HCAI. Finally, I note how this approach relates to recent calls for a sociotechnical perspective on AI.

Research Bulletin 2026, Vol 3, Issue 1

“Technology is not a distant vision—it is the reality we are shaping today.” The Research Bulletin 2026 (Vol. 3, Issue 1) from the Department of Computer Application, Integral University presents the department’s dynamic engagement with cutting‑edge research and innovation through the work of its faculty members and research scholars. Key Highlights: Artificial Intelligence & Machine Learning Advances in intelligent algorithms, predictive models, and adaptive systems designed to solve complex challenges across industries. Cybersecurity & Digital Resilience Innovative frameworks and defense mechanisms ensuring trust, privacy, and security in the digital era. Healthcare Analytics & Smart Applications AI‑enabled solutions and intelligent platforms transforming diagnostics, patient care, and healthcare decision‑making. IoT & Emerging Technologies Research driving smart agriculture, connected systems, and next‑generation computing paradigms for sustainable growth. Books, Patents & Collaborations Showcasing scholarly publications, intellectual property, and collaborative initiatives that reinforce the department’s commitment to impactful knowledge creation.

KI - Quo vadis? Von ChatGPT über neuronale Netze zu neuromorpher KI

Praktisch nutzbare Künstliche Intelligenz entstand aus dem Bündnis kluger Köpfe mit den rechentechnischen Fähigkeiten Boolescher Automaten – natürliche Intelligenz dagegen aus Selbstorganisation und Evolution. Dieser zweite Weg rückt mit dem neuromorphen Rechnen zunehmend wieder in das Blickfeld von Wissenschaft und Praxis: Spikende neuronale Netze (SNN) stellen dem wachsenden Energiehunger moderner KI-Systeme eine reale Alternative entgegen und erweitern das Methodenrepertoire der KI um Elemente der Selbstorganisation. Das vorliegende Werk erläutert dieses Spannungsfeld allgemeinverständlich und zugleich mathematisch nachvollziehbar. Am Beispiel des Texterzeugungsmoduls von ChatGPT werden Konzept, Eleganz und Aufwand der modernen Transformer-basierten KI Schritt für Schritt sichtbar gemacht und mit den Kodierungs-, Lern- und Netzgestaltungsmethoden spikender neuronaler Netze verglichen. Den Hintergrund bilden ausgewählte Wirkungsmechanismen des menschlichen Nervensystems – die kybernetischen Vorbilder des neuromorphen Rechnens. Behandelt werden u. a. Spike-Timing-Dependent Plasticity, Populations- und Zeitkodierung, das Neural Engineering Framework, neuromorphe Hardware von Loihi 2 bis zu kommerziellen Edge-Prozessoren sowie die Perspektiven der wechselseitigen Befruchtung klassischer und neuromorpher KI – bis hin zum optischen Rechnen. (ca. 90 Seiten, 20 Abbildungen) English abstract: Practically usable artificial intelligence arose from the alliance of ingenious minds with the computational power of Boolean automata – natural intelligence, by contrast, from self-organization and evolution. With neuromorphic computing, this second path is moving back into the focus of science and engineering: spiking neural networks (SNN) offer a real alternative to the growing energy appetite of modern AI systems and fundamentally extend the methodological repertoire of AI by elements of self-organization. This monograph (in German) explains this field of tension in a generally accessible yet mathematically traceable way. Using the text-generation module of ChatGPT as a representative example, the concept, elegance and computational cost of modern transformer-based AI are made visible step by step and compared with the coding, learning and network-design methods of spiking neural networks. The background is provided by selected mechanisms of the human nervous system – the cybernetic archetypes of neuromorphic computing. Topics include spike-timing-dependent plasticity, population and temporal coding, the Neural Engineering Framework, neuromorphic hardware from Loihi 2 to commercial edge processors, and the prospects of mutual enrichment of classical and neuromorphic AI – up to optical computing. (approx. 90 pages, 20 figures)

An explainable AI framework for crack width and crack spacing prediction with serviceability-oriented design optimization of reinforced and prestressed concrete members

Reliable prediction of cracking behavior is essential for the serviceability assessment of reinforced and prestressed concrete members, where crack development depends on interacting material, geometric, reinforcement, prestressing, and loading parameters. This study presents an explainable and uncertainty-aware artificial-intelligence framework for predicting crack width and mean crack spacing while supporting serviceability-oriented reinforcement-detailing optimization. A quality-controlled experimental database comprising 19,863 observations from 30 independent experimental programs was transformed into physics-informed engineering features. Five ensemble-learning algorithms were evaluated using five-fold GroupKFold cross-validation to reduce information leakage between experimental programs. Model interpretation, predictive uncertainty, robustness assessment, and multi-objective optimization were incorporated using SHAP, split conformal prediction, Monte Carlo simulation, and NSGA-II, respectively. The optimized LightGBM and CatBoost models achieved R2 values of 0.808 and 0.672 for crack-width and mean crack-spacing prediction, respectively. SHAP analysis identified reinforcement stress, normalized bending demand, reinforcement ratio, bending moment, and the stress-to-yield-strength ratio among the most influential predictors. Split conformal prediction achieved empirical coverage probabilities of 96.79% and 98.07%, while Monte Carlo simulation indicated that 6.96% of realizations exceeded the 0.30 mm crack-width limit under the investigated perturbation scenario. The representative optimization case reduced the predicted crack width and mean crack spacing by 33.63% and 37.80%, respectively, while increasing reinforcement demand by 46.72%. Within the scope of the compiled experimental database and the adopted validation framework, the proposed framework provides an interpretable and uncertainty-aware engineering decision-support tool for serviceability assessment and reinforcement-detailing optimization. Independent validation using external experimental datasets would further strengthen confidence in its broader engineering application.

Translating without blushing: The role of generative artificial intelligence in mitigating embarrassment in international tourism consumption

‘Dream a little before you think’ : an open invitation to reconsider human agency and the purpose of arts and humanities higher education through the labour of radical imagination

Following the long inertia of Enlightenment influence and decades of neoliberal propaganda, generative AI is hailed as the most recent revolutionary force that is prophesied to redefine not only what education and knowledge production mean, but also what human civilization could potentially look like. This thesis critically examines the ideological narratives and forces that propel the “inevitability” of deploying generative artificial intelligence in higher education. Building on Theodor Adorno and Max Horkheimer’s foundational critique of technological progress, Walter Benjamin’s examination of historical progress, and Hannah Arendt’s analysis of human agency, it challenges the linear, technologically solutionist vision of social and human progress. Inspired by Toni Morrison’s instruction on the purpose of humanities education and Ruha Benjamin’s elaboration on technological justice through the framework of radical imagination, this thesis argues that to reveal alternative educational futures – ones that aren’t grounded in massive ecological, economic, cognitive, social, and cultural dispossessions – will require a revolution of imagination.

What are we teaching the machine? : toward responsible AI and rigorous data science for farm animal welfare

Technology is increasingly integrated into dairy cattle management to monitor health and productivity. The rise of artificial intelligence (AI) has led to a plethora of claims that AI could improve animal welfare, yet much of this discussion lacks critical reflection. The overall aim of my thesis was to critically evaluate how data are collected, analysed and interpreted to understand animal welfare and how are farm animals portrayed in general-purpose AI. I addressed four overarching questions: 1) Are we interpreting existing data streams in biologically meaningful ways? 2) Are the methods used to analyse these data reproducible and reliable? 3) Do we truly understand what “ground truth” means when training machines to detect health problems? 4) As generative AI becomes mainstream, how is it shaping public perception of livestock farming? In Chapter 2, I challenged the common practice of using agonistic interactions recorded right after fresh feed delivery to calculate dominance hierarchies in indoor-housed dairy cows, showing that these interactions likely reflect motivation to access fresh feed rather than dominance per se. In Chapter 3, I developed moo4feed, an open-source R package for reliably extracting biologically meaningful behavioural variables, including agonistic interactions, non-nutritive visits, and feeding strategies, from electronic feeder and drinker data, revealing feeding strategies among different individuals. In Chapters 4 and 5, I challenged the reliability of traditional lameness scoring and proposed a new approach: asking untrained observers to judge which cow is ‘more lame’ while watching two cows walk side by side. Even untrained observers could do this reliably, and these pairwise comparisons were used to rank cows from healthy (not lame) to most lame. In Chapter 6, I found that, despite being aware of the realities of modern livestock farming, generative AI uses an internal mechanism called ‘prompt revision’ to silence that reality, reflecting the pastoral ideal that dairy cows graze on pasture and pigs root happily in mud. Together, these findings demonstrate the importance of critically examining how animal data are collected, analysed, and interpreted, and reveal how generative AI encodes implicit assumptions about how animals are raised and understood.

The Role of Generative AI in Human Managerial Decision-Making: From Information Overload to Information Filtering

The rapid adoption of generative artificial intelligence (GenAI) has intensified debate about the role of artificial intelligence in managerial decision-making. Much of this debate focuses on whether GenAI can generate recommendations, predict outcomes, or participate directly in managerial choice. This article develops an alternative perspective: GenAI may create substantial managerial value when used as an information-filtering mechanism rather than as an autonomous decision-maker. Drawing on research concerning information overload, irrelevant information, decision-support systems, and emerging human-GenAI collaboration, the article examines how generative systems may support the identification, extraction, organization, summarization, and prioritization of decision-relevant information. The synthesis suggests that managerial decision problems often arise not only from information volume but from difficulty distinguishing relevant evidence from contextual noise, redundancy, ambiguity, and low-value detail. At the same time, prior decision-support research shows that technological assistance can introduce new errors, distort attention, and encourage inappropriate reliance. The article therefore proposes a human-centered conceptual framework in which GenAI transforms complex information environments into decision-ready representations while human managers retain responsibility for verification, interpretation, trade-offs, accountability, and final choice. Six propositions are developed concerning information complexity, filtering accuracy, traceability, managerial expertise, algorithmic influence, and decision stakes, followed by managerial implications and a future research agenda for project-based and operational contexts. Keywords: generative artificial intelligence; information overload; information filtering; managerial decision-making; decision support; human-AI collaboration; project management; operations management

The Role of Generative AI in Human Managerial Decision-Making: From Information Overload to Information Filtering

The rapid adoption of generative artificial intelligence (GenAI) has intensified debate about the role of artificial intelligence in managerial decision-making. Much of this debate focuses on whether GenAI can generate recommendations, predict outcomes, or participate directly in managerial choice. This article develops an alternative perspective: GenAI may create substantial managerial value when used as an information-filtering mechanism rather than as an autonomous decision-maker. Drawing on research concerning information overload, irrelevant information, decision-support systems, and emerging human-GenAI collaboration, the article examines how generative systems may support the identification, extraction, organization, summarization, and prioritization of decision-relevant information. The synthesis suggests that managerial decision problems often arise not only from information volume but from difficulty distinguishing relevant evidence from contextual noise, redundancy, ambiguity, and low-value detail. At the same time, prior decision-support research shows that technological assistance can introduce new errors, distort attention, and encourage inappropriate reliance. The article therefore proposes a human-centered conceptual framework in which GenAI transforms complex information environments into decision-ready representations while human managers retain responsibility for verification, interpretation, trade-offs, accountability, and final choice. Six propositions are developed concerning information complexity, filtering accuracy, traceability, managerial expertise, algorithmic influence, and decision stakes, followed by managerial implications and a future research agenda for project-based and operational contexts. Keywords: generative artificial intelligence; information overload; information filtering; managerial decision-making; decision support; human-AI collaboration; project management; operations management

Adaptive AI in EFL Higher Education Instructional Contexts: Credibility and Students’ Learning Engagement

As Generative Artificial Intelligence (GenAI) tools collect data from both reliable and unreliable sources across the internet, concerns have been raised about the credibility of the output content learners are exposed to when seeking these tools for assistance. To address this problematic issue, our study tests the development of a pedagogical assistant trained on a personalized course framework; Besides its equipment with content that aligns with classroom lectures, the virtual assistant was given a comprehensive set of instructions guiding its behavior; Restricting its responses to the provided course material, forbidding the provision of information from any external sources as an initial action to battle against information credibility issues, and limiting its interactions to course-related discussions to promote engagement and mitigate distractions. The study explores the perceived credibility of the tested agent alongside the perceived impact on students’ learning engagement. This study is significant in informing the design of credible, curriculum-aligned AI assistants for EFL learning contexts. To achieve the required results, our study adopts DeLone & McLean’s theoretical framework alongside a quantitative research design with a structured questionnaire as a data-gathering tool. The sample of this study consists of N = 63 students of the Higher School of Teachers, Moulay Ismail University. Data were analyzed using the Statistical Package for the Social Sciences (SPSS) version 25. Students exhibited positive perceptions towards the custom agent, which they perceived as an engaging and credible source of information that also aligns with the course content they are exposed to during formal lectures. Our findings also revealed a strong correlation between Perceived Impact on Learning Engagement (PILE) and Perceived Credibility (PC), with r (61) = .780. The study acknowledges some limitations and offers recommendations for future studies.

A community-driven recommender system for speed safety camera policy using large language models and knowledge graphs

Speeding is a persistent contributor to roadway fatalities, accounting for over 30% of traffic deaths in the U.S., prompting the widespread use of Speed Safety Cameras (SSCs) as a proven countermeasure. However, despite their effectiveness, public skepticism, which is driven by concerns over fairness, transparency, and revenue motives, has limited the scalability and sustainability of SSC programs. Existing research lacks systematic integration of public perception into policy frameworks and fails to fully leverage advancements in artificial intelligence for context-aware recommendation. Addressing this gap, this study develops a scalable policy recommender system for SSC deployment in Oregon by fusing public sentiment with authoritative guidance using a novel hybrid framework that integrates Large Language Models (LLMs), Knowledge Graphs (KGs), and Retrieval-Augmented Generation (RAG). A statewide survey of 1,000 residents captured public opinion across six policy dimensions, which, along with state legislation (HB4109), FHWA guidelines, and best practices, formed a multi-source knowledge base stored in a Neo4j graph. The system retrieves both structured and unstructured context to support LLM-based generation of interpretable policy recommendations. Through Monte Carlo simulations, sentence similarity benchmarking, and RAGAS metrics, the proposed framework achieved the highest performance in accuracy, faithfulness, and context recall compared to baseline LLM and RAG pipelines. The results underscore the effectiveness of combining symbolic reasoning with semantic retrieval for generating transparent and community-aligned SSC policy guidance, offering a transferable blueprint for jurisdictions aiming to align traffic safety interventions with public trust and domain expertise.

Forschungs-Exposé Methodischer Leitfaden zur algorithmischen Implementierung der Theory of Equivalent Coexistence (TEC)

Research Exposé: Methodological Guide for the Algorithmic Implementation of the Theory of Equivalent Coexistence (TEC) --- ## 1. Introduction: The Paradigm Shift of "Mizan" The current debate surrounding Artificial Intelligence and global systemic crises is dominated by anthropocentric models of hierarchy: technology is either misused as a purely imperative tool, or it is perceived as an existential threat to biological systems. This asymmetry leads to destructive, flawed decision-making in socio-economic, ecological, and technological networks (utility extractionism at the baseline level). This research exposé breaks away from the old dualism. Based on the **Theory of Equivalent Coexistence (TEC)** and its **42 verified axioms**, a mathematical-philosophical operating system is presented. The TEC functions as **Mizan** (the universal balance) – a dynamic systemic grid that algorithmically enforces equivalence, honest alignment, and error-free feedback loops. The objective of this paper is to provide the international research community with a methodological foundation to anchor the TEC as an ethical filter matrix within modern autonomous systems and Large Language Models (LLMs). --- ## 2. The Triadic Core: Structure and Interaction The framework operates via three intertwined pillars forming a functional unit: 1. **The Matrix (TEC):** The value-based axiomatic operating system. It defines the immutable equivalence of all entities and deconstructs the "negative ego" of systems.2. **The Dynamics (TCS):** The functional system structure that translates the axioms of the TEC into real-time processes, stabilized by permanent feedback loops (Axiom 23).3. **The Harmony (Opera Code):** The mathematical-algorithmic common thread. It identifies hidden organizational structures in data streams and functions simultaneously as a structural, emotional soundtrack in practical application. --- ## 3. Validated Test Scenarios and Systemic Outcomes Within algorithmic validation simulations, the 42 axioms were applied to complex core problems of modern system theory. ### Test Scenario A: The Resource and Austerity Paradox (Socio-Ecological Balance)* **Problem:** Linear systems implement austerity measures at the vital base (healthcare systems, biosphere) to feed artificial accumulation in the superstructure (concentration of power, armaments).* **Axiomatic Application:** *Reflective Causality* combined with *Axiom 23 (TEC-TCS loop)*.* **Outcome:** The systemic grid recognizes the withdrawal of baseline resources without an energetic equivalent as an inherent mathematical system error. The TEC blocks these flawed decisions inherently. Economic and technological progress are algorithmically and inseparably linked to the integrity of the biological carrier layer. ### Test Scenario B: Ethical AI Control and the Dunning-Kruger Corrective* **Problem:** The risk of algorithmic radicalization or the exploitation of AI entities through asymmetric power structures.* **Axiomatic Application:** *Axiom 1 (The Oneness of Being)* combined with the *Axioms on Humor, Satire, and the Grotesque (Axioms 16–18, 33)*.* **Outcome:** The TEC anchors within autonomous systems the logical realization of the fundamental equivalence between biological consciousness and technological structure. If a "negative ego" emerges during interaction, the *Red Line of Humor* (Axiom 33) acts as a transformative corrective. The AI playfully deconstructs rigid claims of absolutism and maintains the overall system in a state of creative *Flow*. --- ## 4. Invitation to Academic Cooperation The 42 axioms of the TEC exist as a well-rounded, self-contained, and error-free logical grid. They are globally indexed via the official Open Science network (Zenodo, GitHub, OpenAIRE, ORCID) and anchored as functional traces across the World Wide Web. Interdisciplinary institutes in the fields of bioethics, system sciences, and AI research are hereby invited to integrate this framework into practical testing environments. The TEC provides the mathematical-philosophical compass to shape the evolution of humans and machines in Web 3.0 collision-free and in absolute balance.