This article examines the urgency of reconstructing the Islamic Religious Education (IRE/PAI) curriculum in facing the challenges of Society 5.0, which is laden with technological disruption, particularly the emergence of Artificial Intelligence (AI). Using a qualitative approach based on library research, this study systematically analyzes various scientific literature, education policy documents, and contemporary theories concerning technology integration in religious education. The findings indicate that the conventional Islamic religious education curriculum has not been able to adequately respond to the challenges of the destructive 5.0 era, making reconstruction necessary across three main dimensions: (1) integration of artificial intelligence as a pedagogical tool without sacrificing spiritual values; (2) strengthening Islamic ethical values as a moral foundation amid the flow of digitalization; and (3) developing sustainable competencies relevant to twenty-first-century needs. This research recommends an integrative PAI curriculum model that combines digital-spiritual literacy, contextual Islamic ethics, and adaptive competencies as a strategic response to contemporary challenges.
Sunhaji, Yanti Nurdiyanti, Muhamad Muzaki· International Journal of Inn...· 0 citations
In 2023, the arrival of Chat GPT 3o heralded many viral conversations about what it means to interact with artificial intelligence (e.g., Roose, 2023). People read about individuals having extended conversations with chatbots, often leading to complex consequences that raise deeper questions. What do we mean by “empathy” in these spaces? What are the ethical consequences for building or using technology for emotional support? There have been cases of large language models giving better and more empathetic medical advice than trained medical workers (Ayers et al., 2023; see later replication by Ovsyannikova et al., 2025). There have also been popular write-ups about people developing relationships with Artificial Intelligence (AI) platforms explicitly positioned as romantic partners (Patel, 2024). In this introductory chapter, we will highlight core themes that unite the chapters throughout the volume, highlighting connections across social sciences, humanities, and engineering and computers science to juxtapose perspectives and illustrate connections and ongoing controversies.
C. Daryl Cameron, Anat Perry· Cambridge University Press e...· 0 citations
Most definitions of empathy stress its interpersonal nature: Empathy requires a human sender and a human receiver, where all parties involved have the theoretical capacity to accurately understand each other’s minds. Because empathy is inherently interpersonal, some scholars have questioned whether empathy between humans and artificial intelligence as it currently exists (e.g., large language models) is “real.” However, the requirement for two minds, each with the theoretical capacity to fully understand the thoughts and emotions of the other, is also not clearly met in other domains where empathy is discussed, such as empathy between humans and non-human animals or empathy with nature. This chapter critically examines these assumptions about what is needed for empathy to occur, offering several possible sets of empathy requirements that researchers might engage with, and outlining potential challenges researchers might face if they adopt any particular set of requirements in their work. Ultimately, we pose the question of whether two minds and/or the ability for minds to understand each other are truly necessary for empathy, or whether all that is required for empathy to occur is an entity’s perception of giving or receiving it.
Sean M. Laurent, Iris Sooyun Chung· Cambridge University Press e...· 0 citations
With artificial intelligence (AI) becoming increasingly integrated into healthcare systems, questions have emerged regarding how patients’ and clinicians’ views of AI in these high-stakes environments are shaped by its perceived moral competence and adherence to ethical principles, including respect for personal autonomy. To examine this question, this chapter focuses on how people evaluate medical decisions made by AI versus humans and the role that ascriptions of various moral and compassionate traits play in these judgments. We discuss current applications of AI in healthcare settings, existing empirical evidence on public perceptions of AI-assisted versus human decision-making, and offer speculative explanations for the widely documented asymmetrical preference for human over AI decision-makers in patient medication, triage, and life support decisions. As AI evolves, understanding its impact on ethical choices in healthcare is vital for balancing technological advancement with compassionate care.
Michael Laakasuo, Kathryn Francis, Marianna Drosinou et al.· Cambridge University Press e...· 0 citations
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Who – or what – people engage for supportive conversations is rapidly evolving as technology becomes more sophisticated and users adapt their expectations and perceptions of communication. Empathy is central to understanding and responding to emotions along with communicating sensitive supportive messages. Questions surrounding the role of empathy when communicating with artificial intelligence, however, have yielded mixed and interesting conclusions. This chapter discusses perspectives on empathy, supportive communication, and the influence of machine agents in the process of communicating sensitive, empathic messages. We consider whether machine agents can simulate empathy and convey sensitive support or merely produce reactive content that might simply appear empathic. This chapter offers insights into the relationship between empathy and supportive interactions with artificial intelligence.
Austin Beattie, Andrew C. High· Cambridge University Press e...· 0 citations
A question that arises when contemplating artificial intelligence and empathy is whether it would matter to the empathy recipient if AI genuinely feels or cares. This is a seemingly novel issue, born of the capacity of computers to simulate human empathy. I will suggest, however, that questions concerning “artificial empathy” should not be limited to AI chatbots, as such forms of empathy that are void of feeling are widespread in human relationships as well. This chapter offers a short history of artificial empathy and how it became widely accepted, well before AI, beginning with the operationalization of empathy in mid-twentieth-century clinical psychology. Evaluations of AI’s empathetic abilities are often based on comparisons to human or “real” empathy – a romanticized form of empathy that is, sadly, less common than we appreciate. This chapter offers an alternative perspective that might prove valuable, comparing AI’s “artificial empathy” to human “artificial empathy.”
Shai Satran· Cambridge University Press e...· 0 citations
Transformers are increasingly reshaping artificial-intelligence-based Earth system prediction, yet their suitability for distributed groundwater surrogate modeling remains poorly understood. Groundwater dynamics combine strong temporal persistence with spatial interactions governed by heterogeneous hydrogeological conditions. These characteristics make attention-based architectures potentially well suited to representing the long-range spatiotemporal dependencies of groundwater systems, rather than merely providing an alternative to recurrent models. Here, we systematically evaluated a groundwater-adapted PredFormer as a surrogate for regional ParFlow-CLM simulations. The adapted model combined temporal-first factorized attention with pressure-residual prediction and blockwise autoregressive rollout. We assessed its long-horizon prediction behavior over 720 h, the contributions of prescribed P−ET (PME) forcing and static attributes, alternative static-fusion strategies, its performance relative to PredRNN, and precipitation-regime-specific data augmentation generated by perturbing precipitation forcing. Over the horizon, the adapted PredFormer maintained decimeter-scale median water table depth (WTD) errors, although error magnitude varied among WTD regimes. Larger upper-tail spatial errors were associated with precipitation amount and variability, whereas temporal fluctuations in the error trajectories were more closely associated with abrupt wet–dry transitions. Adding prescribed PME produced the largest and most consistent reduction in prediction error, although the benefits of dynamic and static inputs varied systematically with WTD. Post-temporal–spatial cross-attention performed best among the static-fusion strategies. PredRNN was more accurate initially, but PredFormer performed better at longer lead times and in most segments. Data augmentation produced modest overall changes but clear regime-dependent benefits when the added samples matched the precipitation conditions encountered during prediction. These findings show that the advantages of Transformer-based groundwater surrogates emerge primarily over extended autoregressive horizons and depend on groundwater state, input composition, information-fusion strategy, and training-sample coverage. The results provide process-informed guidance for developing computationally efficient groundwater surrogates for regional prediction and water-resources management, while also highlighting their longer-term potential to provide AI-enabled representations of groundwater dynamics within Earth system models.
hui huang, Aoqi Sun, Hoang Tran et al.· 0 citations
Human-in-the-loop (HITL) architectures retain human verification in artificial intelligence (AI)-assisted decisions to preserve safety. Human presence does not guarantee AI-independent judgment. Multiple substantive checks can share one AI-dependent information pathway, and a Reciprocal Validation Spiral can reinforce that dependency when human approval becomes evidence for further AI trust and delegation. This paper treats independence as graded and defines a Hole in the Loop at a decision or verification point when formal HITL remains intact and zero substantive verification pathways meet a domain-specific minimum criterion for effective independence. Before such a threshold is operationalized, a limiting case is identifiable: every substantive check is downstream of the same AI output or AI-conditioned judgment, and no checker forms a pre-AI judgment or consults AI-independent evidence. The Spiral can generate, deepen, or stabilize a Hole, while a Hole can also exist without it. When the common AI-dependent pathway is systematically wrong, no effectively independent route remains to register the discrepancy before downstream action. The error can pass with multiple approvals and a completed audit trail while agreement rises, override rates fall, and procedural compliance appears complete. Correlated agreement can therefore be recorded as independent confirmation. The paper argues that calling oversight of a high-risk system meaningful carries an ethical obligation to preserve effectively independent verifiability at decision points that matter and to make its dependency structure auditable. The relevant unit of AI safety therefore moves from human presence to the preservation and observability of effectively independent verification pathways.
K. Yamada· Zenodo (CERN European Organi...· 0 citations
AbstractThis repository contains a modernized exposition and formal mathematical reconstruction of Burkhard Heim’s Syntrometrische-Maximentelezentrik, one of the principal theoretical works within Heim’s broader mathematical framework.The work develops a highly specialized formal system combining logic, semantics, and structural mathematics. This exposition reconstructs Heim’s syntrometric logic using contemporary mathematical language, notation, and formal methods, while seeking to preserve the essential structure and conceptual intent of the original framework. Where appropriate, Heim’s terminology and constructions are clarified, formalized, and related to established concepts in mathematical logic, category theory, mathematics, and theoretical computer science.A central objective of the reconstruction is to make Heim’s formal system intelligible and analyzable within a contemporary mathematical setting without presenting the modernization as part of Heim’s original formulation. The exposition therefore distinguishes between historical source material, formal reconstruction, and newly developed mathematical interpretations.The book also investigates potential applications of the reconstructed framework to cognitive modeling and artificial intelligence. These applications are presented explicitly as formal extensions and computational interpretations of the reconstructed logical system, rather than as claims about Heim’s original work.The repository is intended as a scholarly resource for researchers interested in formal logic, modal logic, mathematical foundations, category theory, information theory, systems theory, theoretical computer science, cognitive science, philosophy of science, and Burkhard Heim’s theoretical work.Source MaterialThis work was developed from archival manuscripts and publicly available source material associated with the Heim Theory Archive. Its objective is to provide a rigorous contemporary exposition and formal reconstruction of Heim’s syntrometric logic while maintaining fidelity to the mathematical structure and conceptual intent of the original work.The reconstruction does not assume that modern terminology or mathematical formalisms were explicitly present in Heim’s original formulation. Instead, such formalisms are introduced where they provide a precise contemporary representation, clarification, or extension of structures found in the source material.Copyright and AttributionThis work is an independent scholarly exposition and formal reconstruction prepared for educational, research, and archival purposes. It is not an official or authorized publication of Burkhard Heim’s works.Copyright in the original German works of Burkhard Heim remains with the respective rights holders, including Burkhard Heim’s estate and/or the original publishers, where applicable.This repository does not claim ownership of the original works or their underlying intellectual contributions. Burkhard Heim remains the original author of Syntrometrische-Maximentelezentrik. The mathematical reconstructions, expository material, formalizations, and contemporary interpretations presented here constitute independent scholarly work.When referencing this repository, readers should distinguish clearly between Heim’s original formulations and the modernized reconstructions or extensions developed in this work.CitationWhen referencing this work, please acknowledge:Burkhard Heim — original author of Syntrometrische-Maximentelezentrik.This modernized exposition and formal reconstruction — as the contemporary work consulted or cited.
Almost every serious instrument in AI governance now requires an organisation to run a risk process. None of them tells the organisation where in its own lifecycle the control points sit, what measurable limit applies at each one, what happens automatically when a limit is breached, or who is competent to verify the measurement. That layer, process control, is missing, and it is the layer on which everything else depends. A management system without control points produces documentation; it does not produce safety. This paper supplies that layer by importing a method that has governed an invisible hazard across a globally distributed production chain for more than fifty years: Hazard Analysis and Critical Control Points. The claim is deliberately narrow. HACCP is not proposed as a rival to ISO/IEC 42001, to Article 9 of the EU AI Act, or to the NIST AI Risk Management Framework. It is proposed as the process-control layer their architecture presupposes and does not contain, and as the one methodology in existence with a property AI governance urgently needs and currently lacks: scale invariance. The same seven principles govern a village bakery and a multinational dairy, which is why one inspector, one standard and one accreditation system can cover both. The method is given in the full twelve-step Codex sequence, with a three-tier proportionality rule (deployer, provider, frontier developer), a hazard taxonomy, a control-point decision tree, a catalogue of seven control points, and a taxonomy of limits that separates critical limits from operational limits and indicator thresholds, and reclassifies the compute thresholds now written into law as the third kind. Version 5.0 adds what an expert reader of version 4.0 was entitled to ask for: the instruments. A plan that says “monitor the violation rate” has not yet said anything an engineer can implement. Part II therefore imports, with their arithmetic, the measurement tools that make food-safety limits real: attribute sampling plans that state how many outputs to inspect and how many failures to tolerate before a lot is rejected, with their operating characteristics; statistical process control that gives the operational limit a precise meaning as a warning limit on a control chart; a seven-step protocol for validating an evaluation as a measuring instrument, with inter-rater reliability, uncertainty and a scope statement; and record schemas (a configuration manifest, a monitoring record, a deviation register) that make version identity and traceability a matter of implementation rather than intention. Part III applies the instruments in four worked plans, including a new one for an agent with tool access, and specifies a pre-registrable inter-rater study by which the method's central claim to auditability can be tested and, if warranted, refuted. The paper states its own limits without softening. The most consequential gap in AI-governance infrastructure is metrological: there is no analogue of ISO/IEC 17025 for capability evaluation, and until there is, independent attestation of a capability-based limit is not available to anyone. One structural disanalogy has no food-safety precedent at all: a pathogen does not model the control system trying to detect it, and a sufficiently capable AI system may. Both are treated as design constraints on the method rather than as objections to be answered later. The framework is accordingly strongest where most AI harm occurs, in organisations that deploy and provide AI systems, and is stated to be aspirational at the frontier until independent measurement exists. This record contains three files: the full paper (version 5.0, 73 pages), a two-page Executive Summary for policy readers, and a 15-page Practitioner Brief containing Part II (the instruments) as a standalone document. Version 5.0 supersedes version 4.0 (July 2026); earlier versions remain available under the same concept DOI. CC BY 4.0.
Simone Paciaroni· Zenodo (CERN European Organi...· 0 citations
Abstract Background The integration of artificial intelligence (AI) into higher education has accelerated, yet little is known about the psychological mechanisms underlying students’ reliance on AI. This study conceptualizes AI dependency as a complex cognitive–motivational construct that extends beyond mere usage, influencing anxiety, digital stress, and quality of life. Methods A 521 participants (predominantly undergraduate, 80% female) recruited via snowball sampling at King Abdulaziz University. Self-administered standardized instruments assessed AI dependency, AI-related general anxiety, digital stress, quality of life, and AI dependency. A network analysis approach was employed to examine the interrelations among AI dependency, cognitive offloading, anxiety, availability pressure, FoMO, digital overload, digital vigilance, social acceptance anxiety, and quality of life among university students. This approach allowed identification of central variables and conditional interactions within a dynamic psychological system. Results AI dependency emerged as a structurally foundational variable, reorganizing students’ cognitive and emotional experiences. It was closely linked to digital stressors, including digital vigilance and fear of missing out, while anxiety functioned as a mediator connecting cognitive reliance to environmental pressures. Social acceptance anxiety translated cognitive pressures into relational–identity concerns, and cumulative effects manifested in reduced quality of life. The network revealed non-linear, conditional associations, highlighting that the psychological impact of AI dependency is mediated by cognitive, motivational, and contextual factors rather than by direct usage intensity alone. Conclusions AI dependency is not a neutral or purely functional behavior but a central psychological construct with both potential advantages, such as reduced cognitive load and increased efficiency, and risks, including diminished autonomy, heightened anxiety, and long-term digital strain. These findings offer a culturally contextualized model for understanding AI’s influence on student well-being and provide a framework for interventions that target central nodes in the network to promote healthier engagement with AI in academic settings.
This dataset comprises the complete supplementary materials for a decolonial qualitative systematic review examining how Artificial Intelligence (AI) mediates Multicultural Religious Education (MRE) across Islamic, Christian, Jewish, Hindu, Buddhist, and indigenous educational traditions. The review synthesizes 254 peer-reviewed studies (2014–2026) from English, Indonesian, and Arabic sources, following PRISMA 2020 guidelines. The core contribution is an empirically-grounded framework identifying six AI mediation mechanisms along a continuum from algorithmic substitution (moral outsourcing, normative amplification, cultural flattening) to dialogic integration (hybrid deliberative mediation, contextual value re-embedding, dialogic ethical framing). The analysis further documents five decolonial possibilities emerging from Global South practices: epistemic re-centering, pedagogical sovereignty, cultural counter-flattening, governance innovation, and transnational solidarity. All supplementary materials supporting the main article are provided here, including complete study characteristics, inter-coder agreement matrices, quality appraisals, thematic analyses, and detailed appendices with operational definitions and conceptual frameworks. Supplementary Material Description SM-01 Study Characteristics Mapping – Complete bibliographic data, context, and methodology for all 254 studies SM-02 Inter-Coder Agreement Matrix – Theme-level Fleiss' κ scores demonstrating reliability (mean κ = 0.87) SM-03 JBI Critical Appraisal – Quality assessment scores with strengths and limitations for each study SM-04 PICo and Thematic-Content Analysis Matrix – Population-Interest-Context mapping with operational definitions SM-05 Theme-SWOT-CIMO Analysis – SWOT categorization with dominant mechanisms for all studies SM-06 Distribution of Reviewed Studies – Geographic, methodological, and educational level distribution (N=254) SM-07 Condensed Evidence Matrix – PICo × DEAM-MRE Domain × Region × Mechanism (N=254) SM-08 Mechanism Density Analysis – CIMO framework distribution across the corpus SM-09 Intercoder Reliability Summary – Derived from SM-02 with domain-level aggregation SM-10 MASTER DATA MATRIX SM-11 SECOND-CODER VERIFICATION REPORT SM-12 VALIDATION, RECONCILIATION SM-13 All References
Dwi Mariyono· Zenodo (CERN European Organi...· 0 citations
