ABSTRACT: Zoo training doesn’t prepare you for the jungle. TQ (Thinking Quotient) measures the trainable capacity to organize information using universal structure ()—and to detect and correct predictable structural errors in that organization—making it a direct instrument for “jungle-smart” adaptivity rather than “zoo-smart” ( g /IQ) test performance. Jungle-smart refers to real-world adaptivity under changing, embodied constraints. IQ is commonly treated as a synonym for the -factor. While is a powerful statistical summary of performance across narrow analytic ability tests, it is routinely overstated as “general intelligence”—as if being general across tests implied being general across real life. Intelligence in the wild is adaptivity: the capacity to remain aligned with situational reality as reality changes. This paper reframes real-world intelligence as movement toward model–world alignment over time (the Love–Reality Loop limit, ) and shifts the primary measurement handle from Zoo IQ to Jungle IQ: from IQ to TQ, a psychometric instrument of organizing capability () and metacognitive control of organization (the ability to detect and correct predictable structural error, including Pareto allocation bias). This is a theory-and-instrument paper: it specifies a mechanistic model, defines measurable targets, and outlines an extensible measurement interface. It does not claim to settle the full empirical program in a single manuscript or to replace all existing psychometrics; it claims that intelligence in the wild requires measurement of organization plus constraint and debt dynamics, and that TQ provides an existing, testable handle on the organization term. Grounded in O-theory, we formalize mental models and reality as and , implying that performance depends multiplicatively on information () and organization (). This makes trainability explicit: while baseline traits may vary, organization is trainable; therefore real-world adaptivity is substantially trainable. We define Mental Fitness as the coupled capacity to (1) increase and regulate organizational control via TQ-indexed metacognition (de-biasing structural error) and (2) manage and/or regulate Emotional, Motivational, and Physical constraints (EMP) so that organizational capacity is expressed as effective organization in the moment. We retain a conservative weakest-link logic for realized adaptivity (limiter behavior) while distinguishing organizational capacity from its state-constrained expression. To capture delayed consequences of lopsided optimization, we define Cross-Domain Debt as accumulating unmodeled costs (otherizing) and introduce a bounded leading indicator (Sustainability) that combines current state and trajectory to surface collapse risk before it appears in outcomes (“we looked fine until we didn’t”). Operationally, debt is detected when costs are repeatedly pushed into “other” categories of the model—left untracked, deferred, or externalized—until they return as binding constraints (e.g., skipped recovery becoming injury; suppressed conflict becoming rupture; chronic override becoming burnout). In measurement terms, debt is any persistent, time-accumulating deficit signal in a domain that is being systematically under-modeled relative to what reality enforces. Finally, we specify a measurement interface that treats TQ as the primary instrument for -capability and metacognitive error-correction, complemented by admissible mixed panels indexing EMP (emotional, motivational, physical) constraints and debt dynamics, positioning operationalization as an ongoing empirical program. We conclude that remains useful for predicting Zoo-smart analytic performance, but Jungle-smart adaptive intelligence is more parsimoniously modeled—and more directly measurable and trainable—via TQ-indexed organization plus constraint and debt dynamics that govern adaptivity in the wild. This paper’s claim is deliberately bounded: it does not reject within its testing scope; it argues that intelligence in the wild requires additional measurable structure—organization plus state and debt dynamics—that standardized test batteries are not designed to capture.

This study takes 300 literatures (from 2014 to 2024) included in the Web of Science Core Collection (mainly SSCI and SCI-E) as the data source and uses CiteSpace 6.3.R1 software to analyze the evolution of Profile Technology Applied in Education (PTAE). The research identified three core clusters: learner analysis and recommendation, intelligent technology-driven Educational Data Mining (EDM), and governance of the blended learning ecosystem. It reveals the deep transformation path of the field from basic feature characterization through data-driven prediction to a panoramic intelligent ecosystem. The research finds that the core research model is transitioning from static portrayal to a dynamic and precise intervention mechanism based on a panoramic learning analytics framework, establishing the underlying logic of modern adaptive systems Based on the findings, this paper explores algorithmic fairness and ethics challenges, and proposes practical strategies for building an ecologically adaptive intelligent educational environment.

Marketing has long been both an art and a science, connecting firms with consumers and shaping competitive advantage. Yet measurement practices remain overly dependent on financial outputs such as Return on Investment (ROI), emphasizing short-term gains while overlooking marketing’s strategic and societal value. In an era defined by artificial intelligence, digital acceleration, and economic volatility, such traditional metrics no longer capture marketing’s full impact. This paper introduces the AI-Integrated Marketing Measurement Model (AIMM 2.0)—an adaptive, AI-driven framework that redefines marketing effectiveness. AIMM 2.0 integrates Marketing Mix Modeling (MMM), Multi-Touch Attribution (MTA), and Incrementality Testing (IT) within a continuous, intelligent learning system. Guided by the Four “I” Principles—Intelligence, Interpretability, Integration, and Impact, the model positions marketing measurement as a strategic capability that evolves with data, technology, and managerial insight. By bridging creative strategy with advanced analytics, AIMM 2.0 transforms marketing from the art of persuasion into the science of sustainable growth. It quantifies marketing’s contribution beyond short-term sales, linking analytical insights to business performance, innovation diffusion, and national economic resilience. AIMM 2.0 thus marks a paradigm shift—elevating marketing measurement from a retrospective financial tool to a forward-looking system of adaptive intelligence and strategic growth.

Biological intelligence is inherently adaptive-animals continually adjust their actions in response to environmental feedback. However, creating adaptive artificial intelligence (AI) remains a major challenge. The next frontier is to go beyond traditional AI to develop 'adaptive intelligence', defined here as harnessing insights from biological intelligence to build agents that can learn online, generalize and rapidly adapt to changes in their environment. Recent advances in neuroscience offer inspiration through studies that increasingly focus on how animals naturally learn and adapt their models of the world. This Perspective reviews the behavioral and neural foundations of adaptive biological intelligence, examines parallel progress in AI, and explores brain-inspired approaches for building more adaptive algorithms.

This study investigates how artificial intelligence (AI) and digital transformation shape inclusion and equity in Romania’s preuniversity education system. Using a sequential explanatory mixed-methods design, we surveyed 362 teachers, school leaders, and support staff across eight development regions (January-March 2025), followed by 22 semi-structured interviews with educators, students (including learners with special educational needs), counsellors, digitalization coordinators, and inspectorate representatives (April-May 2025). We introduce the Inclusive AI-Transformation Nexus for Romania (IATN-RO), which integrates four interdependent domains – Access Infrastructure, Adaptive Intelligence, Justice and Governance, and Relational Support Systems – under the principles of Agency, Accountability, and Alignment. Quantitative analysis validated two composite measures: the Inclusive Integration Index (III-RO) (α = .82–.90) and the Equity Outcomes Composite (EOC-RO) (α = .88). III-RO was strongly correlated with EOC-RO (r = .52, p < .001) and remained a significant predictor in regression models controlling for location, funding, and school level (β = .39, p < .001). AI governance maturity independently predicted equity outcomes (β = .31, p < .001), while general AI usage intensity was not significant (β = .08, p = .11). Professional Learning Ecosystems mediated the III-EOC relationship (indirect effect = 0.10, 95% CI [0.05, 0.17]), and connectivity constraints moderated implementation effects (β_interaction = −.11, p = .03). Thematic analysis revealed five recurring patterns: the centrality of human mediation, transparency as trust, co-design with students and parents, inclusive improvisation in resource-poor settings, and the negotiation of algorithmic tensions. Overall, the findings show that equity depends on institutional capacity, governance, and relational support rather than the volume of AI tools. Policy recommendations address ethics, infrastructure, teacher training, and inclusive orchestration within Romanian legislative and infrastructural realities.

Reinforcement learning-driven adaptive intelligence for phytochemical optimization in cannabis cultivation using precision agriculture

This study investigates whether existing commercial video games are capable of accurately representing the complexity of traditional peasant society prior to the mechanization of agriculture. The central research question is as follows: If one were to create a virtual museum dedicated to the intangible heritage of peasant civilization, are there existing games that can effectively simulate the multifaceted nature of such a world? In order to address this question, the paper begins by reconstructing the socio-cultural characteristics of pre-industrial rural life, with particular emphasis on its adaptive intelligence, relational knowledge systems, and close integration with natural cycles. It then conducts a comparative analysis of current video games on the market that claim to represent rural or agricultural settings, evaluating their fidelity in terms of social, symbolic, and experiential complexity. Where existing commercial solutions prove insufficient, particularly in capturing the epistemic and affective dimensions of peasant culture, the study argues for the necessity of designing a dedicated edugame. This prospective educational tool should not merely reproduce surface elements; instead, it should integrate principles of simplexity and embodied learning, thereby offering an immersive, situated, and meaningful encounter with a form of life that, while no longer materially present, continues to inform local knowledge and values. The paper concludes by outlining the key features and design considerations for such an edugame, establishing a foundation for future development and interdisciplinary collaboration.

Despite Nigeria’s enormous natural and human resources, the country remains entrapped in chronic poverty and developmental fragility. This paradox of “poverty amidst plenty” raises crucial questions about the sustainability and appropriateness of Western-led development paradigms that often marginalise local epistemologies. This paper explores whether African Indigenous Knowledge Systems (AIKS) can serve as a transformative tool for poverty alleviation in Nigeria. Through a qualitative synthesis of ethnographic studies, policy documents, and secondary data, the paper examines how indigenous agricultural practices, community-based governance, traditional healthcare, and informal economic networks sustain livelihoods where modern systems fail. The findings reveal that indigenous knowledge is not merely cultural residue but a living system of adaptive intelligence with potential to complement formal poverty reduction policies, where development is defined socioeconomically and culturally by the people, for the people and from the people. The paper concludes that the revalorisation of indigenous knowledge through integration into education, agriculture, economics and governance, offers a pragmatic pathway toward inclusive and sustainable development. Specifically, the Nigeria government and financial operatives should link traditional savings and investment schemes with Microfinance Banks in rural and semi-urban areas to empower low-income groups to foster a hybrid rural economy of AIKS.

Climate change is increasingly disrupting businesses and ecosystems, creating urgent demand for data-driven adaptation strategies. This study examines how artificial intelligence (AI) can strengthen climate resilience across diverse industries, with particular attention to the innovative business strategies that help organizations respond to global environmental challenges. The objective is to address gaps in existing AI frameworks, focusing on developing countries with resource and technical limitations. The study highlights the significance of AI in fostering sustainable practices, particularly in climate change mitigation. A systematic review of 42 high-quality studies, published between 2010 and 2025 in the Scopus database, was carried out using the PRISMA framework. The analysis identifies key AI applications, technologies, and challenges. Data were organized according to industry applications, technological contributions, and obstacles. Key findings indicate that AI enhances climate risk assessment through predictive modelling, supports adaptive decision-making via scenario analysis, and optimizes resource allocation for sustainability. Applications in renewable energy, precision agriculture, and disaster management are also noted. However, significant barriers persist, including ethical concerns such as algorithmic bias and data privacy, technical complexities, and high financial costs. The review underscores the necessity of collaborative approaches, such as public-private partnerships, and the importance of conducive policy frameworks. The research's originality lies in its comprehensive synthesis of AI applications for climate resilience, offering actionable insights for scholars, practitioners, and policymakers. The findings highlight AI's potential to drive sustainable business models while calling for interdisciplinary research to address scalability and ethical implications in resource-constrained environments.

Pneumonia remains a serious worldwide health concern, particularly in low resource countries, where prompt diagnosis is challenging. Early detection relies on chest radiography, but data privacy rules and patient data fragmentation make AI model building difficult. Federated Learning allows collaborative model training without patient data sharing, a promising solution. Standard federated learning methods like FedAvg suffer with data heterogeneity and significant communication overhead. To overcome these constraints, this research proposes an upgraded federated framework with FedProx, which mitigates client drift in non-IID contexts by proximal optimization and Low-Rank Adaptation, a parameter-efficient fine-tuning technique that minimizes communication costs. Vision Transformers are used as the backbone architecture for chest X-ray categorization because they capture the global visual context better than convolutional models. The tiny memory footprint proposed in this research, fits resource-constrained medical infrastructure. The proposed technique was validated for a pneumonia classification job utilizing the publicly available Chest X-Ray Images dataset, which was distributed across simulated clients to replicate real-world healthcare organizations. The model's performance is measured using accuracy, precision, recall, F1-score, AUC and system-level measures including communication cost per round and convergence rate. The proposed federated model had 88.5% classification accuracy under data heterogeneity and reduced communication overhead and computation cost. Explainability research employing attention heatmaps supports the model's clinically important pulmonary areas, boosting clinical adoption, trust and transparency.

IntroductionConventional artificial intelligence (AI) systems are limited by static architectures that require periodic retraining and fail to adapt efficiently to continuously changing data environments. To address this limitation, this research introduces a novel biologically inspired computing paradigm that supports perpetual learning through continuous data assimilation and autonomous structural evolution. The proposed system aims to emulate biological cognition, enabling lifelong learning, self-repair, and adaptive evolution without human intervention.MethodsThe system is built upon dynamic cognitive substrates that continuously absorb and map real-time information streams. These substrates eliminate the traditional distinction between training and inference phases, supporting uninterrupted learning. Quantum-inspired uncertainty management ensures computational robustness, while biomimetic self-healing protocols maintain structural integrity during adaptive changes. Additionally, micro-optimization via fractal propagation enhances mathematical specialization across hierarchical computational levels. Recursive learning mechanisms allow the architecture to refine its functionality based on its own outputs.ResultsExperimental validation demonstrates that the proposed architecture sustains effective learning across diverse, heterogeneous data domains. The system autonomously restructures itself, maintaining stability while improving performance in dynamic environments. Specialized cognitive processing units, analogous to biological organs, perform distinct functions and collectively enhance adaptive intelligence. Notably, the system prioritizes and retains valuable information through evolution, reflecting biological memory consolidation patterns.DiscussionThe findings reveal that continuous, self-modifying AI architectures can outperform traditional models in non-stationary conditions. By integrating quantum uncertainty control, biomimetic repair mechanisms, and fractal-based optimization, the system achieves resilient, autonomous learning over time. This approach has far-reaching implications for developing lifelong-learning machines capable of dynamic adaptation, self-maintenance, and evolution paving the way toward fully autonomous, continuously learning artificial organisms.

The rapid evolution of technology has transformed the relationship between humans and intelligent systems, shifting from basic automation to highly interactive and adaptive collaboration. Intelligent Adaptive Technologies (IAT) represent this new phase, where AI systems are designed to learn from human behavior, adjust to changing tasks, and provide timely support that strengthens decision-making and workplace efficiency. Rather than replacing human capability, these systems work alongside individuals, helping to improve accuracy, productivity, and innovation in everyday operations. This research explores how Human–AI collaboration through adaptive technologies influences organizational performance, particularly in the sectors of education, healthcare, and business services. A quantitative study was carried out with a sample of 210 participants, and data was analyzed using descriptive statistics, chi-square analysis, regression methods, and Structural Equation Modeling (SEM). The findings indicate that intelligent adaptive systems have a strong positive impact on employee productivity (β = 0.62, p < 0.001), accuracy in decisions (β = 0.54, p < 0.001), and overall user satisfaction (β = 0.47, p < 0.01). The results highlight that the future of work will be driven not by full automation, but by augmentation—where technology amplifies human strengths and reduces operational burdens. The study proposes a conceptual model for achieving effective Human–AI collaboration and offers practical recommendations for building organizational readiness through trust, transparency, ethical design, and employee training. These insights open pathways for further research and strategic implementation of collaborative intelligence in rapidly changing digital environments.

Purpose This study aims to develop an adaptive learning platform that leverages generative artificial intelligence (AI) to automate assessment creation and feedback delivery. The platform provides self-correcting tests and personalised feedback that adapts to each learner's progress and history, ensuring a tailored learning experience. Design/methodology/approach The study involves the development and evaluation of a web-based application for revision for the UK driving theory test. The platform generates dynamic, non-repetitive question sets and offers adaptive feedback based on user performance over time. The effectiveness of AI-generated assessments and feedback is evaluated through expert review and model analysis. Findings The results show the successful generation of relevant and accurate questions, alongside positive and helpful feedback. The personalised test generation closely aligns with expert-created assessments, demonstrating the reliability of the system. These findings suggest that generative AI can enhance learning outcomes by adapting to individual student needs and offering tailored support. Research limitations/implications Limitations include the narrow, self-taught scope and lack of large-scale validation. The algorithmic adaptation may miss nuanced learner needs. Future research requires longitudinal studies and the development of more semantically intelligent adaptation algorithms. Originality/value This research introduces an AI-powered assessment and feedback system that goes beyond traditional solutions by incorporating automation and adaptive learning. The non-memoryless feedback mechanism ensures that student history and performance inform future assessments, making the learning process more effective and individualised. This contrasts with conventional systems that provide static, one-time feedback without considering past progress.

The meta-layered framework: a diagnostic approach to ethical pluralism in human–AI systems—paradox, pluralism, and the moral architecture of adaptive intelligence

Artificial Intelligence Integration in Financial Systems and Enterprise Automation: Technical Architecture and Implementation Frameworks

Emotional intelligence (EI) describes a person’s capacity to identify emotions, understand their meaning, manage them appropriately, and use emotional cues to guide behavior in themselves and others. It is a key construct in psychological, educational, and organizational research. This review synthesizes recent empirical studies (2000–2024) examining the associations between Emotional Intelligence and psychological and behavioral variables, such as resilience, stress, life satisfaction, academic motivation, personality traits, metacognitive awareness, and quality of life. Findings across diverse populations, including students, teachers, and medical professionals, consistently indicate that higher EI is positively associated with improved mental health, enhanced stress coping mechanisms, and greater academic and occupational performance. Furthermore, constructs like resilience, self-efficacy, and social support have been shown to play critical intermediary roles in the association between EI and life outcomes. Despite methodological challenges, such as variations in EI measurement tools and discrepancies across models, the accumulated evidence underscores the significant role of EI in adaptive functioning and psychological well-being. The review concludes by discussing theoretical implications and highlighting directions for future research, including the need for longitudinal studies and standardized EI measurement tools.

This research presents an innovative smart community interactive art therapy platform that integrates multimodal computer graphics with resilient artificial intelligence adaptation mechanisms to address the growing challenges of home-based elderly care. The platform employs a four-layered hierarchical architecture encompassing perception, network, platform, and application layers to deliver personalized therapeutic interventions. The system utilizes multimodal data fusion algorithms to process visual, auditory, and haptic inputs while implementing adaptive learning mechanisms that continuously optimize user experiences based on individual preferences and capabilities. Experimental validation demonstrates superior performance with response times averaging 387 ms under 100 concurrent users, therapeutic recommendation accuracy of 87.3%, and user satisfaction scores of 4.2/5.0 across multiple evaluation dimensions. The resilient adaptation mechanisms achieved 99.7% service availability and 34% improvement in CPU utilization compared to conventional systems. Long-term usage tracking revealed sustained engagement patterns with minimal dropout rates over 6-month evaluation periods. The platform successfully addresses key limitations of traditional elderly care models by providing comprehensive support that encompasses cognitive stimulation, emotional well-being, and social connection while maintaining cost-effectiveness and scalability for large-scale deployment in smart community environments.

Wireless sensor networks (WSNs) will be necessary for the next generation of Internet of Things (IoT) apps. They make it possible to use smart and long-lasting sensors and smart automation in healthcare, Industry 4.0, and critical infrastructure. But security is particularly hard since they have built-in flaws, not enough computer power, not enough energy, and a significant danger of insider threats. Standard encryption methods aren’t enough, and in situations where resources are restricted, heavier blockchain or machine learning solutions aren’t always possible. This study presents ChainShieldML, a lightweight hybrid security architecture that combines Blockchain (BC) and machine learning (ML) to provide decentralised, adaptive, and resource-efficient protection for wireless sensor networks (WSNs). The idea is based on a two-pronged defence strategy. The Blockchain Prevention Module’s permissionless blockchain architecture for base stations and cluster heads makes it possible to verify identities, maintain trust in a decentralised way, and keep node interactions unchangeable. Smart contracts made in solidity and connected to the Ethereum ecosystem make it possible to safely register nodes and keep an eye on what they do. The VBFT consensus algorithm makes it possible to quickly validate without using as much computing power as most proof of work methods. The machine learning detection module uses the lightweight gradient boosting method (LightGBM) to find and rank dangerous nodes in real time. LightGBM is the best machine learning classifier when looking at things like recall, F1-score, Matthews correlation coefficient, training cost, and inference latency. ChainShieldML dramatically improves the detection of insider attacks, builds trust, and protects data while using very little energy and having very little communication delay, as shown in tests. For Wireless sensor networks (WSNs) to keep working, all of these things are very important. ChainShieldML is a novel solution to keep IoT devices safe. It uses blockchain’s decentralised trust and ML’s adaptive intelligence to make a defence system for next-generation wireless sensor networks that can grow, is strong, and is ready for the future.

This study introduces a hybrid control strategy that synergizes event-triggered deep reinforcement learning (DRL) with an adaptive fuzzy PID to address the challenges posed by multi-physics disturbances in ultra-precision motion systems. The proposed system employs an event-triggered mechanism that activates system updates only when control errors exceed preset thresholds, significantly reducing unnecessary computational loads. A Deep Q-Network (DQN) is integrated to autonomously optimize control policies through environment interactions, enabling intelligent adaptation to complex disturbances. Concurrently, an adaptive fuzzy PID controller dynamically adjusts proportional, integral, and derivative gains based on real-time error signals and disturbance intensity, effectively compensating for system nonlinearities and uncertainties. The synergy between DRL-based decision-making and fuzzy logic parameter tuning ensures coordinated responses to time-varying disturbances. Experimental validation demonstrates notable performance improvements, with response times consistently maintained at 3.4–3.7 ms and steady-state errors reduced to 0.003–0.006 μm under multi-physics interference. These metrics confirm the strategy’s capability to balance rapid response with micron-level precision while minimizing controller actuation frequency. The dual-layer optimization approach–combining intelligent event-triggered learning with model-free fuzzy adaptation – provides a scalable solution for high-precision motion control in environments with coupled physical disturbances, offering potential applications in semiconductor manufacturing and precision optics alignment systems.

Abstract The rapid rise of generative artificial intelligence (AI) is transforming the landscape of human cognition, education, and society. This position paper explores the implications of generative AI for human intelligence, creativity, and wisdom, with a particular focus on educational contexts. Drawing on cultural, psychological, and educational theories—especially the framework of adaptive intelligence and the Teaching for Active Concerned Citizenship and Ethical Leadership (ACCEL) model—we argue that AI challenges foundational human cognitive abilities by automating tasks traditionally central to learning and intellectual, creative and ethical development. We examine how AI reshapes the cultural attributes of human intelligence and creativity—context dependence, dynamism, and modifiability—highlighting both the potential for cognitive amplification and the risks of cognitive deskilling. The paper also addresses the erosion of critical thinking and the ethical dilemmas posed by AI’s integration into education. While acknowledging the benefits of AI, such as personalized learning and enhanced productivity, we caution against overreliance and the uncritical adoption of AI-generated outputs. We advocate for an educational response that prioritizes the cultivation of analytical, creative, and ethical reasoning—skills that remain uniquely human and essential for democratic citizenship. The paper concludes by examining the motivational forces driving AI development and adoption, and by calling for a renewed commitment to preserving human autonomy, intellectual integrity, and wisdom in an increasingly machine-mediated world. Ultimately, the future of human intelligence and creativity in the age of AI will depend not only on technological advances but on the values and educational practices we choose to uphold.