Education Technology advances many aspects of learning. More and more learning is taking place online. Learners’ learning behaviors, style, and performance can be easily profiled through learning analytics which collects their online learning footage. It enables and encourages educational research, learning software application development, and online education practices towards personalized and adaptive learning. As we continue to see personalized and adaptive learning progress, we must also pay attention to the negative impacts that feed into our research. In this paper, we will present our introspection of personalized and adaptive learning and argue that it is the social and moral responsibility of educators and institutions to apply personalized and adaptive learning wisely in their education practice. Educators and institutions should also recognize the realistic diversities of individual students’ learning styles and variable learning progress, contextually dependent learning accessibility, and their correspondent support needs for the fine-grained learning activities. We argue that the strategically balanced practices and innovated learning technology are crucial towards an optimized learning experience for the learners.

Achieving Artificial General Intelligence (AGI) requires a unified framework capable of modeling the full spectrum of intelligent behavior—from logical reasoning and sensory perception to emotional regulation and collective decision-making. This paper proposes Constrained Object Hierarchies (COH), a neuroscience-inspired theoretical model that represents intelligent systems as hierarchical compositions of objects governed by symbolic structure, neural adaptation, and constraint-based control. Each object is formally defined by a 9-tuple structure: $O=(C,A,M,N,E,I,T,G,D)$, encapsulating its Components, Attributes, Methods, Neural components, Embedding, and governing Identity constraints, Trigger constraints, Goal constraints, and Constraint Daemons. To demonstrate the scope and versatility of COH, we formalize nine distinct intelligence types—including computational, perceptual, motor, affective, and embodied intelligence—each with detailed COH parameters and implementation blueprints. To operationalize the framework, we introduce GISMOL, a Python-based toolkit for instantiating COH objects and executing their constraint systems and neural components. GISMOL supports modular development and integration of intelligent agents, enabling a structured methodology for AGI system design. By unifying symbolic and connectionist paradigms within a constraint-governed architecture, COH provides a scalable and explainable foundation for building general purpose intelligent systems. A comprehensive summary of the research contributions is presented right after the introduction.

Modern enterprise information systems must simultaneously support complex organizational structures, ensure robust security, and remain scalable and maintainable over time. Traditional Role-Based Access Control (RBAC) models, while effective for permission management, operate primarily as post-design security layers and do not provide a unified methodology for structuring system architecture. This paper introduces the Zoned Role-Based (ZRB) model, a mathematically formalized and comprehensive framework that integrates organizational modeling, system design, implementation, access control, and long-term maintenance. ZRB models an organization as a hierarchy of zones, each containing its own roles, applications, operations, and users, forming a recursive Zone Tree that directly mirrors real organizational semantics. Through formally defined role hierarchies, zone-scoped permission sets, and inter-zone inheritance mappings, ZRB provides a context-aware permission calculus that unifies authentication and authorization across all zones. The paper presents the theoretical foundations of ZRB, a multi-phase engineering methodology for constructing integrated enterprise systems, and a complete implementation architecture with permission inference, navigation design, administrative subsystems, and deployment models. Primary validation and evaluations across several developed systems demonstrate significant improvements in permission accuracy, administrative efficiency, scalability, and maintainability. ZRB thus offers a rigorously defined and practically validated framework for building secure, scalable, and organizationally aligned enterprise information systems.

Governance research commonly evaluates political systems through regime classifications, institutional procedures, or isolated measures of state capacity. Such approaches provide limited insight into how governance capabilities interact dynamically to influence human flourishing. This conceptual paper develops the General Governance Success Model (GSM), a systems-theoretical metamodel representing governance as a complex adaptive and information-processing system. The model defines governance through five coupled capabilities: accountability, institutional competence, social cohesion, strategic continuity, and adaptive learning. Drawing on cybernetics, the Viable System Model, system dynamics, and behavioural theories of bounded rationality, the framework specifies governance performance as an outcome of capability levels, complementarities, feedback delays, and functional misalignment. Governance capabilities and flourishing outcomes are modelled as co-evolving state vectors subject to institutional, resource, legal, and normative constraints. The paper identifies empirically testable hypotheses concerning capability interactions, misalignment, resilience, and the incremental explanatory value of the model beyond regime classification. It also outlines a comparative validation strategy combining dynamic panel analysis, model comparison, sensitivity analysis, and future agent-based simulation. The framework treats computational methods as decision-support and explanatory tools rather than substitutes for political judgment. Normative objectives and model weights remain subject to public deliberation, constitutional rights, transparency, and contestability. The model contributes a transdisciplinary foundation for analysing how interacting governance capabilities may support sustainable and distribution-sensitive human flourishing.

Modern enterprise systems must be both secure and adaptive—able to enforce strict access policies while responding intelligently to changing conditions. Yet the theories that explain intelligent behaviour and the engineering methodologies that build reliable systems have evolved separately, leaving a gap that forces designers to choose between formal guarantees and adaptive intelligence. This paper introduces the Constrained Zoned‑Object Architecture (CZOA), a unified formalism that bridges this gap. CZOA integrates the mathematical rigour of Constrained Object Hierarchies (COH) with the practical, validated structure of the Zoned Role‑Based (ZRB) framework. We show that enterprise systems are a natural species of intelligent systems, and that the combined framework preserves minimality while enabling new capabilities: neural‑enhanced permission mining, semantic embeddings for cross‑zone understanding, adaptive access control, and continuous daemon‑based enforcement. A complete Python implementation (CZOI toolkit) and a logic language (UniLang) make the formalism accessible to engineers. Five case studies across healthcare, finance, traffic, education, and supply chain demonstrate that CZOA delivers sub‑millisecond permission checks, up to 23% improvement in operational metrics, and high‑accuracy anomaly detection (94% precision) while maintaining rigorous security constraints. The paper concludes with a discussion of limitations, threats to validity, and future research directions.

Constrained Object Hierarchies (COH) offer a unified theoretical foundation for artificial general intelligence (AGI), rooted in neuroscience principles and developed with full mathematical rigor. This paper presents the complete formalization of COH theory, showing how intelligence emerges from hierarchically composed structures governed by adaptive optimization constraints. We introduce precise definitions, establish core properties of soundness and completeness, and situate COH within established mathematical frameworks including category theory, dynamical systems, and information theory. Building on this foundation, we prove three central theorems that demonstrate COH’s practical significance for AGI: guaranteeing high‑fidelity world modeling, preventing jagged or non-smooth intelligence behaviors, and enabling the construction of coherent agentic systems. These results provide quantitative bounds on representational accuracy, generalization performance, and decision-making complexity. Collectively, the findings show that COH delivers a mathematically rigorous, interpretable, and scalable basis for modeling intelligent systems across six heterogeneous domains, while preserving the flexibility required for general intelligence and ensuring explicit guarantees for safety and transparency.