explainable ai

Using Artificial Intelligence to Achieve Inclusive and Effective Education

Artificial intelligence, as one of the transformative technologies of the contemporary era, has considerable capacity to respond to learners’ individual differences, reduce barriers to access to education, and improve the quality of the teaching–learning process. The present study was conducted with the aim of explaining how artificial intelligence can be used to achieve inclusive and effective education. The research method is library-based and analytical; accordingly, the data were collected through the study, classification, and analysis of documents, scientific reports, and reputable peer-reviewed articles in the fields of artificial intelligence in education, inclusive education, and learning technologies. The findings indicate that AI-based tools, including adaptive learning systems, intelligent tutors, learning analytics, speech-to-text technologies, text-to-speech technologies, and assistive technologies, can align educational content, learning pace, feedback, and assessment methods with learners’ diverse needs, abilities, and circumstances. These capabilities can provide more equitable opportunities for participation in education, particularly for students with special educational needs, learners in under-resourced areas, and individuals with linguistic or cultural differences. However, the real effectiveness and inclusiveness of these technologies depend on observing principles such as the protection of personal data, algorithmic transparency, the mitigation of bias, equal access to digital infrastructure, and the preservation of the teacher’s central role (Miao & Holmes, 2023; Zawacki-Richter et al., 2019). It can therefore be concluded that artificial intelligence, when used responsibly and through a human-centered approach, can move beyond being merely a technological tool and become a means of strengthening educational equity, personalizing learning, and increasing the effectiveness of education.

Are Feature Selection and Feature Attribution the Same? A Comparative Survey -- Extended Version

Dynamic and Explainable Deep Learning Models

This paper presents a novel approach to developing dynamic and explainable deep learning models. The core challenge in deploying deep learning systems is often their "black box" nature, hindering trust and adoption. This work addresses this issue by integrating Explainable Artificial Intelligence (XAI) techniques with Reinforcement Learning (RL). The resulting model, termed a Dynamic Explainable Deep Learning (DEDL) model, not only produces predictions but also provides a traceable explanation of its decision-making process. Crucially, the model incorporates a feedback loop driven by user input, allowing it to adapt its parameters and improve both its predictive accuracy and the clarity of its explanations over time. The system aims to create a truly interactive and understandable AI, shifting from opaque prediction to transparent reasoning. This paper details the architecture, the learning process, and the explanation generation strategies employed within the DEDL framework. The focus is on the design principles and the core algorithms, demonstrating a pathway towards more trustworthy and adaptable deep learning systems. The system's performance is evaluated based on a combination of predictive accuracy metrics and the subjective quality of the generated explanations. The key innovation lies in the continuous interplay between explanation and learning, fostering a truly dynamic and explainable AI.

Intuitions and Directional Shifts in Minimal Computation Cosmology

This research genealogy traces the development of Minimal Computation Cosmology from its earliest prime-based intuitions to the separation of LAW and SOURCE, the Wonsik Reality-Renderer Architecture (WRRA), and a non-branching universe with a single accumulated history. Rather than presenting the model as a completed physical theory, the report documents how each intuition exposed a hidden assumption, missing information layer, forced input, or invalid identification. It follows the conceptual transition from prime operators, dimensional filtering, finite-window transport, and the common carrier to fifteen-channel accessibility, autonomous Hamiltonian evolution, dormant-state opening, Standard-Model phenotypes, quantum realization, and the distinction among Actual, Reality, and Record. The report also explains why known physical constants and Planck-scale values may be legitimate disclosed inputs; why LAW must remain distinct from SOURCE; why SOURCE includes not only values but dependencies, read order, addressing, and realization conditions; and why the universe need not branch into every possible outcome. In this framework, many futures may remain possible, but only one event enters the physical record at each realization. Each major intuition is linked to its related public research paper and clickable Zenodo Version DOI. The result is both a conceptual history of the model and a transparent map of its claims, failures, revisions, open boundaries, and research lineage. Keywords: Minimal Computation Cosmology, WRRA, Wonsik Reality-Renderer Architecture, LAW and SOURCE, quantum mechanics, quantum realization, Standard Model, general relativity, cosmology, finite computation, information physics, dimensional filter, prime operators, zeta function, common carrier, Hamiltonian constraint, dormant state, FLRW cosmology, rank opening, fifteen-channel carrier, Actual–Reality–Record, Born probability, single outcome, non-branching universe, open future, research genealogy, Zenodo, DOI, human–AI collaborative research.

AI-Based Phishing Detection: A Review and Practical Study

This paper presents a review and practical study of AI-based phishing detection. It explains how machine learning can be used to identify phishing websites using URL and webpage features. The paper discusses common machine learning models such as Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, and Neural Networks. It also explains the main steps of building a phishing detection system, including data collection, preprocessing, feature extraction, model training, and evaluation. The paper discusses the challenges and limitations of machine-learning-based phishing detection and suggests areas for future work.

Neuro-Symbolic Reasoning with Hierarchical Bayesian Networks

This paper proposes a novel neuro-symbolic reasoning framework based on Hierarchical Bayesian Networks (HBNs). The core challenge in neuro-symbolic reasoning lies in effectively integrating the pattern recognition capabilities of neural networks with the structured reasoning capabilities of symbolic systems. Traditional approaches often struggle with knowledge representation and the ability to handle uncertainty. This work addresses these limitations by constructing a system where HBNs are used to represent and reason about knowledge hierarchically. Neural networks are employed to learn specific features and relationships within the HBN structure, while the Bayesian network provides a framework for probabilistic inference and reasoning under uncertainty. The hierarchical structure enables the system to decompose complex problems into smaller, more manageable sub-problems, improving both accuracy and interpretability. This approach demonstrates the potential to create more robust and explainable AI systems capable of handling complex reasoning tasks. The key contribution is the specific application of HBNs for this integration, moving beyond simple neural-symbolic hybrids. We outline the architecture, learning process, and inference mechanisms, focusing on the benefits of a structured knowledge representation.

Explainable AI for System Security

This paper investigates the application of Explainable Artificial Intelligence (XAI) to enhance system security. Traditional security systems often rely on opaque "black box" AI models, hindering effective threat detection and defense. This work proposes a system architecture leveraging XAI techniques to provide interpretable insights into AI-driven security decisions. The core claim is that building an XAI-based system for security threat detection and defense will improve both the efficiency and reliability of security measures. The proposed mechanism utilizes XAI to elucidate the reasoning behind AI's judgments, thereby facilitating the identification of vulnerabilities and potential threats. Specifically, we explore methods for generating explanations that highlight critical factors influencing security decisions, allowing human analysts to validate, refine, or override AI recommendations. This approach addresses the limitations of current black-box AI security systems by incorporating human understanding and control, ultimately leading to a more robust and trustworthy security posture. The research contributes to a paradigm shift in system security, moving beyond solely relying on AI's predictive capabilities to actively incorporating human expertise within the decision-making process.

Explainable AI via Causal Bayesian Networks

Current Explainable AI (XAI) techniques frequently generate explanations that are merely post-hoc justifications for model predictions, lacking a deep understanding of the model's reasoning process. This paper proposes a novel approach to XAI based on the construction of Causal Bayesian Networks (CBNs). CBNs are employed to explicitly model the causal relationships between input features and the model's output, offering a more robust and interpretable explanation. Unlike existing XAI methods which often rely on correlations, our approach leverages causality to provide a truly grounded understanding of how the model arrives at its decisions. The method is presented with a detailed theoretical framework and outlines the steps involved in constructing and utilizing CBNs for explainability. This research addresses a critical limitation in current XAI by moving beyond correlation-based explanations towards a causal understanding, ultimately leading to more reliable and trustworthy AI systems.

k-jinma/dogo-tutor: dogo-tutor v0.0.2 - Initial Release

This is the initial release of "dogo-tutor," an AI tutor designed for coding education. This release is created to archive the repository and obtain a DOI via Zenodo. Key Features Provides step-by-step hints (problem clarification, strategy, logic structuring, and partial code) rather than direct answers. Designed to offer deeper hints only when students explain "what they tried and what happened" in their own words. Automatically generates review notes and short quizzes (/review) based on the last 24 hours of dialogue history, focusing on concepts the student struggled with. Features a self-reflection tool (/log) that allows students to objectively track their dependency on deep hints. Employs a safe guidance design that strictly references URLs from actual lecture materials and official documentation specified by the instructor. Recent Changes Revamped the README for users and added an English summary. Adjusted the icon design for the VS Code extension. Updated various configuration files (e.g., package.json) for public release.

A Structural Framework for AI Ethics and Predictability: Re-centering Alignment on Stable Otherness

Coexisting safely with unpredictable Artificial Intelligence (AI) remains a foundational challenge for contemporary AI ethics. This paper proposes “stable otherness” as a relational framework that re-centers alignment on sociotechnical structures. Rather than treating AI as an autonomous conscious agent, we define AI as an inanimate “pseudo-otherness”—a mechanical externalization of human reflexive cognition. Drawing on a heuristic natural-historical scaffolding of interspecies relations, we analyze how relational predictability, interpretability, and response stability emerge. We show that AI’s unpredictability, unlike biological “wildness,” stems from structural limits including symbol-grounding deficits and next-token prediction dynamics. Operationalizing stable otherness through the triad of Explainability, Alignment Stability, and Safe-by-Design, we discuss implications for governance and argue that claims of AI rights may constitute a category error, thereby re-anchoring ethical responsibility in human design. This framework directly contributes to AI ethics, information ethics, and the philosophy of technology, providing a clear evaluative structure for alignment and governance.

Autonomous AI-Native Digital Twins for Mission-Critical Data Centres: A Multi-Layer Framework for Predictive Commissioning, Operational Readiness, Resilience and Lifecycle Optimization

The rapid convergence of artificial intelligence (AI) workloads, high-density computing, advanced liquid and air cooling, increasingly distributed electrical architectures, and stringent availability requirements is transforming the data centre from a predominantly passive infrastructure environment into a highly dynamic cyber-physical system. Conventional data centre infrastructure management (DCIM) platforms remain largely dependent on fragmented telemetry, threshold-based alarms, static visualization, and human interpretation, limiting their ability to continuously correlate physical infrastructure behaviour with computational workload, commissioning state, operational risk, and lifecycle constraints. This paper proposes AIDCDT-X, an AI-native autonomous data centre digital twin framework that extends the conventional digital twin concept from visualization and monitoring toward predictive, context-aware, and governance-controlled infrastructure intelligence. The proposed architecture evolves the original five-layer AIDCDT model into an integrated cyber-physical intelligence stack comprising physical asset representation, synchronized multi-domain telemetry, contextual digital-twin state estimation, predictive and prescriptive intelligence, decision orchestration, and governance/audit control. The framework establishes a continuous information loop connecting building management systems (BMS), electrical power management systems (EPMS), DCIM, information-technology telemetry, environmental sensing, commissioning records, asset configuration, and operational events. A central contribution is a lifecycle-aware twin maturation model linking Design, Commissioning, Operational Readiness, and Steady-State Operation. Rather than treating commissioning documentation as a terminal project artifact, the proposed approach converts Level 1–5 commissioning tests, integrated systems testing, defect registers, burn-in observations, and as-built configuration data into structured training and calibration evidence for the operational twin. This creates a facility-specific intelligence baseline capable of continuously updating its representation of infrastructure health, thermal behaviour, electrical resilience, capacity margin, and operational risk. The paper further introduces a proposed multi-objective cyber-physical optimization formulation in which latency, energy efficiency, thermal stability, availability, predictive uncertainty, maintenance risk, lifecycle cost, and governance constraints are jointly evaluated. A bounded-autonomy principle is incorporated to explicitly separate prediction from authorized physical action, thereby preventing unrestricted AI control of mission-critical infrastructure. The resulting framework provides a pathway toward explainable and auditable autonomous operations while retaining human authorization at safety-critical decision boundaries. Unlike a conventional conceptual digital twin, AIDCDT-X is designed to support measurable validation through predictive lead time, anomaly-detection performance, commissioning defect detection, decision latency, false-positive and false-negative rates, availability impact, energy efficiency, and human-override metrics. The present work establishes the architecture and analytical methodology; it does not claim empirical performance from a live facility. Future experimental validation will therefore focus on a controlled pilot or high-fidelity digital-twin environment using facility-specific commissioning and operational datasets.

Transforming Chemical Safety Policy in the AI Era: A Seven-Action Strategy for the Five Chemical Safety Acts Under the Ministry of Climate, Energy, and Environment

Chemical safety in the Republic of Korea is governed by five acts falling under the purview of the Ministry of Climate, Energy, and Environment (MCEE): the Act on Registration and Evaluation of Chemicals (K-REACH Act), the Chemicals Control Act (CCA), the Environmental Health Act, the Environmental Damage Relief Act, and the Consumer Chemical Products and Biocides Safety Act.These five acts manage substantial datasets but operate on largely independent data systems, classification schemes, and identifiers, which limits cross-act risk prediction and timely policy responses.Advances in artificial intelligence (AI) and knowledge graph technologies suggest a possible paradigm shift, but realizing this potential calls for a redesign of data infrastructure and institutional frameworks tailored to the five-act structure.Drawing on international best practices, including the European Union (EU) One Substance One Assessment (OSOA) package, the EU Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) regulation, the United States Toxic Substances Control Act (TSCA), Data Catalog Vocabulary-Application Profile (DCAT-AP) metadata standards, and ontology-and knowledge graph-based chemical safety studies, as well as on the structural lessons of past data-integration failures such as the 9/11 information silos, a seven-action strategy is proposed: (1) Designating high-value chemical-safety datasets across the five acts; (2) Redesigning data collection around policy questions; (3) Adopting DCAT-based metadata standards; (4) Developing a chemical-safety domain ontology with cross-act bridges anchored by a common chemical identifier; (5) Transforming incident reports into knowledge graphs; (6) Building AI-based early warning and prioritization systems; and(7) Institutionalizing explainable AI.The seven actions form a logical pipeline from data through metadata, ontology, knowledge graph, AI, and explainability, and are intended to be pursued in a stepwise manner.Realizing this transformation will likely require coordination across the five acts within MCEE, legal safeguards, and sustained investment in data curation and explainable AI.