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Agentic artificial intelligence (AI) systems capable of autonomous goal-directed behavior, multi-step planning, tool use, multi-agent coordination, and iterative self-correction represent a transition from passive clinical AI tools toward systems that can participate in complex healthcare workflows. However, empirical evidence remains fragmented across clinical decision support, patient monitoring, and administrative applications, and no systematic synthesis has evaluated which agentic principles have been technically demonstrated and which have accumulated sufficient evidence to support responsible clinical deployment. We conducted a PRISMA-informed systematic review of peer-reviewed empirical studies published between January 2025 and April 2026. Searches across five bibliographic databases and Google Scholar, supplemented by citation tracking, identified 443 unique records for screening, of which 25 met the predefined PICOS and quality appraisal criteria. Evidence was synthesized using an evidence-informed seven-principle framework derived from the integration of agentic AI, clinical AI, and healthcare governance literature. This framework provides a structured lens for examining how agentic principles are evaluated individually and in combination, enabling a deployment-readiness perspective that extends beyond capability-focused assessments alone. The evidence base was concentrated on technical capability principles, whereas human oversight, safety, compliance, and equity-related evaluation received comparatively limited attention. Most studies remained at the laboratory, benchmark, or proof-of-concept stage, and none reported demographic-stratified performance outcomes. Overall, the findings suggest a structural asymmetry in agentic healthcare AI: empirical research is advancing agentic capabilities more rapidly than it is generating evidence for the oversight, safety, equity, and governance mechanisms required for responsible clinical translation.

This study aims to reconstruct a Greek Exegesis learning model based on formative hermeneutics by integrating syntactic analysis with the spiritual transformation of theology students. Traditional Greek instruction often emphasizes technical grammar and textual translation, thereby creating a gap between academic rigor, interpretive sensitivity, and spiritual formation. To address this issue, the present research develops and evaluates an instructional model that positions grammatical inquiry not merely as a linguistic exercise, but as a formative process for understanding Scripture responsibly. This research employs a quantitative descriptive-correlational design involving 30 theology students as participants. Data were collected through syntactic analysis tests and Likert-scale instruments measuring hermeneutical awareness and spiritual disposition. The results show that the implementation of the formative hermeneutical model achieved a high effectiveness level of 81.7%. Students’ syntactic competence reached 78%, which falls into the good category, while spiritual transformation attained 84.8%, categorized as very good. These findings indicate that the model significantly improves both analytical competence and spiritual development. This study proposes an integrative pedagogical framework in which syntactic analysis functions as a formative hermeneutical practice that shapes theological virtues, interpretive responsibility, faith maturity, and holistic ministerial readiness among theology students within diverse contemporary theological education contexts and ministries.

Classroom Action Research (CAR) is a widely used approach to improve teaching practices through iterative cycles of planning, action, observation, and reflection. Despite its popularity, many studies report methodological weaknesses, particularly in ensuring coherence between research problem formulation, objectives, and success indicators. This study aims to examine the alignment among these three key components in CAR studies using a Systematic Literature Review (SLR) approach. Data were collected from peer-reviewed articles indexed in Scopus, ScienceDirect, Taylor & Francis Online, ERIC, and Google Scholar, published between 2015 and 2025. The study selection followed PRISMA 2020 guidelines, including identification, screening, eligibility assessment, and final inclusion of relevant studies. A qualitative thematic synthesis was applied to analyze patterns in the relationship between problem formulation, research objectives, and success indicators. The findings show that CAR problem formulations are generally contextual and based on classroom issues, while research objectives function as operational translations of these problems. Success indicators are used as measurable criteria to evaluate the effectiveness of interventions. However, inconsistencies are still found in several studies, such as misalignment between objectives and problems or poorly defined success indicators. Overall, the study emphasizes the importance of conceptual coherence to improve the quality and credibility of CAR research designs.

This study examines contextual discipleship as a strategic approach to Christian education in a multicultural society. It employs a convergent mixed-methods design to obtain a comprehensive understanding of how discipleship contributes to students’ spiritual growth and character formation. Quantitative data were gathered through structured questionnaires, while qualitative data were collected through in-depth interviews and focus group discussions with relevant participants. The findings show that contextual discipleship significantly strengthens students’ understanding of faith, promotes tolerance and respect for diversity, encourages social engagement, and helps students respond wisely to challenges arising from technological development and digital culture. Contextual discipleship becomes effective when it is implemented consistently, supported by relational mentoring, and grounded in the translation of Gospel values into the learners’ real-life social context. The study concludes that contextual discipleship is a strategic and transformative model for developing holistic Christian education that is spiritually formative, socially responsive, and relevant to multicultural realities. Therefore, this approach can strengthen Christian educational practice and equip learners to live faithfully, responsibly, and constructively within diverse contemporary communities amid ongoing social, cultural, and increasingly digital transformation today.

This study aims to develop a sentiment classification method for YouTube user comments related to the game Love and Deepspace using the Naïve Bayes algorithm, focusing on improving the text data processing and understanding user perceptions. Comment data were collected through scraping from YouTube videos, followed by preprocessing including text cleaning, normalization, stopword removal, stemming, and translation into English. Initial labeling was conducted using TextBlob, then the data were randomly sampled for training the Naïve Bayes model. Evaluation involved comparing sentiment distributions and visualization using Word Cloud and bar charts. The Naïve Bayes model achieved an accuracy of 77.36% in sentiment classification. The sentiment distribution shows differences between TextBlob (positive: 1,011, neutral: 1,312, negative: 575) and Naïve Bayes (positive: 901, neutral: 1,627, negative: 370), with Naïve Bayes being more conservative. The Word Cloud visualization identifies dominant words such as "bang," "game," and "main," while the bar chart shows the largest proportion of neutral sentiment. Naïve Bayes is effective for sentiment classification on informal comment data, with significant differences from rule-based methods like TextBlob. This research contributes to the development of text data processing techniques and user perception analysis, as well as opening up optimization opportunities with other algorithms like SVM for better accuracy.

 This study aims to analyze the use of Google Translate in the process of learning English, highlighting its benefits, limitations, and impact on students’ learning quality. This research employs a qualitative approach using a library research method, where data are obtained from various literature sources such as books, scholarly articles, and relevant online publications. The findings indicate that Google Translate is widely used by students as a tool to quickly and practically understand English texts. However, this high level of usage also leads to several issues, including the inaccuracy of translations in certain contexts, increased dependency on technology, and a limited understanding of language structure. Nevertheless, Google Translate still provides advantages in improving learning efficiency and access to global knowledge resources. Therefore, the use of this technology needs to be guided wisely so that it supports the learning process without reducing students’ cognitive engagement. This study is expected to contribute to a better understanding of the role of technology in language learning in the digital era and to serve as a reference for educators and students in utilizing technology more effectively.  

The rapid proliferation of learning analytics, business intelligence (BI), artificial intelligence (AI), and generative AI (GenAI) has significantly expanded universities’ ability to collect, integrate, analyze, and operationalize institutional data. However, despite advances in predictive analytics, dashboards, and AI-driven systems, the translation of analytical outputs into consistent and accountable institutional decision-making remains uneven. This systematic literature review synthesizes contemporary research on analytics-enabled decision-making in higher education with the aim of moving beyond dashboard-centric perspectives toward a socio-technical and computing-oriented understanding of how data are transformed into institutional actions and outcomes. Guided by the PRISMA framework, the review synthesizes evidence across four interconnected dimensions: data ecosystems and learning analytics foundations; analytics capability, BI adoption, and digital readiness; AI and advanced analytics for decision support; and human-in-the-loop (HITL) decision routines and institutional outcomes. The findings show that predictive performance and analytical sophistication alone do not guarantee decision value. Instead, effective analytics-enabled decision-making depends on interoperable data ecosystems, organizational analytics capability, governance mechanisms, explainability, and sustained human oversight. Based on these findings, this review contributes a computing-oriented decision-intelligence framework that conceptualizes analytics-enabled decision-making as an end-to-end socio-technical pipeline linking heterogeneous data acquisition, integration, feature construction, analytical modeling, explainability, human validation, governance, and feedback-based refinement. By integrating learning analytics, BI, AI, GenAI, and HITL mechanisms within a unified framework, the review clarifies how universities can move beyond dashboard-based reporting toward accountable, adaptive, and institutionally actionable decision-support infrastructures.

Communication is an essential human right, yet a significant communication gap persists between individuals with sensory disabilities, specifically the deaf and speech-impaired, and the general public. While many technological solutions have been proposed to translate sign language, existing models primarily rely on heavy deep learning architectures such as Convolutional Neural Networks (CNN) or Recurrent Neural Networks (RNN/LSTM). These models often demand high computational power, leading to latency and limiting real-time application on standard devices. This study proposes a lightweight, fast, and highly responsive sign language translation system specifically designed to recognize static alphabets (A-Z) and single-character air writing. The system utilizes MediaPipe for hand tracking, where feature extraction is intelligently processed by calculating the relative spatial coordinates of fingertips to the wrist, reducing dependency on raw camera coordinates. Classification is performed using a Support Vector Machine (SVM) with a Radial Basis Function (RBF) kernel, prioritizing computational efficiency without sacrificing accuracy. To enhance user experience, the system introduces three key novelties: smart relative feature extraction, an anti-duplication hold system with a 1-second timer to prevent input spamming, and a non-blocking multithreaded audio execution (Daemon Thread) utilizing Google Text-to-Speech (gTTS), ensuring the webcam feed remains fluid during audio playback. Additionally, an alternative air-writing mode is integrated, utilizing geometric heuristics and PyTesseract OCR to read single drawn letters in the air. The results indicate that the proposed system operates swiftly and efficiently, bridging the communication barrier with a hardware-friendly approach.

Sentiment analysis for Indonesian regional languages faces two persistent challenges: labeled training data is extremely limited for most regional varieties, and transformer models pre-trained on Bahasa Indonesia do not generalize reliably to languages with substantially different morphological structures. Prior work on the NusaX benchmark has primarily relied on direct fine-tuning, treating each regional language independently and without exploiting linguistic proximity between related languages as a transfer signal. This paper proposes Language-Similarity-Guided Transfer (LSGT), a sequential fine-tuning strategy that first adapts a pre-trained model to a pivot language selected using character trigram similarity, followed by fine-tuning on the target language. Four transformer models are evaluated across all 12 NusaX languages using the official train/validation/test splits: IndoBERT, NusaBERT, mBERT, and XLM-R. Performance is evaluated using four metrics: accuracy, macro F1, macro precision, and macro recall. Experimental results show that LSGT improves macro F1 in 44 of 48 model-language combinations, demonstrating that the fine-tuning strategy itself is a major factor in low-resource cross-lingual sentiment classification. XLM-R benefits most strongly from LSGT, achieving an average improvement of +0.137 macro F1 and a peak gain of +0.298 on Madurese. SHAP-based token attribution analysis further reveals that predictions rely heavily on named entities and domain-specific nouns rather than sentiment-bearing vocabulary, indicating a dataset-level bias inherited from the original SmSA corpus and propagated through the NusaX translation pipeline.

With the advancement of technology, artificial intelligence tools have begun to emerge in the field of education, such as ChatGPT, which is now used by some teachers at Pondok Modern Darussalam Gontor to compare translations of Indonesian texts into Arabic. Although there are recognized reference books for correcting translations, there are discrepancies between machine translations, including errors in terminology selection and contradictions with the educational context. This raises questions about the accuracy of ChatGPT’s translations, making this study necessary to analyze and evaluate terminological errors. This study aims to analyze the translation errors made by ChatGPT when translating Indonesian articles into Arabic. This study also seeks to evaluate the quality of these translations based on the classification of terminological errors and Ali Al-Qasimi’s theory regarding the evaluation of terminology in terms of acceptance and rejection. This study employs a qualitative approach using content analysis, in which ChatGPT’s translations are compared with those in reference books, with reference to specialized Arabic dictionaries. Subsequently, the data was analyzed to identify error patterns and their acceptance levels within an educational context. The results indicate that translation errors can be classified into four main patterns: phonetic transfer, conceptual errors, semantic shifts, and inaccurate synonyms.

This study investigates the role of Google Translate in supporting English vocabulary learning among junior high school students in the digital era. The research employs a qualitative approach involving seventh-grade students at SMP Negeri 19 Medan. Data were collected through pre-test and post-test, classroom observation, interviews, and documentation. The findings reveal that Google Translate significantly helps students understand vocabulary more quickly, improves their learning efficiency, and increases their confidence in completing English tasks. Students also demonstrate greater autonomy in exploring new vocabulary independently. However, the study also identifies challenges, particularly students’ tendency to rely excessively on direct translation without fully understanding context, which may lead to superficial learning. Therefore, teacher guidance is essential to ensure the effective and critical use of this tool. The study concludes that Google Translate can serve as a valuable learning resource when integrated appropriately into classroom instruction. The findings contribute to the development of technology-enhanced language learning and provide practical implications for teachers in utilizing digital tools effectively.

This study aims to describe students’ mathematical creative thinking skills through the use of Project-Based Learning (PBL)-based Student Worksheets (LKPD) on the topic of translation via a tessellation project. The study employed a quantitative descriptive approach supported by qualitative data in the form of documentation of students’ project outcomes. The research subjects consisted of 20 students divided into 10 groups. Data were collected through the assessment of tessellation projects and documentation of students’ work, which were analyzed based on indicators of mathematical creative thinking skills, namely fluency, flexibility, originality, and elaboration. The results of the study indicate that students were able to generate various ideas in designing tessellation patterns, employ diverse pattern-arrangement strategies, and develop works demonstrating a visual understanding of the concept of translation. Elaboration skills were evident in the development of details, neatness, and refinement of the works, while originality was evident in the variety of motifs and designs produced by each group. Project-based learning provided students with the opportunity to explore ideas, collaborate in groups, and connect mathematical concepts with tangible products. The implementation of Project-Based Learning (PBL) in local curriculum materials through a tessellation project also helps students understand the concept of translation more concretely through activities involving the design and creation of repeating patterns. Overall, this approach facilitates the development of students’ creative mathematical thinking skills regarding the topic of translation and provides a more active, meaningful, and student-centered learning experience.

This study analyzes the integration of turoth (classical Islamic scholarship) and digital media in moderate Islamic education through Gus Kautsar’s YouTube lectures. Amid concerns that religious digitalization may simplify Islamic teachings and reduce epistemological depth, this research examines whether turoth transmission in digital platforms maintains traditional scholarly structures. Using a qualitative approach with thematic content analysis, this study analyzes three classical text-based lecture videos published between January and March 2024. The findings show that turoth is represented through direct reading of Arabic classical texts, hierarchical argument structures (Qur’an–Hadith–Ulama), references to madhhab authorities, and contextualization of contemporary digital issues. The adaptation identified is pedagogical rather than epistemological, reflected in episodic segmentation, communicative translation, and contextual illustrations suited to Generation Z’s learning patterns. Furthermore, moderate Islamic education in the analyzed content emerges not through normative slogans, but through systematic engagement with classical texts. This study demonstrates that digital media can function as a medium for recontextualizing Islamic tradition while fostering moderate religious values among Generation Z in the digital era.

This research explores the translation of imported teaching theories into practice by secondary school teachers in Kenya, Tanzania and Uganda. The focus of the problem is the disconnect between global teaching theories and practices in East Africa. Despite ongoing promotion of learner-centered, competency-based and constructivist pedagogies by governments and international entities, teaching practices have not changed or have only changed partially. This research aims to unravel the meanings, interpretations, resistance and transformations teachers make to imported teaching theories in their sociocultural and institutional contexts. This study used a qualitative comparative study design, with 72 teachers in 24 secondary schools. Semi-structured interviews, classroom observations and documents were analysed. The study shows that teachers engage in pedagogical translation in the following four ways: selective incorporation, syncretic blending, instrumental appropriation and transformative adaptation. These modes involve factors including examination, language policy, material resources and teacher experience. The research finds pedagogical translation is not an "implementation failure" but a process of adaptation and innovation. It suggests that education policies should be mindful of teacher agency, assessment practices to support pedagogical practices, and context-sensitive pedagogies.

The increase in the number of elderly residents demands the provision of residential facilities that not only meet physical needs, but also support the psychological and social well-being of their users. This study aims to formulate the concept of behavioral architecture-based nursing home design by focusing on the relationship between elderly activity patterns, privacy levels, and spatial relationships of space in the local context of Indonesia. This study uses a qualitative-descriptive approach in the framework of architectural design, with data collection through observation of elderly activities, site analysis, and documentation studies. Activity data was analyzed to identify space needs and usage patterns, then synthesized with site characteristics to formulate the concepts of zoning, circulation, and behavior-based spatial relationships. The results of the study show that the activities of the elderly form a layered behavioral structure, including residential and health activities as primary needs as well as social, productive, and educational activities as support for psychosocial welfare. Hierarchically arranged space zoning based on privacy levels has been proven to improve the readability of the space, sense of security, and comfort of the elderly. The integration of green open spaces as part of the activity system also strengthens support for light physical activity and social interaction. This study confirms that the application of behavioral architecture allows the translation of data on elderly behavior and site conditions into a contextual, humanist, and quality-of-life-oriented design concept. These findings provide practical implications for designers and policymakers in the development of sustainable elderly housing.

This study investigates the role of artificial intelligence (AI) in enhancing language learning, with a focus on five key applications: automatic text analysis, personalized learning, adaptive feedback, language error detection, and automatic translation. The study addresses the challenge of integrating AI effectively in educational contexts while balancing technological potential with pedagogical guidance. The objective is to provide a comprehensive understanding of how AI tools contribute to more adaptive, efficient, and engaging language learning experiences. A systematic literature review method was employed, selecting and critically analyzing studies published between 2020 and 2025 that examined AI-assisted language learning strategies. The findings indicate that automatic text analysis supports comprehension monitoring and guided learning, while personalized learning adapts content to individual learner needs, enhancing motivation and retention. Adaptive feedback delivers immediate, targeted guidance that fosters accuracy and self-regulated learning, and language error detection tools enable learners to identify and correct grammatical and lexical mistakes, promoting metalinguistic awareness. Automatic translation broadens access to authentic texts and cross-cultural materials, supporting comprehension and independent learning. Synthesizing these findings highlights the transformative potential of AI to improve learning outcomes while also revealing challenges such as tool reliability, ethical considerations, and the need for teacher oversight. The study concludes that AI, when thoughtfully integrated, complements instruction, enhances learner engagement, and supports differentiated and data-driven teaching strategies, providing valuable insights for language educators and guiding future research on AI-enabled language learning.

Inconsistent terminology across cybersecurity frameworks undermines global governance and interoperability. The National Institute of Standards and Technology Cybersecurity Framework (NIST CSF 2.0) and ISO/IEC 27001:2022 share similar objectives but diverge semantically in defining risk, control, and resilience. This semantic gap causes difficulties in compliance mapping and automated policy translation. Research Objectives: This study aims to analyze the semantic similarity and divergence between NIST and ISO/IEC 27000 terminologies, identify conceptual structures influencing interoperability, and propose an AI-assisted foundation for harmonizing cybersecurity language globally. Methodology: A mixed-method semantic comparative design integrates Natural Language Processing (NLP) and ontology mapping. Using the nist_glossary.csv dataset and ISO vocabularies, terms were normalized and analyzed via cosine similarity using sentence-transformer embeddings. Ontological alignment was visualized through the Semantic Threat Graph (STG) and validated by certified experts using Cohen’s Kappa reliability tests. Results: From 672 term pairs, results show 40.9% high semantic equivalence, 38.8% partial overlap, and 20.3% semantic divergence. Strongest alignment appears in “Protect” and “Identify” domains, while divergences occur in governance and recovery-related terms. Ontology mapping revealed three conceptual clusters—Risk Governance, Technical Safeguards, and Organizational Readiness. Conclusions: Findings confirm a 79.7% total semantic alignment, indicating strong potential for harmonizing global cybersecurity standards. The study contributes an empirical model combining computational linguistics and AI-based ontology mapping to establish semantic interoperability, enabling unified cybersecurity governance and AI-driven compliance automation. Keywords: Semantic Interoperability; Ontology Mapping; Cybersecurity Frameworks; Terminology Alignment; AI Harmonization

Artificial Intelligence (AI) is increasingly recognized as a transformative enabler of early disease detection, with the potential to improve diagnostic accuracy, support predictive risk stratification, and advance preventive healthcare. Despite rapid methodological progress, many existing reviews remain performance-centric, offering limited insight into generalizability, ethical governance, and real-world implementation constraints. This paper presents a narrative and integrative review with an adoption-focused, translational perspective, synthesizing recent developments in AI-driven early disease detection across oncology, cardiology, neurology, and infectious disease surveillance. Drawing on peer-reviewed literature published primarily between 2016 and 2025, the review examines reported performance gains alongside persistent limitations related to data heterogeneity, population bias, explainability, and regulatory fragmentation. Through cross-sectional synthesis, we identify three recurring gaps in prior reviews: (i) overgeneralization of AI’s diagnostic superiority, (ii) insufficient consideration of ethical and legal accountability, and (iii) a lack of actionable guidance for scalable clinical implementation. Integrating technical, ethical, and policy dimensions into a unified conceptual framework, this review demonstrates that while AI systems can consistently enhance diagnostic accuracy and early risk stratification in well-defined tasks, sustained clinical adoption depends on aligning technical performance with governance readiness, interpretability, and workflow integration. The analysis further highlights how implementation mechanisms—such as explainable AI, continuous post-deployment monitoring, and clinician-centered deployment strategies—mediate the translation of algorithmic innovation into real-world healthcare impact. Overall, this review provides a critical reference for researchers, clinicians, and policymakers seeking to translate AI innovation into safe, equitable, and trustworthy clinical practice.

This research is motivated by the importance of understanding the Heart Sutra within the framework of Humanistic Buddhism, especially for students of Buddhist religious education. The main problem lies in the limited understanding of the relevance of the Heart Sutra to humanistic values ​​in the context of modern life. This study aims to explain the relevance of the Heart Sutra teachings in building applicable spiritual, ethical, and social awareness. The method used is a literature study with a descriptive qualitative approach through text analysis and academic literature review. Data were obtained from primary sources in the form of translations of the Heart Sutra and secondary sources including journals, books, and scientific articles related to Humanistic Buddhism. The results of the study show that the teachings of sunyata (emptiness) in the Heart Sutra not only have profound philosophical meaning, but also provide practical implications in everyday life. The Heart Sutra is able to strengthen the philosophical foundation of Humanistic Buddhism, foster an attitude of compassion, wisdom, and tolerance, and serve as a relevant ethical guideline in facing the dynamics and challenges of modern society.

The thinking process carried out by teachers occurs in three phases, namely before learning, during learning, and after learning. The results of the analysis of these processes have the potential to produce innovative didactic designs, and these three processes can be formulated as a series of steps to produce new didactic designs. This series of activities is formulated as Didactic Design Research (DDR). Didactic Design Research basically consists of three stages, namely: (1) analysis of the didactic situation before learning which is in the form of a Hypothetical Didactic Design including ADP, 2) metapedidactic analysis, and 3) retrospective analysis, namely an analysis that links the results of the analysis of the hypothetical didactic situation with the results of the metapedidactic analysis. From these three stages, an Empirical Didactic Design will be obtained which is not closed to being continuously refined through the three stages of DDR.