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The internship program of Universitas Mayjen Sungkono in Begaganlimo Village introduced a training initiative focused on the Development of an Adaptive Learning Platform Based on D-PACK as an effort to enhance the quality of digital learning in Begaganlimo elementary schools. This training was designed to assist teachers in strengthening their ability to design and utilize interactive learning media supported by artificial intelligence (AI). Through this socialization activity, teachers were not only introduced to the use of adaptive technology but also guided to apply it in teaching practices so that the learning process becomes more effective, easier to understand, and more engaging for students. In addition, the socialization activities provided a deeper understanding of how technology can be used and integrated into teaching. Teachers not only learned to operate AI-based applications or devices, but they also gained insight into the pedagogical principles within the DPACK framework, which ensures that technology is applied appropriately. As a result, learning is expected to become easier to understand, more engaging, and capable of increasing students’ participation and motivation in the classroom.

This study aims to explore the shift in students' ethical paradigms regarding the use of Generative Artificial Intelligence (AI) and its relationship to the phenomenon of plagiarism. Using a qualitative approach with the theoretical frameworks of Jean Baudrillard's Simulacra and Pierre Bourdieu's Habitus, this study examines how AI technology is changing the nature of scientific work. The results show the normalization of AI use as a new "digital habitus," where 83% of students consider AI a legitimate research tool, but on the other hand, it creates a condition of "Aesthetics Without Substance." The main findings reveal a reduction in originality where academic honesty is only measured through technical scores (such as Turnitin), rather than intellectual depth. The comparison between authentic and AI-based writing indicates the risk of systemic intellectual atrophy. In conclusion, this study recommends the need for a redesign of educational evaluation systems that focus on processes and verbal dialectics to mitigate the impact of pseudo-competence on college graduates.

The architecture of consumer decision-making has completely changed due to the quick development of recommendation systems based on artificial intelligence (AI). The majority of earlier studies saw algorithms as instruments for forecasting and maximizing preexisting preferences. This study, however, makes a different claim: algorithmic curation actively shapes preferences rather than just reflecting them. This study creates and evaluates a structural model that examines the impact of algorithmic curation intensity on perceived search autonomy, identity resonance, affective evaluation, and the development of initial preferences. The model is based on identity-based consumption theory and the literature on human-AI interaction. The study's findings, which are based on survey data from Generation Z consumers and Structural Equation Modeling (SEM) analysis, demonstrate a contradictory dynamic: algorithmic curation improves identity resonance and directly influences initial preferences while simultaneously decreasing feelings of autonomy. The primary mediating mechanism that links algorithmic exposure to emotional assessment and preference creation is identified as identity resonance. In addition to introducing the concept of algorithmic consumer formation as a new conceptual framework for comprehending consumer behavior in the AI-based digital era, our findings expand the notion of bounded rationality toward algorithmically bounded agency.

This study aims to develop and implement an artificial intelligence-based reading learning application using Deep Learning technology to enhance the literacy skills of eighth-grade junior high school students. The research employed the Kemmis & McTaggart Classroom Action Research model combined with a mixed-methods approach. Data collection involved pretests and posttests, complemented by observations, interviews, and questionnaires. The findings revealed that the use of this application significantly improved students' reading comprehension, question-answering skills, and overall engagement in the learning process. Key features of the application, such as adaptive learning technology, allowed for real-time adjustments to the difficulty level of the material, which catered to each student’s individual learning pace. Additionally, the provision of instant feedback enhanced the learning experience by helping students understand their progress and areas for improvement. These results suggest that the application is an effective tool in fostering literacy development and aligns with the goals of the Independent Curriculum. Consequently, this Deep Learning-based application offers a promising innovation for improving student literacy skills in the digital age. 

The increasing demands of global trade, the risk of transboundary diseases, and the stringency of sanitary and phytosanitary (SPS) standards require a more integrated, transparent, and risk-based national quarantine system. The partial readiness of digital systems and traceability mechanisms has the potential to weaken Indonesia’s food security and export competitiveness toward the vision of Golden Indonesia 2045. This study aims to analyze the strategic role of the quarantine system in national development, identify institutional and technical root problems, and formulate the most concrete and feasible policy alternatives. The method used is policy analysis with a multi-criteria analysis (MCA) approach to compare several solution alternatives based on effectiveness, efficiency, technical aspects, and political feasibility. The results indicate that the digital transformation of the national quarantine system based on traceability and risk-based intelligence is the most superior policy option, as it can simultaneously address data fragmentation, improve service efficiency, and strengthen biosecurity surveillance. This study recommends the development of a Quarantine Super App, full implementation of e-certification, integration with the National Logistics Ecosystem and Customs, and the strengthening of an artificial intelligence-based risk profiling system. Gradual implementation accompanied by performance indicator-based evaluation is key to the success of the policy in enhancing national food security and export competitiveness.

The Society 5.0 era promotes the integration of cyber-physical technology through Artificial Intelligence (AI) and Big Data for human welfare, where digital zakat transformation becomes a crucial strategy to bridge the wide gap between national zakat potential (±IDR 327 trillion) and its actual collection. This study aims to analyze digital zakat transformation strategies in accelerating poverty alleviation in Indonesia within the smart society era. The research method employed is a descriptive qualitative approach using a library research method, analyzing literature from the 2020-2025 period sourced from digital databases. The results indicate that the implementation of digital technologies such as fintech platforms, blockchain, and QRIS significantly enhances transparency, accountability, and muzakki trust, while accelerating fund distribution time by up to 50%. Digital-based productive zakat strategies through MSME empowerment have proven effective in increasing mustahik's average income by up to 100%, facilitating the transformation of mustahik into independent muzakki. This study concludes that digital zakat transformation serves as a primary catalyst for achieving the first pillar of the Sustainable Development Goals (SDGs) (No Poverty), although its success requires national regulatory harmonization and the strengthening of technological infrastructure in rural areas.

The rapid advancement of digital technology in higher education has transformed learning and assessment systems, particularly through the adoption of Learning Management Systems (LMS) and Computer-Based Tests (CBT). However, many university students still face challenges in adapting to digital examination environments due to limited technical experience and digital literacy. This Community Service Activity (PKM) aims to enhance students’ academic readiness and digital competence through the implementation of a Moodle-based LMS at Universitas Murni Teguh. The activity was conducted in the form of an interactive workshop, including system introduction, simulation of CBT, guided practice, and discussion sessions involving university students and lecturers. The results showed a significant improvement in students’ understanding of LMS features, confidence in using CBT systems, and ability to manage digital-based examinations effectively. Active participation and engagement during the sessions reflected high enthusiasm and adaptability toward digital learning transformation. This activity demonstrates that the implementation of LMS Moodle is effective in improving academic preparedness and digital literacy among students, and can serve as a sustainable model for strengthening technology-based learning in higher education institutions.

The rapid advancement of digital technology in higher education has transformed learning and assessment systems, particularly through the adoption of Learning Management Systems (LMS) and Computer-Based Tests (CBT). However, many university students still face challenges in adapting to digital examination environments due to limited technical experience and digital literacy. This Community Service Activity (PKM) aims to enhance students’ academic readiness and digital competence through the implementation of a Moodle-based LMS at Universitas Murni Teguh. The activity was conducted in the form of an interactive workshop, including system introduction, simulation of CBT, guided practice, and discussion sessions involving university students and lecturers. The results showed a significant improvement in students’ understanding of LMS features, confidence in using CBT systems, and ability to manage digital-based examinations effectively. Active participation and engagement during the sessions reflected high enthusiasm and adaptability toward digital learning transformation. This activity demonstrates that the implementation of LMS Moodle is effective in improving academic preparedness and digital literacy among students, and can serve as a sustainable model for strengthening technology-based learning in higher education institutions.

This study explores student agency in Artificial Intelligence (AI)-supported STEM learning environments, addressing a critical gap in existing literature that predominantly focuses on learning outcomes rather than learner-centered processes. Drawing on an interpretive qualitative approach, this research investigates how students experience autonomy, self-regulation, and decision-making when interacting with AI technologies in STEM education. Data were collected from 15 participants engaged in AI-supported learning through in-depth semi-structured interviews, supported by observations and document analysis. The data were analyzed using thematic analysis to identify recurring patterns and meanings related to student agency. The findings reveal that student agency is a dynamic and multidimensional construct shaped by the interplay between technological affordances and learner engagement. Four major themes emerged: enhanced autonomy, development of self-regulated learning, negotiated decision-making, and ambivalent dependency on AI. While AI technologies provide adaptive support that empowers students to take control of their learning, they also introduce the risk of over-reliance, which may reduce cognitive engagement. This study contributes to the theoretical advancement of student agency by conceptualizing it as a spectrum rather than a fixed attribute, highlighting the dual role of AI as both an enabler and a constraint. The findings offer important pedagogical implications for designing AI-supported STEM learning environments that promote active, reflective, and responsible learning. Future research is recommended to explore this phenomenon across diverse contexts and through longitudinal designs.

The development of artificial intelligence (AI) has brought about significant changes in higher education, particularly in how students acquire, understand, and manage academic information. This study aims to deeply understand students' experiences using AI as a learning tool and explore how they interpret the technology's role in supporting academic activities. A qualitative phenomenological approach was used, with three informants selected purposively from various study programs actively using AI. Data were collected through semi-structured interviews, observation, and documentation, then analyzed using data reduction, data presentation, and conclusion drawing. The results show that students view AI as an effective tool for improving learning efficiency, deepening conceptual understanding, and motivating them to be more independent and creative. However, challenges were also identified, including dependence on technology, doubts about the accuracy of information, and concerns about academic ethics violations such as digital plagiarism. Student experiences also demonstrate that AI plays a dual role as both a learning tool and a cognitive partner in the modern learning process. Therefore, the use of AI needs to be directed wisely and accompanied by strong digital literacy to balance technological convenience and the development of critical thinking skills. These findings emphasize the importance of adaptive, humanistic, and ethical learning strategies in the era of digital higher education transformation.

. The rapid development of artificial intelligence (AI) has brought significant changes in various fields, including education and church life. This study aims to examine the dynamics of the formation of prospective catechists in facing the challenges of AI use at the IPI Malang Pastoral College. This study used a qualitative, descriptive approach, through observation and interviews with students as research subjects. The results show that the use of AI has become an integral part of the learning process, helping students understand material, develop academic skills, and enhance creativity in the pastoral field. However, the use of AI also poses challenges in the form of a tendency towards instantaneous attitudes and potential dependency that can hinder critical thinking skills and deep reflection on faith. In the context of pastoral education, the formation of prospective catechists emphasizes not only intellectual aspects but also spirituality, personality, and pastoral sensitivity. Therefore, the wise and proportional use of AI is necessary, while still positioning it as a supporting tool, not a substitute for human roles. The role of lecturers and educational institutions is crucial in guiding students to be able to use technology critically, reflectively, and responsibly. Thus, the integration of technology and faith values ​​is expected to produce prospective catechists who are able to respond to the challenges of the times in a relevant and meaningful manner.

The rapid development of digital technology and artificial intelligence has transformed human life in significant ways while simultaneously fostering a reductionist understanding of the human person based on productivity, efficiency, and technical capability. This phenomenon poses serious challenges to the understanding of human dignity and identity. This study aims to examine the concept of the human person according to Pope Leo XIV in the encyclical Magnifica Humanitas and to analyze its relevance for contemporary society. The research employs a qualitative method with a literature review approach, drawing primarily on the encyclical Magnifica Humanitas and related scholarly sources. The findings reveal that Pope Leo XIV understands the human person as created in the image and likeness of God (imago Dei), possessing an inherent dignity that does not depend on ability, social status, or productivity. This understanding is grounded in Christological and Trinitarian foundations, which affirm the human person as a relational being called to communion with God and others through love and self-giving. The encyclical also offers a critical response to technocratic paradigms, transhumanism, and posthumanism, which risk reducing human beings to technological objects or economic instruments. In the context of contemporary society, Pope Leo XIV’s thought provides a theological and ethical foundation for the development of human-centered technology while reinforcing respect for human dignity in social, educational, economic, and ecclesial life. This study concludes that the anthropological reflection presented in Magnifica Humanitas offers a significant contribution to addressing the human challenges emerging in the digital and artificial intelligence era.

This study aims to analyze the implementation of Artificial Intelligence (AI)-based learning media and its implications for student learning motivation in Islamic Religious Education (PAI). This study uses a systematic literature review approach by examining various reputable scientific articles discussing the integration of AI in education and the dynamics of learning motivation in the context of PAI. The results of the study indicate that the use of AI through adaptive learning systems, educational chatbots, gamification, and learning analytics can increase the effectiveness, personalization, and interactivity of learning. This implementation has a positive impact on cognitive motivation through increased conceptual understanding, affective motivation through active participation and emotional engagement, and spiritual motivation through strengthening reflection and internalization of Islamic values. However, ethical challenges, the risk of depersonalization of the teacher's role, and inequality in digital access are crucial issues that require policy attention and human-centered pedagogical design. Theoretically, this study offers an integrative conceptual framework that combines technological innovation with Islamic educational epistemology. Practically, the results of this study provide recommendations for teachers, schools, and policymakers to develop AI-based PAI learning models that are adaptive, ethical, and oriented towards character building.

The rapid development of digital technology in the 21st century has brought significant changes to various aspects of life, including education. One of the emerging innovations in the educational field is the use of Artificial Intelligence (AI) as a tool to support the learning process. The integration of AI in history education offers new opportunities to create more interactive, adaptive, and contextual learning experiences through access to various digital historical sources. This study aims to analyze the integration of Artificial Intelligence in history learning and its implications for improving students’ digital historical literacy. This research uses a qualitative approach with a descriptive method through a literature study by reviewing various scientific articles related to the use of AI in education and digital literacy in history learning. The findings indicate that the use of Artificial Intelligence can assist students in accessing, analyzing, and understanding various digital historical sources more broadly through interactive media such as online archives, historical simulations, and technology-based learning platforms. In addition, the integration of AI can increase student engagement in the learning process, encourage critical thinking skills, and support more personalized learning based on students’ needs and abilities. However, the implementation of this technology must be accompanied by the strengthening of digital historical literacy as well as the application of academic integrity, ethical principles, and responsible use of technology. Therefore, the proper integration of Artificial Intelligence can serve as an innovative strategy to enhance the quality of history education in the digital era.

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.

This study examines the application of Transparent Artificial Intelligence (AI) for fraud detection in public welfare programs using publicly available administrative data. Persistent challenges in welfare governance such as misallocation, fraud, and data inaccuracy necessitate analytical frameworks that are both effective and explainable. The research aims to design and evaluate an interpretable anomaly detection system capable of identifying irregularities in welfare distribution while maintaining transparency and accountability. Methodologically, the study employs two unsupervised models Isolation Forest and Local Outlier Factor (LOF) to detect anomalies in sub-district-level welfare data, incorporating features such as population size, number of beneficiaries, and coverage ratio. An Explainable AI (XAI) framework integrating surrogate Random Forests, Permutation Feature Importance (PFI), and local linear surrogates (LIME-like) is applied to ensure interpretability of both global and local model behaviors. Findings reveal that receivers per 1000 population and percentage coverage are dominant determinants of anomaly scores. Fifteen administrative units were flagged for potential inconsistencies suggesting over- or under-reporting of beneficiaries. Cross-validation between IF and LOF models confirmed consistency in identifying anomalous regions. The integrated XAI explanations enhance transparency, enabling policymakers and auditors to trace the rationale behind detected anomalies. In conclusion, the proposed Transparent AI framework demonstrates that combining anomaly detection with interpretability tools can strengthen accountability and fairness in welfare administration. It offers a reproducible, ethical, and data-driven approach to social program monitoring, reinforcing public trust and supporting responsible AI governance.

Explainable artificial intelligence (XAI) has become a critical requirement in cybersecurity due to the high-stakes nature of security decision-making and the limitations of black-box learning models. This study investigates the construction of an explainable cybersecurity knowledge representation by leveraging standardized terminology from the NIST cybersecurity glossary. The primary problem addressed is the lack of transparent and semantically grounded reasoning mechanisms in existing AI-driven cybersecurity systems, which limits trust, accountability, and analyst adoption. To address this challenge, we propose a NIST-based semantic knowledge graph that embeds explainability directly into its ontology structure and reasoning process. The proposed framework systematically extracts definitional entities and relations from NIST glossary entries to construct a domain ontology and a multi-relational knowledge graph. A rule-based semantic relation extraction method is employed to ensure faithful, interpretable, and reproducible reasoning paths. The resulting knowledge graph contains over 3,000 cybersecurity concepts and approximately 27,000 semantic relations, covering hierarchical, associative, dependency, and mitigation semantics. Experimental evaluation demonstrates that the proposed approach achieves a high level of explainability, with 92.4% of reasoning outcomes being fully traceable and only 1.4% classified as non-traceable. Most explainable reasoning paths are limited to two or three hops, indicating an effective balance between inferential depth and human interpretability. Structural analysis further confirms the presence of meaningful hub concepts that support multi-hop semantic inference. These results confirm that ontology-driven, standard-based knowledge graphs provide a robust foundation for explainable cybersecurity intelligence. The study concludes that explainability-by-design, grounded in authoritative standards, offers a viable and trustworthy alternative to opaque AI models for cybersecurity applications.

This study investigates the role of Artificial Intelligence (AI) tools, specifically ChatGPT and Jenni AI, in supporting academic integrity through accurate citation generation. Employing a descriptive qualitative method, the research involved 30 undergraduate students who evaluated AI-generated citations using Fishman’s (2014) Academic Integrity Theory and Smith’s (2020) Citation Accuracy Framework. Data were collected through questionnaires assessing students’ perceptions of reliability, ethical responsibility, and accuracy in AI-assisted citation practices. Quantitative analysis revealed that Honesty and Accountability were the most dominant values (22.58% each), followed by Fairness and Respect (19.35% each), Trust (12.90%), and Courage (3.23%). Qualitative findings showed that students recognized AI’s potential to enhance writing efficiency but emphasized the need for human verification to ensure factual correctness and ethical compliance. Comparatively, Jenni AI demonstrated greater consistency and citation verification than ChatGPT, which exhibited more frequent fabrication and inaccuracy. The study concludes that while AI tools can enhance academic productivity, maintaining academic integrity still requires critical human oversight, ethical awareness, and adherence to scholarly honesty and accountability.

This study aims to investigate the skill gaps experienced by bachelor’s degree graduates in Indonesia when facing the dual demands of Industry 4.0 and Society 5.0. The method applied in this research is a systematic literature review, synthesizing findings from 21 scientific articles focusing on the dynamics of skill gaps and the effectiveness of higher education interventions in Indonesia. The study findings reveal two main categories of competency gaps. In the context of Industry 4.0, the most significant deficiencies are observed in specific hard skills, particularly data literacy, artificial intelligence proficiency, and automation across various sectors. Meanwhile, within the Society 5.0 dimension, deeper gaps emerge in soft skills and human-centered competencies, including complex problem-solving, emotional intelligence, creativity, and environmentally sustainable skills. These human-centered skill gaps play a critical role in enhancing graduate value as AI technologies increasingly replace routine tasks. Although the Merdeka Belajar Kampus Merdeka program shows positive outcomes in improving sustainability skills and digital certification, it has not fully succeeded in driving fundamental transformation of higher education learning outcomes, limiting alignment with sustainability principles and the Society 5.0 approach. It can be concluded that Indonesian higher education faces the challenge of undertaking fundamental curriculum reform to integrate human-centered competencies as a foundation for preparing future human resources.

This study aims to provide a comprehensive analysis of Predictive Policing and its implications for law enforcement transformation in Indonesia, based on an extensive review of its global applications, benefits, and challenges. The study uses qualitative literature and international case study review methods to assess the impact and complexity of implementing digital technologies such as artificial intelligence (AI), machine learning, and big data analytics within a Predictive Policing framework. The results of this review highlight that while Predictive Policing offers significant potential for proactive crime prevention and increased operational efficiency, its implementation is consistently fraught with critical legal, ethical, and technical challenges, including regulatory gaps, risks of algorithmic bias, and data privacy concerns, which are particularly relevant to Indonesia. The findings underscore that public trust and police legitimacy in the context of adopting such technologies are strongly influenced by transparency, strong accountability mechanisms, and community involvement in shaping their use. This study contributes to the growing discourse on digital policing in developing countries and culminates in practical policy recommendations designed to guide the Indonesian police towards the development and implementation of Predictive Policing models that are effective, efficient, and fundamentally respectful of legal and human rights principles.