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The rapid development of digital technology requires teachers to possess competencies that are relevant to modern learning needs. This necessity underlies the focus of this community service program, namely transforming teacher competencies through character-based learning innovation supported by Artificial Intelligence (AI). The program was conducted at Madrasah Tsanawiyah Nurul Huda Muaro Jambi with the aim of strengthening teachers’ skills in utilizing AI as a tool to support interactive learning while instilling character values in students. The method included socialization, training, and direct mentoring in the practical application of AI-based learning media in the classroom. The results showed significant improvements in teachers’ ability to design technology-based learning materials, a better understanding of integrating character education, and increased motivation to innovate. Furthermore, the program fostered a more engaging learning atmosphere aligned with the needs of the digital generation. Therefore, the transformation of teacher competencies through AI not only enhances teacher professionalism but also plays a crucial role in shaping students with strong character amid technological advancement.

Digital transformation and advances in artificial intelligence (AI) have fundamentally changed the demands of the workplace, creating a gap between graduate competencies and industry needs. This study aims to evaluate the effectiveness of AI Agent-based career adaptability psychoeducation as a community empowerment strategy to improve the work readiness of high school/vocational school and university graduates. The study design used a descriptive-interventional approach with 27 participants who participated in a four-week online training. Data were collected through a pre-post survey using the Career Adapt-Abilities Scale (CAAS) and qualitative observations during the training. The results of the Wilcoxon Signed-Rank test showed a significant increase in career adaptability scores (Z = –4.543, p < .001), with all participants experiencing increased career adaptability. Observations showed that participants became more confident, reflective, and proactive in designing their career directions after interacting with the AI ​​Agent. These findings indicate that psychoeducational interventions integrated with intelligent technology can strengthen the adaptive capacity and work readiness of the younger generation. Theoretically, this study expands the application of the career adaptability concept in the context of AI-based learning; In practice, the results provide a relevant community empowerment model for educational and employment institutions in the era of digital disruption.

Digital transformation and advances in artificial intelligence (AI) have fundamentally changed the demands of the workplace, creating a gap between graduate competencies and industry needs. This study aims to evaluate the effectiveness of AI Agent-based career adaptability psychoeducation as a community empowerment strategy to improve the work readiness of high school/vocational school and university graduates. The study design used a descriptive-interventional approach with 27 participants who participated in a four-week online training. Data were collected through a pre-post survey using the Career Adapt-Abilities Scale (CAAS) and qualitative observations during the training. The results of the Wilcoxon Signed-Rank test showed a significant increase in career adaptability scores (Z = –4.543, p < .001), with all participants experiencing increased career adaptability. Observations showed that participants became more confident, reflective, and proactive in designing their career directions after interacting with the AI ​​Agent. These findings indicate that psychoeducational interventions integrated with intelligent technology can strengthen the adaptive capacity and work readiness of the younger generation. Theoretically, this study expands the application of the career adaptability concept in the context of AI-based learning; In practice, the results provide a relevant community empowerment model for educational and employment institutions in the era of digital disruption.

This study aims to evaluate the effectiveness of the Artificial Intelligence-Based Learning Media Development Program (P3MP-AI) in enhancing teachers’ technological and pedagogical competencies at SMK Muhammadiyah 2 Malang. The program employed a descriptive approach using both quantitative and qualitative methods, including pre-test and post-test assessments, as well as direct observation of the training process. A total of 30 teachers from various disciplines actively participated in the program conducted on August 12, 2025. The evaluation results revealed an increase in the participants’ average scores from 100 to 130 out of a maximum of 150, indicating a significant improvement in their understanding of AI concepts and applications in education. Beyond competency enhancement, the training also fostered teachers’ confidence, creativity, and ability to integrate AI-based tools into interactive learning media. However, several challenges were identified, such as limited technological resources and time constraints in classroom implementation. Overall, this program has made a tangible contribution to strengthening teachers’ digital literacy and can serve as a replicable professional development model for other vocational schools seeking to advance AI-based educational transformation.

Since 2022, the application of the Internet of Things (IoT) in the healthcare sector has grown significantly, marked by the increasing adoption of wearable technology, artificial intelligence (AI), machine learning (ML), and blockchain integration. Research highlights India and China as leading contributors in this domain. IoT enables real-time monitoring of chronic diseases, tracking of patient vital signs, and detection of health protocol compliance. Integrated systems such as Monit4Healthy and RADAR-IoT support personalized medical recommendations and cross-platform interoperability. However, key challenges persist, including patient data privacy and security, system interoperability issues, data fragmentation, and barriers to user acceptance due to cost, digital literacy, and device comfort. Proposed solutions include blockchain for secure data sharing, adaptive congestion control for network performance, and user training to improve technology adoption. Therefore, successful IoT deployment in healthcare requires a comprehensive approach that addresses technological, social, ethical, and sustainability aspects to achieve an effective and inclusive transformation of health services.

The growing interest in learning the Japanese language in Indonesia, driven by popular culture such as anime, creates a need to understand the effectiveness of different learning media. The non-uniform effectiveness of media for each individual poses a major challenge. Therefore, this study aims to analyze the effectiveness of both anime and textbooks by segmenting learner profiles and identifying key determinants of success using an artificial intelligence approach. This research employed a quantitative method through a questionnaire survey of 120 respondents. The data were analyzed in two stages: the K-Means Clustering algorithm was used to group respondents into learner profiles, and the Decision Tree algorithm was used to identify the most significant factors that differentiate these profiles. The analysis successfully identified three distinct learner profiles: "Intensive & Adaptive Learner," "Flexible Learner," and "Passive Learner." The decision tree revealed that the perception of textbook effectiveness and the frequency of anime use are the strongest predictors in determining a learner's profile, more so than theoretical learning style preferences. It is concluded that media effectiveness is highly dependent on the learner's behavioral and perceptual profile, which underscores the importance of a personalized approach in language education technology.

This study evaluates the efficiency of two artificial intelligence models, DeepSeek and OpenAI, in generating code for algorithmic systems. Efficiency is assessed through execution speed, code accuracy, and the number of code characters produced. Data were collected from 100 tests covering search, sorting, graph, dynamic programming, optimization, data processing, text, and machine learning algorithms. The objective is to compare the performance of both models to support the development of efficient information retrieval systems. The method involves algorithm testing with statistical analysis of execution time, accuracy, and code length. Results indicate that DeepSeek has an average execution time of 28.74 seconds, slightly slower than OpenAI’s 28.49 seconds. However, DeepSeek’s accuracy (85.88%) surpasses OpenAI’s (85.03%). The average number of code characters is identical at 96.35 characters. The study concludes that DeepSeek excels in accuracy, while OpenAI is faster in certain cases, providing valuable insights for developers in selecting AI models for information retrieval applications.

The integration of artificial intelligence (AI) in forensic investigation has significantly transformed the analysis and authentication of digital evidence. This paper explores the role of AI technologies, specifically machine learning and deep learning algorithms, in examining digital evidence from various sources, including computers, mobile devices, and network systems. We provide an in-depth analysis of current AI-based forensic tools, their efficiency in evidence authentication, and the challenges they face regarding legal admissibility. Our findings indicate that AI-powered forensic systems can detect digital evidence tampering with 94.7% accuracy, drastically reducing analysis time from weeks to hours. However, challenges remain, particularly in areas such as algorithmic transparency, bias prevention, and ensuring the integrity of the chain of custody. This research offers a framework for incorporating AI in forensic laboratories, while also addressing crucial legal and ethical concerns to ensure the admissibility of AI-analyzed evidence in court. These considerations are essential for the widespread acceptance and use of AI in forensic investigations.

Arabic grammar (nahwu) instruction has long been dominated by rule-based approaches that emphasize memorization and formal analysis, often resulting in rigid learning structures and limited responsiveness to learners’ cognitive diversity. While such approaches play an important role in preserving grammatical accuracy, they frequently overlook individual learning trajectories, cognitive readiness, and adaptive instructional needs. In the era of artificial intelligence (AI), language education is increasingly shaped by adaptive learning systems that personalize content, pacing, and instructional strategies based on learners’ profiles. This study aims to reconceptualize Arabic grammar instruction by proposing a conceptual framework that integrates traditional nahwu principles with adaptive learning systems informed by AI. Using a qualitative conceptual analysis, this paper synthesizes classical Arabic grammar pedagogy, contemporary theories of adaptive learning, and recent developments in AI-supported language instruction. The proposed framework highlights key components, including learner profiling, cognitive-level alignment, hierarchical nahwu content structuring, and AI-assisted scaffolding mechanisms. The findings suggest that adaptive learning systems offer significant pedagogical potential to transform nahwu instruction from a static, rule-centered model into a flexible, learner-centered process. This reconceptualization is expected to enhance grammatical comprehension, reduce cognitive overload, and promote learner autonomy in Arabic language education, particularly in Islamic higher education contexts. The study concludes by discussing pedagogical implications and directions for future empirical research on AI-assisted Arabic grammar learning.

This quasi-experimental study examines the effectiveness of Artificial Intelligence (AI)-assisted learning in enhancing critical thinking skills among undergraduate history students. The study involved 60 students divided into experimental and control groups. The experimental group received AI-supported instruction integrating adaptive learning modules, scaffolded source-analysis prompts, and guided argumentative discussions facilitated by conversational AI tools, while the control group followed conventional lecture-based instruction. Data were collected using a validated critical thinking test, classroom observation protocols, and semi-structured interviews. Quantitative data were analyzed using paired and independent sample t-tests, while qualitative data were examined through Miles and Huberman’s interactive analysis model. Results indicate statistically significant improvements in critical thinking scores in the experimental group compared to the control group. Thematic findings reveal enhanced sourcing, contextualization, corroboration, and evidence-based argumentation skills. However, minor risks of over-reliance on AI highlight the need for instructional scaffolding and ethical guidance. The findings suggest that AI can function as a cognitive scaffold that strengthens historical thinking and metacognitive awareness when implemented within a structured pedagogical framework.

This study aims to analyze the effect of the implementation of the Intelligent Tutoring Sistem (ITS) based on Artificial Intelligence (AI) on improving the understanding of mathematical concepts, especially in fractional and basic geometry materials, in Class V students of SD Negeri 2 Badran, Temanggung Regency. The research method used was a quasiexperimental experiment with a Non-equivalent Control Group Design. The research sample consisted of 48 students who were divided into two groups, namely the experimental group (n=24) who received learning with the help of AI-based ITS, and the control group (n=24) who received conventional learning with lecture methods and practice questions. The research instrument is in the form of a test of understanding of mathematical concepts that has been validated by experts and tested for reliability. Data were analyzed using parametric statistical tests of the Independent Sample t-test and N-Gain Score to measure the improvement. The results showed that there was a significant difference in understanding of mathematical concepts between the experimental group and the control group. The average post-test score of the experimental group (82.45) was significantly higher than that of the control group (70.12) with a p< value of 0.05. N-Gain analysis showed that the improvement in conceptual understanding in the experimental group was in the "moderate" category (g=0.56), while the control group was in the "low" category (g=0.32). These findings indicate that AI-based ITS is effective in improving students' understanding of mathematical concepts. The advantages of the system lie in its ability to provide instant feedback, personalize materials according to learning pace, and present interactive materials, thus helping to better construct students' conceptual understanding. It is recommended that schools consider the integration of ITS technology as a supplementary tool in mathematics learning at the elementary level.

The integration of Artificial Intelligence (AI) into educational settings, particularly in formative assessments, offers significant benefits in terms of personalized learning, real time feedback, and increased efficiency. However, the successful implementation of AI driven formative assessments depends not only on technological capabilities but also on socio cultural and organizational factors that shape its adoption. This study explores the socio technical factors influencing the use of AI in formative assessments, emphasizing the importance of considering cultural diversity, institutional culture, and educators' beliefs. AI technologies, while powerful in automating grading and providing personalized assessments, often face limitations in addressing complex student responses that require human judgment. Furthermore, cultural factors, such as students' prior exposure to technology and different cultural attitudes towards AI, play a critical role in the acceptance and effectiveness of these tools. Organizational factors, including leadership support, digital literacy, and the readiness of institutions to adopt AI, are also key determinants in the successful implementation of AI systems in education. Teachers’ beliefs about assessment influence their acceptance and use of AI tools, highlighting the need for professional development and training to ensure that AI enhances pedagogical goals rather than replacing human expertise. The study concludes that the alignment of technology, culture, and assessment beliefs is essential for the effective use of AI driven formative assessments in educational settings. Recommendations for educational institutions include adopting a socio technical approach to AI integration, with a focus on providing resources, training, and fostering a culture of innovation. Future research directions should focus on expanding studies to diverse educational contexts, conducting longitudinal research on AI’s impact on learning outcomes, and exploring additional socio technical frameworks to guide AI adoption in education.

Objective– This article aims to comprehensively examine the main types of food crop pests and their attack patterns through a systematic literature review approach. The research focuses on the dynamics of pest attacks, changes in ecological patterns due to climate change, and advances in modern identification technology that enable more accurate early detection. This study also highlights the significance of new paradigms of pest identification based on artificial intelligence (AI), genomics, and landscape mapping in supporting food security at the regional and national levels. Design/methodology/approach– This study used the Systematic Literature Review (SLR) method for scientific publications from 2015–2025 from reputable sources such as Scopus, Web of Science, PubMed, ScienceDirect, SpringerLink, Taylor & Francis, Wiley, AGRIS, and Google Scholar. Of the 326 articles identified in the initial stage, 30 articles in English and Indonesian were selected through a screening process based on strict inclusion–exclusion criteria. All articles were then analyzed using thematic coding techniques to produce an in-depth, evidence-based synthesis. Findings– The study produced four key findings: (1) there are five dominant pests in global food crops, namely Thrips tabaci, Spodoptera exigua/frugiperda, Helicoverpa armigera, Nilaparvata lugens and Sitophilus oryzae; (2) attack patterns are strongly influenced by temperature, humidity, pesticide resistance, and monoculture; (3) modern identification technology AI, drone imagery, multispectral sensors, and DNA Barcoding have increased detection accuracy to 94–98%; and (4) community-based early warning systems accelerate field response and reduce the risk of crop failure. Practical implications– These findings provide a scientific basis for local governments, agricultural extension workers, and farmers to gradually adopt pest identification technology and strengthen integrated monitoring systems at a regional scale. Authenticity/value– This article offers a new conceptual model of “Pest Identification Pyramid – Attack Pattern – Early Warning System” that integrates pest biology, digital technology, and community response to improve national food security.

This paper reviews the role of Artificial Intelligence (AI) in Islamic education within secondary schools, emphasizing both its practical uses and the ethical challenges it presents. The review looks into the current trends, tools, and the impact of AI on the learning experience, as well as its ethical implications from an Islamic perspective. The study follows a systematic literature review (SLR) approach based on the PRISMA guidelines and includes research from 2022 to 2025, sourced from platforms like Google Scholar. After a thorough selection process, 15 articles were included in the review, offering valuable insights into the technological and ethical aspects of AI in Islamic secondary education. The use of AI has notably enhanced learning outcomes in Islamic education by allowing personalized learning, boosting student engagement, and streamlining feedback mechanisms. Tools like intelligent tutoring systems and educational chatbots have been widely adopted. However, challenges around data privacy, algorithmic bias, and technology access persist. Additionally, incorporating Islamic ethical values into AI-driven educational platforms presents both opportunities and challenges. Addressing these ethical implications is vital, requiring frameworks that align with Islamic principles such as maṣlaḥa (public welfare), justice, and human dignity. Education policies and teacher training programs should concentrate on promoting the responsible use of AI, ensuring it improves educational experiences while preserving ethical and cultural integrity.

Background: In the domain of corporate governance, the separation of ownership and control generates significant agency conflicts, primarily manifesting as Earnings Management (EM). Traditional reactive auditing methods fail to detect manipulation concealed within unstructured data, leading to high agency costs and diminished stakeholder trust. Objective: This study proposes an "AI Proactive Monitoring Model" utilizing Generative Artificial Intelligence to fundamentally enhance the monitoring mechanisms of Agency Theory. Methods: The research employs a qualitative conceptual framework analysis. It synthesizes Agency Theory with the Technology Acceptance Model (TAM) and Systemic Risk Theory to construct a novel strategic governance model. Results: The proposed model shifts governance from periodic sampling to real-time, continuous analysis of total data populations. By cross-referencing structured financial data with unstructured communications (e.g., emails, contracts), the system generates "Risk Narratives" that contextualize anomalies and flag opportunistic behavior immediately. Conclusion: The integration of AI significantly reduces information asymmetry and moral hazard by creating a "panopticon" effect. However, successful implementation requires distinct regulatory frameworks to manage the systemic risks associated with algorithmic reliance.

The development of artificial intelligence has revolutionized higher education by increasing the efficiency and personalization of learning, but it has also posed a major challenge in the form of a decline in students' critical thinking skills due to their dependence on technology. Therefore, an in-depth philosophical study through the post-positivism, critical theory, and constructivism paradigms is needed to understand its impact on cognitive and epistemological processes. This study aims to analyze how these three paradigms guide the development of critical thinking in the context of AI. It uses a descriptive-analytical method based on secondary data from academic literature such as journals and books that have been critically synthesized. The findings show that post-positivism views AI as a tentative critical realism tool for empirical verification. Critical theory critiques power relations, ideology, and the ethics of technological domination. Constructivism emphasizes the construction of social knowledge through human-centered design that supports creativity and collaboration. The positive impacts of AI include instant feedback and content adaptation, while the negative impacts include social isolation, weak digital literacy, and data privacy. Therefore, the implication is the wise use of AI with the assistance of teaching staff to strengthen reflective analysis, ethical literacy, and paradigm adjustments in accordance with the exact or social sciences to create multidimensional learning in the digital era.

The integration of artificial intelligence chatbots as virtual teaching assistants (VTAs) represents a transformative shift in student support services within higher education. This study investigates the implementation, effectiveness, and impact of AI-powered chatbots in providing academic support, administrative assistance, and personalized guidance to university students. Employing a longitudinal mixed-methods approach over 18 months, this research analyzed data from 2,347 students across 15 universities that deployed VTA systems, examining interaction patterns, student satisfaction, learning outcomes, and cost-effectiveness. Quantitative analysis of 487,392 chatbot interactions revealed that VTAs successfully handled 78.4% of student queries without human intervention, with response times averaging 3.2 seconds compared to 4.7 hours for traditional support channels. Qualitative findings from focus groups and interviews highlighted students' appreciation for 24/7 availability, immediate responses, and non-judgmental interactions, while also revealing concerns about empathy limitations, complex query handling, and the desire for human connection in critical situations. The study demonstrates that VTAs significantly improve support service accessibility and efficiency while reducing operational costs by an average of 43%. However, optimal implementation requires careful integration with human support staff, continuous training of AI systems, and attention to equity issues in digital access. This research contributes to understanding how AI can augment rather than replace human educators, offering evidence-based recommendations for implementing VTA systems that enhance student success while maintaining the human elements essential to quality education.

The integration of Artificial Intelligence (AI) in higher education has led to the increasing use of AI-powered adaptive learning models that support personalized and data-driven learning. However, studies examining preservice teachers’ perceptions of these models remain limited, despite their important role in future classroom implementation. This study aims to explore preservice teachers’ perceptions of AI-powered adaptive learning in higher education, focusing on perceived usefulness, learning adaptivity, learning experience, and perceived concerns. A descriptive qualitative research design was employed involving 53 preservice teachers from various universities. Data were collected using a Likert-scale questionnaire and open-ended questions. Quantitative data were analyzed descriptively using percentage distributions, while qualitative data were examined through simple thematic analysis. The findings reveal that preservice teachers generally demonstrate positive perceptions of AIpowered adaptive learning, particularly in terms of learning effectiveness, adaptability, and engagement. Nevertheless, concerns related to over-reliance on AI, ethical issues, and data privacy were also identified. These results indicate that preservice teachers show readiness to engage with AI-supported learning, while highlighting the need for teacher education programs to promote responsible and pedagogically informed AI integration.

This study examines the readiness of elementary school teachers to implement AI-based learning in the era of artificial intelligence, as technological advancements increasingly influence instructional practices in basic education. Despite the growing potential of artificial intelligence to support teaching and learning processes, empirical evidence regarding teachers’ preparedness at the elementary level remains limited. This study employed a descriptive quantitative research design involving 18 elementary school teachers. Data were collected using a structured questionnaire consisting of 15 Likert-scale items measuring technological skills, knowledge of artificial intelligence, attitudes toward AI, pedagogical readiness, and infrastructure support. Descriptive statistical analysis revealed that the overall mean score of teachers’ readiness was 4.08, indicating that teachers are generally ready to adopt AI-based learning. Technological skills emerged as the strongest aspect of readiness, reflecting teachers’ familiarity with digital tools and instructional technologies, while infrastructure and institutional support obtained the lowest mean score, highlighting challenges related to facilities, access to technology, and policy support. These findings suggest that although elementary school teachers demonstrate positive readiness and attitudes toward AI-based learning, effective and sustainable implementation requires strengthened institutional support, improved infrastructure, and continuous professional development to maximize the educational benefits of artificial intelligence in elementary education.

The rapid advancement of digital technologies and Artificial Intelligence (AI) has fundamentally reshaped the management and development of the tourism industry. Digital transformation strategies offer substantial opportunities to enhance destination competitiveness while simultaneously supporting economic, social, and environmental sustainability. This study aims to systematically examine the role of digital transformation and AI in strengthening sustainable tourism competitiveness through a literature review approach. A total of 42 peer-reviewed journal articles published between 2019 and 2025 were analyzed, drawing from Scopus, Web of Science, and Google Scholar. The analysis employed thematic synthesis to identify dominant patterns, conceptual relationships, and emerging themes across the literature. The findings indicate that AI-driven digital transformation enhances operational efficiency, enables personalized tourist experiences, supports data-informed resource management, and facilitates the development of smart tourism destinations. Nevertheless, persistent challenges related to human resource readiness, digital inequality, data governance, and ethical considerations remain evident. This review provides an integrated conceptual perspective on digital transformation and AI in sustainable tourism competitiveness and offers insights for policymakers, practitioners, and future research.