Background of the Study : Libraries in the digital era face rapid technological changes that require managers to adapt through continuous innovation and competency development. Objectives: This research was aimed to analyze the efforts of the Arung Pallawa Library management at Bukit Asam Tanjung Enim High School in developing self-disruption in response to digital disruption. Method: The study used a qualitative method with a descriptive approach, and the Diffusion of Innovation theory served as the analytical framework. Data were obtained through interviews, observations, and documentation. Finding: The result of this research showed that the library management developed various competencies through training, participation in professional organizations, and collaboration with external partners. Implemented innovations include the use of the SLiMS application, participation in OneSearch Indonesia, the development of the SMABA The Best podcast, and the creation of KOLASIBA. The main challenges identified were limited budget and the technological skills of librarians. Conclusion: The study concludes that strong institutional support, particularly through innovation funding, competency enhancement, and improved digital literacy, is essential for school libraries to transform into adaptive and modern learning centers.

The implementation of differentiated learning still faces substantial challenges, particularly in adapting content, processes, and learning products to meet the needs of diverse learners. This study aims to describe student governance in differentiated learning at SMP Negeri 1 Bulawa by examining: (1) planning; (2) organization; (3) implementation; and (4) assessment. Employing a descriptive qualitative approach, the study was conducted at SMP Negeri 1 Bulawa with data collected through observation, interviews, and documentation analysis, and analyzed using an interaction model of data analysis. The findings show that: (1) student governance planning is oriented toward the application of student-centered learning principles; (2) student organization is carried out collaboratively and flexibly; (3) the implementation of differentiated learning proceeds through adjustments to students’ readiness, interests, and learning profiles; and (4) assessment practices are comprehensive, continuous, and reflective. Overall, the implementation of differentiated learning at SMP Negeri 1 Bulawa has been successful and reflects the school’s concrete efforts to realize inclusive, adaptive, and student-centered learning, thereby strengthening governance practices that support differentiated instruction.

Artificial Intelligence (AI) and automation have become pivotal in transforming Human Resource Management (HRM), automating routine tasks, enabling predictive analytics, and personalizing employee experiences across recruitment, performance management, engagement, and learning and development (L&D). The integration of AI in HRM has led to significant efficiency gains, such as reducing hiring time by up to 75%, improving worker retention by 25-65%, and augmenting skill development through personalized, adaptive learning pathways. This paper reviews current academic research highlighting both the transformative potential and ethical challenges of AI adoption, such as algorithmic bias and data privacy concerns. The study advocates for hybrid human-AI models that combine the efficiency of automation with human empathy, emphasizing the need for HR professional upskilling and transparent governance frameworks for sustainable integration.

The rapid evolution of global financial markets has increased the demand for intelligent, data-driven trading systems capable of analyzing complex, high-frequency market dynamics. This study proposes an adaptive deep learning framework for real-time stock market prediction and automated trade execution, integrating multi-modal inputs such as historical price movements, technical indicators, economic signals, and sentiment data. The proposed model utilizes a hybrid architecture combining Long Short-Term Memory (LSTM) networks, Temporal Convolutional Networks (TCN), and reinforcement learning–based policy optimization to enhance predictive accuracy and decisionmaking robustness. A dynamic model-updating mechanism is incorporated to continuously adapt to evolving market behaviors and reduce sensitivity to volatility and noise. Experimental results demonstrate superior performance compared to conventional machine learning and single-model deep learning approaches, achieving improved prediction stability, reduced trading risk, and higher portfolio returns. The framework offers a scalable and intelligent solution for real-time automated trading, contributing significantly to the development of next-generation AI-driven financial systems.

Contemporary educational systems struggle to deliver personalized learning experiences that accommodate diverse learner characteristics and preferences. This paper presents a comprehensive architectural design and implementation methodology for an adaptive learning framework leveraging Reinforcement Learning (RL) and Markov Decision Processes (MDP). The proposed system architecture integrates intelligent components including learner assessment, student modeling, Q-learning-based recommendation engine, and generative AI content delivery. We formalize the learning process as an MDP with 11 states and 8 actions, implementing Q-learning algorithms for sequential learning path optimization. The three-tiered architecture comprises React-based frontend, Node.js backend, and MySQL database, integrated with Gemini API for content generation. The methodology incorporates Felder-Silverman Learning Style Model (FSLSM) with hybrid machine learning classification combining K-means clustering, Artificial Neural Networks, and Bayesian inference. This work addresses critical implementation challenges including cold-start problems, scalability considerations, and privacy-preserving design principles

MediAstra is an integrated AI-powered healthcare ecosystem designed to unify medical assistance, personalized fitness management, and NEET exam preparation within a single intelligent application. Today, users rely on multiple fragmented platforms to access medical guidance, fitness planning, and educational support, leading to inefficiency and lack of comprehensive personalization. MediAstra overcomes this challenge by integrating machine learning, natural language processing, and rule-based decision models to deliver unified support across healthcare, wellness, and academic domains.The platform comprises four core modules: SymptoScan AI for symptom-based disease prediction, Fit360 AI for personalized fitness and diet planning, CareSync for healthcare navigation and consultation, and NeetMind AI for adaptive learning and NEET exam preparation. The system processes user inputs through secure authentication, validates data at the server level, interprets queries using the Groq LLM (Large Language Model), and stores interactions in a robust relational database for personalized insights. Keywords:

The rapid digitization of education in the 21st century has necessitated the integration of Artificial Intelligence (AI) into learning environments to foster inclusivity and adaptability. Despite significant progress globally, Nigerian secondary schools remain constrained by a lack of adaptive learning systems capable of personalizing instruction in line with students’ diverse cognitive and socio-economic contexts. This study presents the design and evaluation of an AIpowered Adaptive Learning Companion (ALC) aimed at enhancing digital literacy among secondary school students in Nigeria. The system integrates Natural Language Processing (NLP) and Reinforcement Learning (RL) algorithms to tailor instructional content dynamically based on learners’ performance patterns and interaction behaviors. A quasi-experimental design was employed, involving 300 students across three Nigerian states. Test Instruments Reported: Cronbach’s Alpha: 0.882 (strong reliability), One-Way ANOVA: F(1,298) = 47.813, p < 0.001 (statistically significant); Partial Eta-Squared (η²): 0.14 (large effect size) and TwoWay ANOVA: Institution × Group interaction insignificant for ENG (p = 0.06), significant for Overall_Mean (p = 0.021).The results demonstrated a significant improvement in learners’ digital literacy scores, engagement rates, and learning retention compared to traditional instruction. The study concludes that AI-driven adaptive systems represent a transformative approach to equitable and scalable education delivery in developing contexts.

Lifelong learning plays a crucial role in both personal development and societal advancement. By continually enhancing their skills, individuals can better adapt to change and contribute to progress. In this regard, artificial intelligence (AI) supports lifelong learning by enabling personalised learning experiences, increasing accessibility, and fostering continuous education. This study examines existing research on AI’s role in supporting lifelong learning, with a focus on personalised education, skill development, and the reduction of learning gaps across educational stages. A systematic literature review was carried out following the guidelines established to examine the evolving contributions of AI to the development and support of lifelong learning practices. This study follows the standards outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Articles relevant to the study’s objectives were identified through a systematic search of the Scopus database, limited to English-language publications in the field of social sciences over the past ten years. The search strategy employed the following string: (“Artificial Intelligence” OR “AI”) AND (“Lifelong learning” OR “continu* education”) AND (“personali* learning” OR “skill* development”). This process yielded a total of 14 selected studies, from which three themes were identified through thematic analysis: 1) perspectives shaping AI in lifelong learning, 2) benefits of AI tools in education, and 3) AI’s potential for optimising and transforming learning. Findings show that diverse perspectives, along with social and cultural factors, shape the design and effectiveness of AI in lifelong learning. Various AI tools, such as adaptive learning platforms, provide personalised content and immediate feedback, enabling learners to progress at their own pace and promote skill acquisition. These tools offer clear benefits: they foster personalised learning experiences that go beyond mere productivity gains to truly enhance learners’ capabilities. Personalised education models also optimise resource allocation by tailoring content to individual needs, improving outcomes in higher education settings. Looking forward, AI presents significant opportunities to transform education through tailored learning and teacher support. AI must balance technology with human interaction to foster critical thinking, creativity, and problem-solving essential to lifelong learning.

This study looks at how language education increasingly uses data from school software and adaptive learning platforms. This creates a big teaching contradiction. While these systems claim to give personalized feedback on student performance, they often turn the complex, cultural process of learning a language into simple numbers that lack context. This can accidentally favor students who speak only one language and frame others as lacking skills. This article contends that technical data skills alone aren’t enough for teachers; instead, argue that teachers need more than just technical skills to read this data. They need a "Critical Data Literacy" (CDL), built on the habit of carefully reflecting on their practice. The researcher has made a three pillar framework: Interrogate, Contextualize and Humanize, as reflective fix. This framework will turn the instructors from passive information consumers to active proponents. The proposed method helps them read dashboard analytic through an equity lens, recover their professional judgment, and map assessment to the rich realities of multilingual learners. This ideology is about denial of the data but an appeal for a considerate, thoughtful and ethical incorporation of the data into humanistic work in language teaching, proposing a pragmatic anchor between critical theory and classroom practice.

Traditional domain adaptation learning methods have a strong dependence on data labels. The transfer process can easily lead to a decrease in training set performance, affecting the effectiveness of transfer learning. Therefore, this study proposes a domain adaptation model that combines feature disentangling and disentangling subspaces. The model separates the content and style features of images through disentangling, effectively improving the quality of image transfer. From the results, the proposed feature disentangling algorithm achieved pixel accuracy of over 84% for semantic segmentation of 14 categories, including roads, sidewalks, and buildings, with an average pixel accuracy of 85.2%. On the ImageNet, the precision, recall, F₁ score, and overall accuracy of the research algorithm were 0.942, 0.898, 0.854, and 0.841, respectively. Compared with the One-Class Support Vector Machine, the precision, recall, F₁, and overall accuracy were improved by 8.4%, 10.3%, 27.8%, and 10.9%, respectively. The proposed model can accurately recognize and classify images, providing effective technical support for image transfer.

Ensuring robust test coverage, high code quality, and a strong security posture are persistent challenges in modern industrial software development, especially as systems grow in complexity and release cycles accelerate with recent Artificial Intelligence (AI) related productivity gains. This paper introduces a conceptual framework, "The Test Pyramid 2.0", which offers a clear and actionable path to integrate the latest advances in AI and DevSecOps principles into engineering workflows to achieve greater efficiency, reduce defect leakage, and create more resilient systems. We examine how AI enhances each layer of the test pyramid through capabilities such as automated test generation, coverage analysis, test data synthesis, anomaly detection, and intelligent UI exploration. In parallel, we embed DevSecOps practices directly into the pyramid by aligning security controls with each testing layer, ranging from static analysis and policy enforcement to dynamic testing, misconfiguration detection, and adversarial simulation. We also explore how AI strengthens these security practices through adaptive learning, risk prioritization, and context-aware detection. Together, these advances create a holistic, AI-augmented, and security-conscious testing strategy that supports the speed of modern development without compromising quality or safety.

South Africa faces persistent socio-economic challenges, including high unemployment, skills mismatches, and structural inequalities that limit inclusive development. Despite a relatively high youth literacy rate, graduate unemployment has emerged as a critical concern, reflecting the disjuncture between higher education outputs and labour market needs. This article systematically reviews literature published between 2005 and 2025 to explore the complex interplay between graduate unemployment, skills mismatch, and labour mobility in South Africa. A total of 43 peer-reviewed articles from Scopus and Web of Science were analysed using a systematic literature review approach. The findings reveal five interrelated themes: (i) enduring inequities in education access and quality, rooted in colonial and apartheid legacies; (ii) limited transformation of higher education, characterised by poor throughput and uneven responsiveness; (iii) persistent mismatches between academic qualifications and industry requirements; (iv) the ideological framing of policy reforms shaped by global discourses such as Human Capital Theory; and (v) the disruptive effects of technological change, particularly the Fourth Industrial Revolution. Collectively, these themes highlight the need for reforms that strengthen the alignment between education and labour market demands, expand equitable access to work-integrated learning, and promote adaptive lifelong learning strategies. Policy implications underscore the importance of industry–university partnerships, curriculum reform, and context-sensitive labour market planning. While this review is limited to published literature, it provides valuable insights for addressing graduate unemployment and advancing inclusive, skills-driven growth. Future research should focus on longitudinal studies, regional comparisons, and the role of technology in reshaping employability.

Leveraging Adaptive Vision Transformers Features Fusion and Machine Learning for Coffee Bean Roast Level Identification

Knowledge discovery and performance-energy optimization in heterogeneous catalytic ozonation via adaptive multi-task learning.

Generation of naturalistic and critical boundary scenarios: A bi-level adaptive deep reinforcement learning method.

Adaptive learning, instruction, and teaching in schools: Unraveling context, sources, implementation, and goals in a systematic review

K-core-guided adaptive learning and policy optimization for targeted influence maximization in complex networks

SRDA: Self-regulating distribution alignment based on prompt learning for unsupervised domain adaptation

FDBPL: Faster distillation-based prompt learning for region-aware vision-language models adaptation

End-to-end burst signal demodulation via adaptive masked deep learning framework