The earthquakes are unforeseen, and hence the matter of life and death is a few seconds away. The urban population trends are on an upward trend, and the population is increasing. In addition to fast urbanization and the growing complexity of the infrastructure, the necessity of smarter and even faster systems of detecting disasters has become urgent. The reason why Artificial Intelligence (AI) is an up-and-coming disruptive technology in managing earthquake disasters is not that it is capable of predicting earthquakes with perfect precision, but rather that it is able to unveil subtle warning signals that may go undetected in the traditional approach [7],[10]. Seismic waves possess subtle and intricate patterns that are buried in enormous data of geophysics and the environment and can be efficiently examined with the help of AI technology [4],[7]. This paper is focused on the possible ways to use AI to predict and prepare better in relation to earthquakes based on historical seismic events and real-time tracking of data streams [6]. Research has shown that AI models are effective in identifying early signs of stress and risks in vulnerable zones, as well as assisting in quick decision-making compared to traditional statistical techniques used [7,10]. Instead of being centred on the precise timing and full magnitude of the earthquakes, AI systems focus on risk management, the escalation of the early warning, and forecasting the impacts with sensitivity [5],[6]. Such systems can help the authorities to focus on high-risk locations, enhance the resilience of infrastructure, and prepare emergency responses in a timely manner [6]. In spite of the existing issues surrounding data quality, uncertainty, and ethics, AI-assisted earthquake prediction is critical in the minimization of human casualties and monetary damage through preparedness and proactive planning [11].

Security Information and Event Management (SIEM) systems are central to modern Security Operations Centers (SOCs), yet they continue to suffer from excessive alert volumes, delayed detection, and limited contextual awareness. These challenges lead to analyst fatigue and inefficient incident response. This paper proposes Intelligent SIEM+, a context-aware and AI-driven enhancement framework designed to improve alert prioritization and decision support without replacing existing SIEM deployments. The framework integrates behavioral analysis, anomaly detection, contextual correlation, and natural language summarization to transform raw alerts into actionable security insights. A descriptive survey-based study was conducted among SOC professionals to assess current SIEM limitations and the perceived value of intelligent alert prioritization. The findings indicate strong alignment between operational SOC challenges and the capabilities proposed in Intelligent SIEM+, suggesting that context-aware SIEM enhancements can significantly improve analyst efficiency and situational awareness.

The rapid integration of Artificial Intelligence (AI) in workplaces is transforming how organizations operate, enhancing efficiency, and automating decision-making processes. While AI promises significant benefits, its successful adoption depends largely on employees’ perceptions, attitudes, and behaviors. This study aims to explore employee perceptions of AI in the workplace, focusing on the factors that influence adoption, resistance, and trust. Using a quantitative research method approach, the research collects quantitative data through surveys from the employees. The study identifies the levels of awareness, willingness to adopt AI, reasons for resistance, and the degree of trust in AI systems. Findings from this research are expected to provide organizations with actionable insights to enhance AI acceptance, reduce resistance, and foster a trusting environment, ultimately enabling smoother AI integration and maximizing its organizational benefits.

Artificial Intelligence (AI) is rapidly transforming the education sector by enhancing curriculum design and content development processes. One of the most important areas influenced by AI is curriculum design and content development. AI enables educational institutions to design flexible, learner-focused, and outcome-oriented curricula using data-driven insights. It supports the creation of personalized and updated learning content, improves instructional planning, and enhances overall teaching effectiveness. AI tools help educators analyse learner needs, personalize learning experiences, and create updated academic content efficiently. This study explores the role of AI in curriculum design and content development, focusing on how AI supports outcome-based education, adaptive learning, and continuous curriculum improvement. This study highlights the benefits of AI, such as time efficiency, accuracy, learner-centric content, and data-driven decision-making. It also discusses challenges such as data privacy, lack of technical skills, and ethical concerns. The study concludes that effective integration of AI in curriculum planning can significantly improve the quality of education and make teaching–learning processes more innovative and inclusive. The findings indicate that AI, when applied responsibly, can improve curriculum relevance and learning outcomes in higher-education institutions.

Artificial Intelligence (AI) is reshaping inclusive education by offering innovative tools that address the unique needs of students with learning disabilities. These learners—often facing challenges such as dyslexia, ADHD and processing disorders—frequently encounter emotional and psychological barriers in traditional learning environments, including academic anxiety, low self-esteem, and feelings of social exclusion. This research examines the psychological benefits of AI-driven educational tools in promoting inclusivity, emotional well-being, and academic confidence among students with learning disabilities. AI-based technologies such as adaptive learning systems, intelligent tutoring programs, speech-to-text and text-to-speech tools, predictive writing assistants, and emotion-sensitive interfaces provide personalized, responsive, and accessible learning experiences. These tools adjust the pace, difficulty level, and mode of instruction to match each learner’s abilities, reducing frustration and fostering a sense of progress and accomplishment. By delivering real-time feedback and continuous support without judgement, AI significantly reduces learning-related stress and enhances student’s self-efficacy and motivation. The study highlights that AI promotes inclusivity by bridging gaps in comprehension, communication, and classroom participation. Students who previously struggled with reading, writing, or focusing can now engage more independently and confidently with academic content. This increased autonomy contributes to improved emotional resilience and cultivates a more positive self-image. Furthermore, AI helps diminish feelings of isolation by enabling students to keep pace with peers and participate more meaningfully in group activities. Overall, the findings suggest that AI serves as a powerful equalizer in education, not only improving academic outcomes but also creating a nurturing, psychologically supportive learning environment for students with diverse cognitive needs. By enhancing emotional well-being, confidence, and engagement, AI has the potential to transform inclusive education and empower students with learning disabilities to thrive both academically and personally.

The rapid expansion of AI-generated writing has introduced significant challenges to academic integrity, particularly in relation to authorship verification within educational and research contexts. This study examines how AI-generated text can be distinguished from human-authored academic writing through a structured integration of data science methods, linguistic analysis, and insights drawn from existing student-centered research (Elkhatat et al., 2023; Opara, 2025; Weber-Wulff et al., 2023). Rather than proposing definitive detection outcomes, the study focuses on identifying recurring stylistic tendencies reported in prior work. The research adopts a mixed-methods review-oriented approach, combining quantitative stylometric analysis with qualitative textual interpretation. Stylometry, a well-established framework for analyzing writing style (Holmes, 1998; Stamatatos, 2009), is used to examine academic texts produced by multiple large language models—ChatGPT, Gemini, Claude, Grok, Perplexity, and DeepSeek—alongside essays written by undergraduate students, as documented in the reviewed literature. The analysis emphasizes observable linguistic features such as function word frequency, sentence structure regularity, and part-of-speech sequence patterns that reflect underlying stylistic behavior. Commonly reported stylometric markers include lexical diversity, average sentence length, syntactic dependency depth, punctuation usage, and recurrent n-gram patterns (Opara, 2025). Prior studies analyze these features using machine learning classifiers such as support vector machines, random forests, and gradient-boosting models to assess stylistic separability at the corpus level (Elkhatat et al., 2023). Qualitative observations suggest that AI-generated writing frequently exhibits predictable phrasing, structured transitions, and consistent hedging, whereas human-authored writing demonstrates greater contextual variation and individual voice (Weber-Wulff et al., 2023). Overall, the study evaluates stylometric analysis as a transparent and interpretable framework for understanding differences between AI-generated and human-authored academic writing, contributing to ongoing discussions on ethical assessment practices and responsible AI use in higher education.

Maharashtra, home to UNESCO World Heritage Sites like Ajanta-Ellora Caves and iconic forts such as Raigad and Pratapgad, faces escalating threats to its cultural heritage from climate change, urbanization, and structural degradation. Traditional conservation methods struggle with resource constraints and lack predictive capabilities. This research examines artificial intelligence (AI) applications for sustainable preservation of Maharashtra's monuments, focusing on predictive maintenance, digital documentation, and automated damage assessment. Findings reveal AI reduces conservation costs by 35-40% through targeted interventions and extends monument lifespan by preempting structural failures. The research proposes a Maharashtra Heritage AI Framework integrating state ASI directorates with IIT research centers, featuring phased implementation: sensor deployment, ML model training and digital twin platforms. Challenges including data privacy, algorithmic bias, and Marathi language NLP limitations are addressed through ethical AI governance protocols. this paper demonstrates how AI technologies enable proactive conservation rather than reactive restoration. The research also addresses critical challenges including ethical concerns, data management issues, financial constraints, digital divide, and the need for responsible AI implementation. The findings suggest that while AI offers immense potential for heritage conservation, its successful implementation requires interdisciplinary collaboration, community participation, adequate funding, and robust regulatory frameworks. This paper concludes that AI-driven heritage conservation represents a paradigm shift from preservation to proactive protection, essential for achieving and developing Maharashtras tourist economy by year 2047.