Sustainable Supply Chain Management (SSCM) has gained significant attention due to increasing environmental concerns, regulatory pressure, and stakeholder demand for transparency and ethical accountability. Traditional supply chain systems rely heavily on centralized data repositories and periodic sustainability audits, which suffer from data manipulation risks, limited traceability, and delayed decision-making. While blockchain technology improves transparency through decentralized and immutable data storage, it largely functions as a secure data repository. Similarly, artificial intelligence (AI) enhances prediction and optimization but often operates on unverifiable or fragmented data sources. This paper proposes an integrated Blockchain and Artificial Intelligence framework for sustainable supply chain management, introducing Blockchain-Anchored Sustainability Intelligence (BASI) as a sustainability-focused AI layer. BASI continuously evaluates, predicts, and explains sustainability performance using blockchain-verified data. Furthermore, smart contracts translate AI-driven sustainability insights into automated incentives and penalties, ensuring accountability. The proposed framework transforms sustainability from static reporting into continuous, explainable, and enforceable governance, enabling proactive and transparent supply chain decision-making.

India’s decentralized milk supply chain continues to face critical challenges, including adulteration, unauthorized container access, and undocumented handling—issues exacerbated by limited digital infrastructure in rural regions. Existing traceability mechanisms, such as QR codes and cloud-dependent platforms, lack tamper-evidence, are easily duplicated, and perform poorly in offline environments. This study proposes a blockchain-enabled, NFC based smart lid system designed to establish secure, low-cost, and tamper-evident traceability tailored to these constraints. Enhance the system’s intelligence and responsiveness, we integrate artificial intelligence across key functions: tamper prediction and detection, anomaly monitoring, fraud prevention, route optimization, demand forecasting, and predictive maintenance. These capabilities enable initiative-taking risk mitigation, data-driven logistics, and improved operational reliability, while maintaining compatibility with intermittent connectivity. By combining AI-driven insights with blockchain-backed immutability, the proposed solution delivers scalable, verifiable, and offline-capable traceability—supporting greater transparency, efficiency, and trust across India’s rural dairy network.

Digital Twin technology, which creates intelligent virtual representations of physical environments, is becoming increasingly significant in global architectural practice. Its integration with artificial intelligence enables predictive simulation, performance evaluation, and data-driven decision-making across a building’s lifecycle. However, despite advancements in countries with mature smart-infrastructure ecosystems, the architectural field still lacks accessible frameworks for understanding how digital twins can be systematically implemented during the design and planning stages. This gap restricts architects from leveraging AI-driven insights to improve sustainability, efficiency, and user experience. This research addresses this gap by examining the conceptual processes, methodological workflows, and technological foundations required to integrate AI-enabled digital twins in architecture. The study synthesizes existing literature, emerging AI techniques, and global digital twin use cases to outline a structured process model that explains how digital twins operate, how data flows through their components, and how AI enhances predictive and analytical capabilities. Rather than building a functional prototype, the research focuses on understanding operational mechanisms and mapping practical integration pathways relevant to architects and early-stage planners. Findings highlight that AI-driven digital twins can support informed decision-making in areas such as energy analysis, environmental modeling, design optimisation, and lifecycle management. The study concludes by presenting a conceptual framework that can guide future implementation, making digital twin adoption more feasible, scalable, and beneficial for sustainable architectural development.

The preservation of global cultural heritage is facing an unprecedented crisis. Rapid urbanization, geopolitical conflict, and environmental degradation are erasing historical sites at an alarming rate, often leaving behind only fragmented ruins or digital photographs. Current digital preservation methodologies, such as Photogrammetry and LiDAR (Light Detection and Ranging), have enabled the creation of high-fidelity "Digital Twins." However, these methods are fundamentally limited; they capture the site only as a static, frozen artefact, devoid of the complex temporal and social contexts that shaped it. They present history as a finished product rather than an evolving process. This research proposes a novel AI-Enhanced Reconstruction Framework that transcends traditional documentation by integrating Natural Language Processing (NLP), Neural Radiance Fields (NeRF), and Generative Adversarial Networks (GANs). We introduce the concept of "Branching Historical Timelines," a computational approach that allows users to explore not just the history that occurred, but also "counterfactual" or alternative histories based on variable pivot points. By integrating these generative models into an immersive Virtual Reality (VR) environment, the proposed system transforms passive observation into active historical inquiry. This framework offers a robust tool for both educational simulation and digital humanities research, shifting the paradigm from static preservation to dynamic simulation.

Penetration testing is a key process for identifying vulnerabilities within organizational systems, simulating real-world cyberattacks to uncover weaknesses before exploitation. These tests often generate extensive reports with numerous findings, making it challenging for organizations to determine which vulnerabilities should be addressed first. Limited resources such as time, budget, and skilled personnel further complicate prioritization. Traditional approaches, relying mainly on technical severity scores, often fail to capture the full business impact, operational criticality, and organizational priorities, leaving critical risks unaddressed. This research proposes an AI-assisted framework to enhance post-penetration testing vulnerability prioritization as a decision-support tool rather than a replacement for human expertise. By integrating technical severity with organizational and contextual factors such as business impact, asset criticality, exploit likelihood, and resource constraints, the framework provides explainable AI recommendations to assist security teams. The approach aims to improve remediation efficiency, strengthen risk management, and align vulnerability prioritization with strategic organizational objectives, demonstrating how AI can complement human decision-making to achieve more resilient cybersecurity postures.

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.

The deployment of artificial intelligence (AI) models traditionally relies on continuously running servers or virtual machines, leading to high operational costs, inefficient resource utilization, and limited scalability under dynamic workloads. Although cloud computing has improved accessibility, challenges such as manual infrastructure management and over- provisioning persist. Serverless AI has emerged as a promising paradigm that enables event-driven and on-demand execution of AI models without explicit server management. This paper presents a structured analysis of Serverless AI, focusing on its system architecture, execution workflow, and operational characteristics. A comparative discussion highlights the advantages of Serverless AI over traditional deployments in terms of scalability, cost efficiency, fault tolerance, and reduced operational complexity. Key application domains including image processing, conversational systems, healthcare analytics, and fraud detection are examined. Despite its benefits, Serverless AI introduces challenges such as cold-start latency, execution time limits, hardware constraints, and model size overheads. These limitations and future research directions are discussed. The paper concludes that Serverless AI provides a flexible and scalable deployment model for AI inference workloads with unpredictable demand patterns.

Large Language Models (LLMs) have become integral to a wide range of applications, raising concerns about their tendency to generate hallucinated content and exhibit biases inherited from training data. While prior research has examined hallucination behavior across different AI models, less attention has been given to how these limitations align with human perceptions of bias and trust. This paper presents a comparative review of existing research on hallucinations in contemporary LLMs, synthesizing findings across multiple studies to identify common trends, evaluation approaches, and reported limitations. In parallel, a human perception study examines how users interpret and judge bias, reliability, and trustworthiness in AI-generated outputs. Participants provide subjective assessments of perceived bias and confidence in model responses, enabling comparison with conclusions drawn in prior technical literature. The findings reveal a clear divergence between empirically reported hallucination behavior and user perception. Models identified as having lower hallucination tendencies are not consistently perceived as less biased or more trustworthy. Instead, fluent and confident responses often lead to higher perceived reliability, regardless of documented limitations. This highlights a disconnect between technical evaluation and human judgment. This study emphasizes integrating human-centered perspectives into LLM evaluation and underscores the need for transparency, clearer communication of limitations, and trust-aware deployment.

Noise suppression and enhancement technologies play a vital role in modern communication systems, especially in video conferencing platforms such as Google Meet, online collaboration tools, and virtual learning environments. Traditional adaptive noise cancellation methods rely mainly on unimodal audio input and low-level acoustic processing, which often proves insufficient in complex real-world environments, leading to the loss of meaningful auditory information. This paper proposes a context-aware noise suppression framework based on multimodal artificial intelligence to overcome these limitations. The framework integrates audio, visual, and motion-based contextual information to enable semantic-level understanding of sound sources. Audio signals are analyzed using speech and noise classification models, while visual and motion inputs assist in determining spatial orientation and contextual relevance. A unified decision mechanism conceptually determines whether sounds should be preserved or suppressed based on surrounding context. The proposed approach is expected to improve speech clarity, enhance user focus, and maintain environmental awareness. It is particularly relevant for applications such as video conferencing, wireless headphones, smart earbuds, assistive hearing devices, gaming headsets, and safety-critical communication systems, highlighting the importance of multimodal intelligence in next-generation noise suppression technologies.

Artificial Intelligence (AI) has become an essential component of modern space exploration, particularly in enabling autonomous navigation for spacecraft and planetary rovers. Due to long communication delays and unpredictable space environments, direct human control is often impractical during space missions. AI-driven autonomous navigation systems allow space vehicles to independently analyze their surroundings, detect obstacles, plan safe routes, and make real-time decisions without relying on continuous instructions from Earth.This study examines the role, advantages, and limitations of AI-based autonomous navigation systems in space exploration. A survey-based research approach was used to understand public perception regarding the reliability, safety, and future importance of AI navigation technologies. The results indicate that most respondents believe AI-driven navigation significantly enhances mission efficiency, safety, and feasibility of deep-space exploration. However, concerns related to system reliability, limited onboard computing power, and potential software errors were also identified. Overall, the study highlights that while AI autonomous navigation presents certain challenges, it is a critical technology that will strongly influence the success of future space missions.

With the increasing use of A.I. (AI) in marketing &advertising, understanding consumer trust towards AI-generated content has become crucial. This study aims to examine whether consumers find AI-generated advertisements as trustworthy &convincing as human-created advertisements. The research follows an experiment-based survey method, where participants are shown two similar advertisements without being informed which one is AI-generated &which one is human-made. Respondents are asked to rate the trustworthiness of both advertisements &indicate their likelihood of purchasing the product. The study also includes a question to understand consumer perception if they are informed that the advertisement is completely AI-generated. Data is collected using a Google Form &analyzed to identify patterns in consumer trust &buying behavior. The findings of this research are expected to provide insights into how AI influences consumer decision-making &whether transparency about AI usage affects trust in marketing communication. We need to understand how AI is revolutionizing the marketing aspects of the organization, as, in one way or another, it is the marketing skills &patterns of the organization that influence customers to get involved with a particular product or organization.

Artificial Intelligence is no longer just a technological tool it is becoming a part of how we learn, connect, and experience society. This paper explores the role of AI in Education with a focus on how it shapes ethical and inclusive development, supports learning innovation, and influences everyday human behaviour. As classrooms adopt smart tutoring systems. AI technology is quietly transforming how people access knowledge and participate in community life. The study uses surveys to understand how students, teachers, and communities interact with AI-driven systems. Survey reveals that AI encourages inclusive education, helps bridge learning gaps, and enhances engagement. However, issues related to governance, justice, bias and responsible use of AI remain major, especially in diverse societies. With that, AI can contribute meaningfully to education if introduced with transparency, fairness and human-centred values.

The paper examines how education and artificial intelligence (AI) are related, emphasizing the importance of ethical, inclusive, and culturally based development. It features the significance of education in fostering moral judgment, empathy, and cultural identity, while AI improves learning through increased accessibility, customized experiences, and cultural preservation. The work advocates for integrating humanistic educational practices with ethical AI approaches to empower communities and foster equality. Education cultivates moral reasoning via discussions, experiential learning, and interpersonal connections, thus imparting deep values and enabling critical thinking in ethical dilemmas. Meanwhile, AI promotes ethical development by fostering fairness and transparency but cannot replace human ethical understanding, as it integrally depends on the values of its creators. Inclusion within education is crucial for equitable participation and appreciation of diversity, addressing social, cultural, and psychological barriers. AI can enhance this inclusivity by offering tailored learning pathways and supporting marginalized learners with translation tools, though it risks worsening marginalization without fair implementations and representative data. Inclusive education stresses community and belonging, paralleling inclusive AI, which aims for equitable access to technology. Moreover, education strengthens cultural identity through arts, narratives, and historical experiences, promoting cross-cultural awareness. AI supports cultural preservation by digitizing heritage, documenting endangered languages, and enabling cultural exchanges. The study emphasizes the interrelated roles of education and artificial intelligence (AI) in developing morally robust and culturally rich civilizations. It contends that education delivers moral guidance, emotional awareness, and cultural knowledge, whereas AI provides flexibility, precision, and accessibility. A synergistic approach is advocated, in which education moulds ethical and inclusive development while AI serves as a supportive tool matched to human ideals. The work contends that AI should supplement rather than replace human understanding in education, as evidenced by worldwide trends and case studies that advocate for a broad view on AI's potential to improve human growth.

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].

With the rapid growth of digital technologies in education, the demand for efficient and accessible student support systems has increased significantly. Educational institutions handle a large volume of student queries on a daily basis, including questions related to admissions, academic schedules, examinations, course information, fees, and institutional policies. Traditional student assistance methods, such as helpdesks, emails, and phone-based support, are often time-consuming, repetitive, and highly dependent on human resources. These limitations result in delayed responses, increased administrative workload, and reduced student satisfaction. Artificial Intelligence, particularly Natural Language Processing (NLP), offers a promising solution to these challenges by enabling automated and intelligent interaction between humans and computer systems. NLP-based chatbots are capable of understanding user queries expressed in natural language and generating relevant responses in real time. This project proposes the design and development of an intelligent NLP-based chatbot system for automated student assistance, aimed at improving accessibility, efficiency, and consistency in institutional support services. The proposed system employs NLP techniques such as text preprocessing, intent recognition, and contextual understanding to accurately interpret student queries. A structured knowledge base containing academic and administrative information is integrated to ensure reliable and up-to-date responses. The chatbot is designed to operate continuously and manage multiple user interactions simultaneously, making it suitable for institutions with large student populations. System performance is evaluated using metrics such as response accuracy, resolution time, and user satisfaction. Natural Language Processing enables machines to understand, interpret, and generate human language, making it suitable for conversational systems in education (Jurafsky & Martin, 2023). Previous studies have shown that AI-based chatbots can significantly improve accessibility and efficiency in student support services (Kerly et al., 2017; Winkler & Söllner, 2018).

India’s traffic challan system relies heavily on traffic wardens who issue fines on the spot. While this human-driven process allows flexibility, it often suffers from errors. Drivers may be fined due to misjudgement, incomplete evidence, or bias, while genuine violations sometimes go unnoticed in crowded or complex traffic situations. These mistakes frustrate citizens, waste administrative effort, and weaken trust in enforcement. This paper explores how AI can support traffic wardens in making fairer and more accurate decisions. Instead of replacing wardens, AI tools can act as assistants: mobile apps that verify license plate details instantly, machine learning models that flag likely violations based on context, and decision-support systems that help wardens distinguish between genuine offenses and unavoidable actions (such as stopping briefly to avoid an accident). By reducing false positives and strengthening true violation detection, AI can make manual enforcement more transparent and trustworthy. The vision is a hybrid system where human judgment is enhanced and not replaced by AI, leading to smarter enforcement and stronger public confidence in traffic governance.

Rapid growth of cities and electronic devices has increased electricity use around the world. This rising demand is becoming hard to manage and is causing problems for both the power grid and the environment. Current energy management systems are not fast or smart enough to handle changing usage patterns, especially in modern homes and large campuses. This paper presents a conceptual AI-based approach to track, study, and potentially reduce electricity use in homes and educational campuses, supported by a user perception study. Using IoT sensors and smart meters, the system collects detailed energy data in real time. Machine learning models such as Long Short-Term Memory (LSTM) networks and Reinforcement Learning, which have been extensively validated for building energy forecasting, HVAC load prediction, and energy-efficient building management in peer-reviewed studies [4], [8], [9], are discussed conceptually to illustrate future intelligent electricity monitoring approaches. In homes, the system works like a virtual energy assistant. It can find hidden power consumption (vampire loads) and plan when appliances should run, based on cheaper electricity hours. On campuses, the system manages large systems such as lighting and air conditioning. It adjusts energy use according to class schedules and the number of people in each building. Survey responses and findings from existing studies indicate that users perceive AI-based electricity management systems as capable of reducing electricity use, particularly in homes and campuses. These savings lower costs, reduce carbon emissions, and help reduce peak pressure on the national grid. The results support the idea that AI should be widely used in buildings to help reach Net-Zero energy goals.[3]

Insider threats have become increasingly difficult to detect as modern organizations adopt cloud platforms, hybrid work models, and AI-driven tools that expand the attack surface. To address these evolving challenges, this paper introduces an advanced insider threat detection framework that uses Large Language Models (LLMs) to generate behavioral signatures from user communications and system activities. The framework captures semantic meaning, communication intent, workflow patterns, and contextual signals across emails, collaboration tools, code repositories, and access logs. These behavioral signatures allow the system to model user norms and identify even subtle deviations that may indicate misuse, data leakage, or compromised accounts. Evaluations on diverse, real-world enterprise datasets demonstrate that the LLM-based method delivers higher accuracy, lower false-positive rates, and more adaptive performance than traditional detection systems. The framework also provides explainable alerts through natural-language reasoning, helping analysts make faster and more reliable decisions. Designed with scalability, privacy preservation, and integration readiness for future autonomous systems, this approach offers a robust and future-proof solution for insider threat detection in next-generation digital environments.

AI tools—ChatGPT, learning apps, resume screeners—are now part of almost every student’s routine here. They make studying faster and sometimes easier, but they also bring real problems when there’s no proper ethical oversight. Recent reports and surveys from 2025–2026 show clear issues: many students use AI to complete assignments and exams with almost no original work, which hurts genuine learning and critical thinking; a large number (up to 88% in some youth surveys) turn to AI chatbots when they feel stressed or anxious, but this can increase feelings of isolation, shorter attention spans, and mental fatigue; entry-level jobs in IT and tech have already dropped by 20–25% because of automation, leaving fresh graduates worried about their future; AI systems sometimes carry forward biases related to caste, gender, or background, making opportunities even harder for students from rural areas or marginalized communities; and deepfakes along with AI-generated misinformation are spreading fast on social media, confusing young people and damaging trust. With over 40 million students in higher education across India, these challenges affect a huge number of young adults at a very important stage of life. Moral responsibility—meaning developers, companies, colleges, and the government must build AI that is transparent, fair, safe, and does no harm—is not optional. Without it, AI risks making existing pressures worse: weaker skills, poorer mental health, fewer jobs, unfair treatment, and less reliable information. It is important to take ethics seriously right now—through better design, regular bias checks (especially for Indian realities like caste), clear rules, and shared accountability—AI can actually help students learn better, find good careers, and grow confidently instead of holding them back.

As judicial backlogs reach critical levels globally, the integration of Artificial Intelligence (AI) has transitioned from administrative support to "predictive justice." Artificial Intelligence (AI) is transforming the judicial ecosystem worldwide — from automating legal research to assisting in bail and sentencing decisions. The concept of Predictive Justice, powered by data analytics and machine learning, aims to make legal outcomes more efficient and consistent. However, the introduction of “Robo-Judges”- autonomous or semi-autonomous and predictive algorithms raises profound ethical and legal confusions: Can justice truly be fair when delivered by machines? This paper explores the applications of AI in justice delivery, the potential benefits, and the serious risks of bias, opacity, and moral detachment. It argues that while AI can support judges and streamline processes, human judgment must remain the centre of Trails to ensure fairness, empathy, and accountability in the rule of law.

This research examines how artificial intelligence technologies are reshaping the mutual fund sector, with particular emphasis on India's rapidly evolving market. Our analysis reveals that the Indian mutual fund landscape has undergone remarkable transformation, with asset bases expanding nearly threefold between early 2020 and late 2025 (AMFI, 2025; ICICI Prudential AMC, 2025). This paper is an attempt to investigate AI's multifaceted applications across portfolio optimization, fraud prevention, investor services, and compliance operations. India presents a compelling case study given its limited current market penetration combined with substantial demographic advantages and advanced digital infrastructure. Through examination of regulatory developments, implementation patterns, and emerging challenges, we demonstrate that while AI offers significant potential for democratizing sophisticated investment services, successful integration requires careful navigation of algorithmic transparency concerns, data governance requirements, and regulatory frameworks. Our findings contribute to understanding technological transformation in emerging market financial services and provide evidence-based recommendations for sustainable AI adoption.

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.

Scientific peer review functions as the primary quality control mechanism in academic publishing. For decades the system remained manageable partly because producing a full manuscript required significant human effort which naturally limited submission volume. Generative artificial intelligence has changed this constraint by reducing the time and cost required to generate academic style text and enabling large scale submission growth. This introduces a new infrastructure level risk where the critical threat is not only low quality writing but sustained overload of editorial and reviewer capacity. This study frames AI-enabled submission flooding as a system stability problem and models the peer review workflow as a two stage queueing pipeline consisting of editorial screening and peer review. A stress testing approach is applied by increasing manuscript arrival rates above baseline levels and evaluating system utilization and backlog formation and expected review delay. The results show a tipping point behavior where moderate increases in submission volume can push the system beyond its stability boundary causing persistent backlog growth and rapid inflation of review timelines from weeks toward months and years. This collapse occurs when baseline peer review utilization is already high, such that a moderate proportional increase in submissions is sufficient to push reviewer demand beyond available service capacity. The findings indicate that content-based AI detection tools alone cannot prevent collapse under high volume conditions because congestion emerges when arrivals exceed service capacity regardless of manuscript origin. Therefore long term resilience requires defenses that reduce adversarial scaling and restore the balance between attacker cost and defender capacity through stronger identity verification and submission throttling and proof-of-personhood style controls.

The rapid expansion of quick-commerce (Q-Commerce) applications has transformed everyday purchasing by offering ultra-fast delivery supported by artificial intelligence. These applications increasingly use AI to streamline shopping decisions, enhance efficiency, and influence consumer behaviour. This study examines the use of AI-enhanced quick-commerce applications in everyday life, with a specific focus on their influence on efficiency in meeting daily needs and the role of trust in shaping consumers’ intention to continue using such applications. Using a survey-based quantitative approach, primary data were collected from 50 users of quick-commerce platforms and analysed using descriptive statistics, correlation, and regression analysis in Microsoft Excel. The results reveal a strong positive correlation between the use of AI-enhanced applications and efficiency in meeting daily needs, which is further supported by regression analysis indicating high explanatory power. Trust in AI-enhanced applications also shows a significant positive relationship with consumers’ intention to continue use, though with comparatively moderate explanatory strength. Overall, the findings confirm that AI-enhanced quick-commerce applications significantly simplify daily purchases, save time, and enhance consumer convenience, while trust emerges as a critical factor influencing continued usage. The study contributes to the growing literature on AI-driven consumer behaviour and highlights important societal implications of technology-enabled consumption in everyday life.

This study explores the role of Artificial Intelligence (AI)–based applications in learning sacred texts, with reference to The Bhagavad Gita. Drawing upon Bandura’s Social Learning Theory, the research compares three modes of learning—traditional text-based reading, listening to spiritual discourses, and AI-enabled application-based learning—based on the researcher’s sustained personal learning experience over five years. The study adopts a qualitative, reflective, secondary research approach and analyses how attention, retention, reproduction, and motivation operate across these learning modes. The findings suggest that AI-based learning tools introduce distinct cognitive and environmental stimuli that transform sacred learning from a passive, reverential activity into an interactive, self-regulated, and personalized learning experience. The study contributes to emerging literature on digital spirituality and AI-mediated learning by offering a theoretically grounded, experiential perspective.

Artificial Intelligence (AI) is increasingly influencing workplace practices by automating tasks, improving efficiency, and reshaping skill requirements across industries. As future professionals in business and service sectors, human resource specialisation students are expected to engage with AI-based tools in their careers. This study examines the awareness among human resource specialisation students regarding the use of artificial intelligence at the workplace. The research assesses students’ understanding of AI concepts, sources of awareness, extent of AI usage, perceptions of workplace relevance, willingness to adopt AI after graduation, and challenges faced in learning AI. Primary data were collected through a structured questionnaire administered to undergraduate human resource specialisation students and analyzed using percentage analysis and correlation statistical test. The findings indicate that although most students possess basic awareness and a positive attitude toward AI, gaps remain in practical exposure, formal guidance, and curriculum integration.

In times when Artificial intelligence is gaining centre stage of the development of mankind and his endeavours, one faces a dilemma about the role of Artificial Intelligence in the literature. Artificial intelligence is rapidly integrating itself into the literary creations today and has redesigned the notions about authorship, creativity and originality. The dilemma whether human imagination is more creative or the machine algorithm is something one needs to address today. While the traditional literature is filled with personal experiences, emotions and culture around the author who creates the poem or novel or a book and has a unique style. Artificial intelligence on other hand can mimic the style, thematic complexity and narrative structures thus, creating something original in its own style. The central issue of the dilemma is originality. The ones using artificial intelligence in literature argue that literature has never been absolute, as all writings exits within the network of influence, inspiration and reinterpretation. This paper explores these both dimensions of dilemma and collected opinions of individuals who are studying or teaching literature in various languages, alongwith those who are not scholars of literature but do use artificial intelligence for some literary creation.

This study analyzes the impact of research conference participation and the use of Artificial Intelligence (AI) tools on the professional development of higher education teaching faculties. Primary data were collected from 35 faculty members using a structured questionnaire and analyzed through Spearman’s Rank Correlation Coefficient. The results indicate a weak and statistically insignificant relationship between conference participation and professional development, while a strong and significant positive relationship was found between AI tool usage and professional development. The study concludes that AI tools play a more influential role than research conferences in enhancing faculty professional development.

This paper proposes a hybrid trading system that leverages quantum computing and artificial intelligence (AI) to trade exchange-traded funds (ETFs) efficiently. The system harnesses quantum computing’s ability to process vast data points simultaneously, employing algorithms like quantum annealing for accelerated decision-making and portfolio optimization. AI models, powered by deep learning, reinforcement learning, and natural language processing (NLP), analyze financial articles to train on current affairs, enabling the system to imitate human-like decision-making by extracting sentiment, trends, and contextual insights from real-time news. This synergy delivers faster analysis and superior risk control over classical trading systems. The framework integrates quantum processors with classical computers via a cloud infrastructure for seamless data flow. Experimental analysis using ETF market datasets and simulated intraday trading scenarios demonstrates that the hybrid approach achieves faster analytical convergence, improved risk management, and more balanced portfolio allocations compared to traditional classical trading systems. Also, the proposed model enhances trading performance without increasing market volatility, thereby supporting market stability. This research demonstrates quantum computing’s practical application in ETF intraday trading, boosting profits without disrupting market stability, to build resilient, high-performance financial trading platforms.

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.