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]

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.

Purpose: The study aims to analyse the impact of AI chatbots on consumer trust and confidence in purchase decisions, focusing on awareness, usage patterns, perception, and satisfaction on shopping and service websites. It also examines whether chatbot interactions influence consumer confidence during online purchases. Research Methodology: A descriptive research design was used. Primary data was collected via a structured 18-question Google Forms survey from 47 respondents using convenience sampling. Secondary data came from research papers, journals, articles, and online sources. Data was analysed using percentages and descriptive interpretation. Results: High awareness and usage of chatbots were observed, mainly for customer support and quick query resolution. Chatbots positively influence confidence in routine purchases, but trust remains moderate due to accuracy issues and lack of human touch. Respondents prefer human support for expensive or complex decisions. Conclusion: AI chatbots enhance online customer experience by improving efficiency, confidence, and satisfaction in routine purchases. However, complete trust is limited, and integration with human support and continuous technological improvements are essential to strengthen consumer trust.

The rapid digitalisation of workplaces in India has significantly altered work patterns, often extending work-related communication beyond official working hours and blurring the boundaries between professional and personal life. In response to these emerging challenges, the Right to Disconnect Bill, 2025 has been proposed to safeguard employees from after-hours work obligations. Against this backdrop, the present study examines the proposed Right to Disconnect Bill, 2025 and its potential impact on work–life balance in India. The study adopts an exploratory research design to assess employees’ awareness, perceptions, and anticipated effects of the proposed legislation. Primary data were collected through a structured questionnaire supported by secondary data. Descriptive and inferential statistical techniques were employed to analyse the perceived relationship between the proposed right to disconnect and key dimensions of work–life balance, including personal time management, psychological well-being, job satisfaction, and work-related stress. The findings suggest that while employees hold positive perceptions of the Right to Disconnect, these perceptions do not immediately translate into improved work–life balance. RTD is largely viewed as a forward-looking safeguard rather than an indicator of existing work–life conditions. The results highlight that organisational culture and individual coping mechanisms play a significant role in shaping employee’s work–life balance.

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.

The growing tendency of internet aggression, cyberbullying, and toxic communication has generated the necessity of smart technology to promote desirable digital behaviour. Although psychologists have gone a long way in creating and testing digital interventions that enhance empathy, cooperation, and positive interaction, they have not yet been applied in real technological systems. The current research suggests an Artificial Intelligence (AI) model that makes psychological understanding available in scalable and data-driven digital solutions to curb online toxicity and encourage prosocial behaviour in adolescents and young adults. The suggested framework is designed based on three mutually reinforcing dimensions, namely, proactive, interactive, and reactive interventions, each of which is accommodated by the properties of user interaction timing and nature. Prevention-based solutions will narrow down the adverse interactions on the internet by using educative prompts, emotional awareness devices, and the digital literacy module provided through AI capabilities. The interactive interventions utilise the real-time monitoring and adaptive feedback tool through natural language processing (NLP) and sentiment analysis in order to promote self-regulation and empathy in online interactions. Reactive intervention is premised on Reactive post-event reflection and behavioural strengthening, which involve the provision of Restorative feedback, online counselling referral mechanisms as well as peer-support. The combination of these layers will result in a complete ecosystem that is toxic in the prevention of online behaviour and responsive. The theoretical framework revolves around the methodological integration of the supervised and reinforcement models of learning with the socio-behavioural data sets when distinguishing linguistic and affective signals of aggression, empathy and cooperation. The lessons inform the dynamic provision of the interventions and consequently contextual lessons with the use of the ethical data. The study also embraces the principles of participatory design because the educators, psychologists and adolescent users are invited in system verification to enhance usability and credibility. There are preliminary signs that AI-inspired interventions grounded on the psychological theory and balanced with interdisciplinary cooperation can result in a drastic decrease in cases of verbal aggression and an increase in the number of cases of empathy and meaningful discussions in the virtual environment. The paper is also an extension of the existing discussions in the field of AI ethics, digital well-being and social technology because it provides a path towards transforming AI into a means of behavioural empowerment and digital citizenship rather than a surveillance tool. It suggests cooperation among the industries to transform technological innovation not only to be safer, but also caring, empathetic, and inclusive in the digital world. The proposed AI application can be duplicated as an evidence-based strategy of the promoting of the positive internet communication within the educational, social, and community platforms.

As global mental health systems face an unprecedented surge in demand, the traditional intake process has become a significant bottleneck, often delaying critical care for weeks or months. This study explores the efficacy of Artificial Intelligence (AI) as a frontline tool for preliminary psychological screening, comparing its diagnostic precision and patient-reported outcomes against traditional human-led clinical interviews. In a controlled experimental setting, we recruited N = 120 adult participants seeking outpatient services. These participants were randomly assigned to either an AI-led intake cohort (using a fine-tuned Natural Language Processing model) or a control group led by Licensed Master Social Workers (LMSWs). Our primary metrics included diagnostic congruence with a "gold standard" independent evaluation, the speed of symptom disclosure, and the quality of the working alliance. The findings indicate a paradoxical "Disinhibitory Effect": participants in the AI cohort demonstrated an 88% diagnostic alignment with independent supervisors, statistically surpassing the human-led group’s 82%. Crucially, the AI system elicited disclosures of "sensitive" clinical data—including substance abuse and suicidal ideation—significantly earlier in the interaction. While the AI group reported lower scores on the Working Alliance Inventory (WAI) regarding empathy, the data suggests that the perceived anonymity of the machine reduces social desirability bias and impression management. This study concludes that AI-driven intake tools offer a robust, scalable solution for clinical triaging. By standardizing the data collection phase, these systems allow human clinicians to focus their expertise on high-level therapeutic intervention, effectively bridging the gap between clinical efficiency and human-centered care.