Artificial Intelligence (AI) has become a transformative technology in education, offering innovative tools that support learning and academic development. AI-based applications, such as intelligent tutoring systems, virtual assistants, Chatbots, and writing assistance tools, are increasingly being used by students for educational purposes. These technologies have the potential to transform traditional learning methods into engaging sessions by improving understanding, engagement, and overall academic performance. The study aims to evaluate the role of Artificial Intelligence in enhancing student academic performance among undergraduate students. The study helps to analyse how AI tools lead to improved understanding of academic concepts, quality of assignments, time management, and overall learning efficiency. It also helps to know the students' perceptions regarding the opportunities and challenges associated with the use of AI in academics. A descriptive research method is used. Descriptive and analytical methods are used to interpret the collected data. The primary data was collected through a structured questionnaire. Secondary data was sourced from journals, articles and online reports. The findings of the study are expected to indicate that AI plays a significant role in enhancing academic performance and offers various benefits. However, challenges like over-dependency on AI tools, accuracy and reliability issues, data privacy and security risk were also identified. The study highlights the importance of proper guidance and awareness about ethical use of AI tools to maximise learning outcomes while minimising over-dependence. The results of this research may help educational institutions and policymakers in framing effective strategies for the responsible use of the AI in education and enhance academic development.

The rapid growth of electronic waste (e-waste) has a significant impact on environmental, economic, and public health challenges worldwide. Traditional e-waste management and recycling methods are often inefficient, labor-intensive, and unable to cope with the increasing volume and complexity of discarded electronic products. This paper shows the study of the role of Artificial Intelligence (AI) in e-waste management and recycling processes. AI technologies such as machine learning, computer vision, and intelligent robotics enable automated identification, sorting, and disassembly of e-waste, leading to improved material recovery and reduced human exposure to hazardous substances.

Artificial Intelligence (AI) is playing an increasing role in how organizations handle communication. As businesses increasingly rely on digital platforms, issues such as slow responses, unclear messages, inconsistent interactions, and too much information have become common. AI tools like chatbots, email helpers, grammar checkers, transcription tools, and Customer Relationship Management (CRM) systems help reduce routine work, increase accuracy, and support better communication between employees and stakeholders (Davenport & Kirby, 2016; Kaplan & Haenlein, 2019). This paper explores how AI can improve the efficiency of business communication. Through a review of existing literature and conceptual models, it highlights the benefits of AI tools, as well as possible drawbacks and their impact on interpersonal communication skills (Prasad & Sharma, 2020; Russell & Norvig, 2021). Overall, the study suggests that combining AI tools with thoughtful human supervision can greatly improve organizational communication, while maintaining a good balance between technology and human judgment.

Net Asset Value (NAV) serves as the price at which investors buy or sell units of mutual funds. It is computed at the end of each business day using closing prices of securities held by the fund. NAV is a benchmark for tracking a fund’s performance and is updated daily for open-end funds. This article presents NAV prediction using XG Boost machine learning Algorithm. The proposed model suggests time series prediction model. Lower MAE / RMSE shows predictions are numerically close. Very low MAPE (~0.55%) indicates strong relative accuracy. It is quite effective, with forecasted values only marginally different from actual NAV. For daily NAV forecasting, such low errors are often considered very acceptable. Very low MAPE (~0.55%) indicates strong relative accuracy.

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.

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.

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.