AI tools are software applications that use algorithms to understand text, images, or speech and then generate helpful outputs such as explanations, summaries, or practice questions. AI tools help students learn faster, stay organized, and improve their writing and understanding of difficult concepts when used properly and ethically. They act like on‑demand tutors, writing assistants, and study planners that support—not replace—real learning. Students must have the skills to understand and utilize AI tools for learning and educational purposes. This research paper seeks to understand students' awareness of and use of AI tools for learning. AI is deemed to be the future-shaping industry and business. There are various tools available at the disposal of students, like chatbot tutors, writing and grammar, revision and summarizing, project assistant, problem solving and analytical tools and many more. This research Paper tries to find out to what extent students are aware of and using these tools in daily learning. The research methodology includes primary and secondary data analysis.

Artificial Intelligence (AI) has evolved from a tool for automation to a powerful mechanism for rational decision support. AI is useful to generate mathematical insights for taking decisions that optimize strategic, operational and predictive outcomes. In this paper theoretical foundations and practical implementations of AI-driven decision systems is studied. It will also highlight connections between mathematical models and AI reasoning which will help in decision making across domains. Among the crucial mathematical fields that support AI for decision-making are linear programming, probability, game theory, graph theory and combinatorics. In this paper the study is concentrated on importance of linear programming problems in AI for decision making.

Environmental degradation and climate change have created an urgent need for financial support for sustainable development. This study examines how artificial intelligence (AI) can enhance green finance and promote sustainable investment. AI technologies help to improve the assessment of environmental, social, and governance (ESG) factors, and support better decision-making through data analysis. As a result, green financial instruments such as green bonds, green loans, and climate funds become more transparent and efficient. The study employs a qualitative and conceptual approach, drawing on a review of existing research literature, policy documents, and reports from financial and environmental institutions. Special attention is given to India’s green finance initiatives and policy efforts. The findings represent that AI-enabled green finance helps direct funds toward environmentally responsible projects, particularly in the renewable energy sectors like solar and wind power, while reducing financial and climate related risks. The study also highlights the important roles of government policies and private sector participation in expanding green finance and supporting sustainable development goals. At the same time, challenges such as poor data quality, ethical concerns, and the need for clear regulatory frameworks remain significant, especially in emerging economies like India. Overall, the study concludes that combining artificial intelligence (AI) with green finance can play a crucial role in achieving long-term development in India.

Artificial Intelligence (AI) has completely revolutionised the way we use computer technologies. The rapid integration of Artificial Intelligence (AI) into academic research writing has transformed scholarly communication by enhancing efficiency, accessibility, and linguistic quality. However, this technological advancement raises significant ethical concerns related to authorship, originality, accountability, and academic integrity. This study examines the ethical dimensions of use of AI tools for research writing, focusing on researchers’ awareness, perceptions, and responsible usage of AI tools. Using a descriptive and analytical research design, primary data was collected through a structured questionnaire administered to faculty members and research scholars. The findings reveal that AI is widely accepted by researchers as a supportive tool for language refinement and formatting, but there is strong resistance toward its use in generating core intellectual content. The study revealed that researchers are well aware about the risks on extensive use of AI tools and agree that it’s use must be limited to only cosmetic aspect of content presentation rather the whole research itself. It also identifies a positive relationship between ethical awareness and responsible AI usage, highlighting the crucial role of institutional guidelines and ethics training.

Social media has become an integral part of daily life. With the widespread use of platforms like Instagram, Facebook, YouTube, and Twitter, businesses increasingly rely on social media advertisements to attract and engage potential customers. Due to high engagement of young demographics on social networking platforms, it is important to understand how social media advertisements influence their buying behaviour. This study investigates the influence of social media advertisements on the buying decisions of degree college students, both undergraduates and postgraduates from selected colleges in Dombivli City.

The rapid integration of Artificial Intelligence (AI) in digital commerce has transformed the way consumers interact with products prior to purchase. AI-powered virtual product experiences, particularly Augmented Reality (AR) – based try-on features, enable consumers to visualize products in real time, thereby reducing uncertainty and enhancing decision-making. This study examines the role of AI-enabled virtual product experience in shaping consumer buying behaviour. Primary data was collected through a structured questionnaire from 80 respondents who actively engage in online shopping and have prior exposure to AR try-on technologies. The study employs descriptive statistics, correlation, and regression analysis using Microsoft Excel to assess the relationship between virtual product experience and consumer buying behaviour variables such as purchase confidence, hesitation, decision speed, and likelihood of product returns. The findings reveal a significant positive relationship between AI-driven virtual product experience and consumer buying behaviour, highlighting the growing importance of immersive technologies in digital retail strategies.

The rapid evolution of AI has dramatically changed the view of scientific research and technological development by making possible systems that could sense, decide, and act with minimal human intervention. The study investigates the contribution of AI-driven intelligent subsystems towards smart system design and automation of diverse application domains. The ways that autonomous control structures, data-driven decision processes, and adaptive learning models enhance the efficiency, scalability, and dependability of contemporary automated systems are highlighted.

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.

In the rapidly developing era of AI (Artificial Intelligence) major changes are observed in society, from helping humans to changing and influencing the decision-making of the human. The way AI is influencing the human to make decisions according to the algorithms, suggestions and behaviour. Which is affecting the overall primary human-driven decision-making to dependency on technological intelligence. This revolution raises the important query regarding self-rule, responsibility and the revolving role of humans in this era. The primary objective of this study is to observe how AI is gradually changing the way human behaves and take independent decisions with or without the help of Artificial Intelligence. Which identifies the pros and cons of the technology over the human behaviour and decision-making process, and simultaneously explores the ethical implications increasing due to the dependency on the intelligent systems. The study reveals both quantitative and qualitative method showing relevant information has been collected from people's choices, research papers, and online sources related to artificial intelligence, human behaviour, and ethics. Aiming to understand behavioural patterns and ethical concerns from human-AI interactions. The findings indicate that due to continuous AI-human interactions, the accountability and the responsibility among the human is gradually changing, which is leading towards high dependency on AI, which regulates algorithms, suggestions, and choices. The results show that there is a need for conscious human control over decision-making and the responsible use of AI, to ensure that the increase in technological advancement supports, rather than replaces, human decision-making abilities.

Severe physical conditions such as locked-in syndrome, amyotrophic lateral sclerosis (ALS), and post-stroke paralysis can greatly limit a person’s ability to speak or move, even though their thinking and understanding remain unaffected. This mismatch between cognitive ability and physical expression creates major obstacles in communication and everyday independence. This paper investigates how artificial intelligence (AI), when combined with neural interface technologies, can help overcome these limitations and provide more effective means of interaction. The study focuses on the use of brain–computer interfaces (BCIs) and neuroprosthetic systems that capture and interpret neural signals directly from the brain. AI-based approaches are applied to process these signals and transform them into practical outputs, including text, speech, or control commands for assistive technologies. Adaptive learning models allow the systems to adjust to individual users, leading to improved performance and reliability over continued use. The findings indicate that AI-supported neural interfaces significantly enhance communication efficiency and usability compared to conventional assistive methods. Beyond communication, these technologies also enable users to operate computers, mobility devices, robotic aids, and smart systems within their environment. Overall, the paper concludes that AI-driven neural interfaces hold considerable promise for improving communication, autonomy, and quality of life for individuals with severe physical impairments.

The rapid integration of artificial intelligence (AI) technologies in education has transformed traditional learning practices and created new opportunities for enhancing students’ academic experiences. The present study examines the impact of AI-based learning tools on the academic behaviour of undergraduate students, with specific focus on study behaviour, time management behaviour, and academic engagement. The study also investigates the influence of behavioural intention to use AI-based learning tools on students’ time management practices. A quantitative research design was adopted, and primary data were collected from 173 undergraduate students using a structured questionnaire. The collected data were analysed using descriptive statistics and simple linear regression techniques to test the proposed hypotheses. The findings revealed that AI-based learning tools have a significant positive effect on study behaviour and academic engagement. Furthermore, behavioural intention to use AI-based learning tools was found to significantly improve students’ time management behaviour. The results indicate that the adoption of AI-supported learning platforms enhances students’ study habits, promotes effective time utilization, and increases active involvement in academic activities.

Artificial Intelligence (AI) has emerged as a transformative and powerful tool in disaster management, significantly enhancing the capacity of governments, humanitarian organizations, and emergency response agencies to prepare for, respond to, and recover from both natural and human-induced disasters. With the increasing frequency and intensity of disasters driven by climate change, rapid urbanization, population growth, and environmental degradation, traditional disaster management approaches are often insufficient. This growing complexity has created an urgent need for advanced, data-driven, and predictive solutions. AI technologies such as machine learning, deep learning, computer vision, and natural language processing play a critical role in addressing these challenges. Machine learning models can analyze large volumes of historical and real-time data to predict disaster risks, identify vulnerable areas, and forecast the potential impact of events such as floods, earthquakes, hurricanes, and wildfires. Computer vision techniques enable the analysis of satellite imagery, drone footage, and surveillance data to assess damage, monitor affected regions, and support search-and-rescue operations. Natural language processing helps analyze social media posts, emergency calls, and news reports to extract real-time information about disaster conditions and public needs. By enabling faster decision-making, accurate risk assessment, and efficient allocation of resources, AI improves coordination among response teams and enhances situational awareness during emergencies. Furthermore, AI-driven systems support post-disaster recovery by assisting in damage assessment, infrastructure planning, and policy development aimed at building long-term resilience.

For today, the use of AI and its tools has become an important part of human life. AI is used in various domains such as healthcare, enterprises, chemical industries, e-commerce, and logistics due to its popularity. AI helps people work more easily and efficiently and also provides suggestions for difficult problems and decision-making. AI tools help people learn faster and also offer creativity, ideas, and opinions. They help students make their study notes more exploratory by shortening and summarizing content. AI can track a person’s behaviour in particular tasks and help them recognize the things most suitable for them. AI chatbots help humans not feel alone; they can communicate with humans and provide emotional support when needed. AI can help people prepare for interviews, build strong self-confidence, and support skill development. There are many advantages of AI, but it also influences human behaviour, thinking, and social interaction with friends and family. This research helps identify both the positive and negative impacts of AI on human psychology. AI has changed the interaction between humans and machines. Instead of human psychologists, AI chatbots have become close companions for humans to discuss issues, seek suggestions, and make decisions. This is dangerous because it can lead to isolation, reduce family and friend interactions, increase stress, and ultimately reduce trust, closeness, and emotional attachment. Initially, it feels like AI is booming and making daily tasks easier, but gradually it becomes a daily necessity. We cannot imagine working or creating documents without using it. As a result, we may lose our ability to think independently, make decisions, and develop critical thinking skills. This can create stress, anxiety, and unconscious dependence because AI use becomes habitual, ultimately affecting mental health. AI also affects personal data, privacy, and identity. Many times, personal details may be shared with third parties, which is unsafe, and data misuse is possible. Everyone should understand how to use AI wisely and ethically. For a better future, we must understand the psychological impact of AI and learn how to use it without harm. Balancing AI usage and human psychology is essential. This study helps identify the pros and cons of AI and shows how these issues can be managed wisely and intelligently, because AI is created by humans. Therefore, we can control and use AI for good practices only, with limited and mindful usage, and understand where to stop. There is a need to develop guidelines that ensure its responsible use for human well-being.

This research paper aims to investigate the principles and applications of Green AI as a critical tool for achieving environmental sustainability. The primary goal is to synthesize existing knowledge and define a clear framework for developing energy-efficient artificial intelligence systems. The key objectives are to: (1) review current Green AI methodologies (2) identify major challenges in implementation (3) propose a strategic roadmap for low-carbon AI development. The methodology involves a systematic literature review and conceptual analysis of case studies where AI has been successfully used to optimize energy use in sectors like smart grids and logistics. By analyzing these applications, the paper establishes core design principles and proposes a conceptual model that aligns AI innovation with sustainability goals, helping to mitigate the technology's carbon footprint.

The increasing complexity and volume of cyber threats have exposed limitations in traditional Security Information and Event Management (SIEM) systems, which primarily rely on static, rule-based detection mechanisms. Recent advancements in Artificial Intelligence (AI) have enabled the development of intelligent threat detection models capable of identifying anomalous patterns and previously unseen attacks. This research paper presents a comparative study between AI-based threat detection approaches and traditional SIEM techniques to evaluate their effectiveness in modern cybersecurity environments. The study adopts a survey-based research methodology complemented by a conceptual AI detection framework to analyse key parameters such as detection accuracy, false-positive rates, response efficiency, and analyst workload. Statistical hypothesis testing is employed to assess whether AI-driven methods provide a significant improvement over conventional SIEM systems. The findings aim to highlight the potential benefits, challenges, and ethical considerations associated with deploying AI in cybersecurity operations. This research contributes to ongoing discussions on the responsible adoption of AI for enhancing cyber threat detection capabilities while maintaining transparency, security, and human oversight.

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.

Many young people today face significant stress, overthinking, and anxiety from schoolwork, jobs, social pressures, and major life changes. Introverts often struggle more, feeling shy or drained around others, which leads to loneliness and trapped thoughts. Therapy is effective but costly, time-consuming, and inaccessible for many. In contrast, AI offers quick, free, anytime support via smartphones with simple tips to calm down and think positively. Previous studies suggest that AI based tools may support emotional regulation, though evidence remains limited and context dependent. This study uses an online survey to collect responses from young people. AI remains safe and effective when it directs users to professional help for severe issues. It can also reach those who hide worries, feel too shy to speak, or withdraw from friends and family due to fear of judgment. Overall, AI tools are perceived by many youth as accessible and non-judgmental sources of emotional support.

As artificial intelligence becomes part of everyday decision-making, trust in these systems is no longer optional - it is essential. While most AI research focuses on improving accuracy, people often interact with systems that provide little to no explanation for their decisions. This study explores a simple but important question: do people trust AI systems that explain their decisions more than those that are highly accurate but opaque? To examine this, we compare two simulated AI models. One model delivers highly accurate decisions without explanation, while the other provides clear, understandable explanations with slightly lower accuracy. Participants are presented with AI-generated decisions in a controlled scenario and are asked to evaluate their level of trust, perceived fairness, confidence, and willingness to rely on each system. The results indicate that transparency plays a significant role in shaping user trust. Participants generally show a stronger preference for AI systems that offer explanations, even when they are informed that these systems may be marginally less accurate. Explanations help users feel more confident, involved, and assured that decisions are being made fairly. These findings suggest that accuracy alone is not sufficient for building trustworthy AI. Instead, explainability should be treated as a core design principle, especially in applications where human judgment, accountability, and ethical concerns are critical.

The adoption of electric vehicles (EVs) is essential for achieving sustainable transportation and reducing greenhouse gas emissions. Despite technological advancements and policy incentives, EV adoption remains slower than expected, largely due to psychological barriers influencing consumer behavior. This study examines key psychological factors such as range anxiety, perceived risk, resistance to change, and lack of trust in EV technology that hinder consumer adoption. These barriers are often driven by subjective perceptions and misinformation rather than actual technological limitations. The research further explores the role of Artificial Intelligence (AI)-based Decision Support Systems (DSS) in addressing these psychological challenges. AI-driven DSS provide personalized insights through data analytics, predictive modeling, and real-time simulations, enabling consumers to better understand EV performance, charging feasibility, and long-term cost benefits. By reducing uncertainty and enhancing perceived usefulness, AI-based decision support tools can positively influence consumer attitudes and adoption intentions. Using a Quantitative research approach, the study aims to evaluate the effectiveness of AI-driven decision support systems in mitigating psychological barriers and supporting informed EV adoption decisions. The findings are expected to offer valuable implications for policymakers, manufacturers, and technology developers seeking to accelerate electric vehicle adoption.

This study studied how artificial intelligence (AI) is used in India's financial services industry and how it affects consumer experience, security protocols, and operational effectiveness. A study design based on surveys was used to gather information from 50 respondents in the Thane district of Maharashtra, India. The data was analyzed using descriptive statistics and hypothesis testing. The findings demonstrated a positive correlation between consumer experience, security precautions, and operational efficiency and the financial sector's adoption of AI. The results additionally indicated that there is a low level of consumer awareness and readiness to adopt AI in financial services. The report also suggested potential ways for AI to be used in the financial industry to improve fraud detection and prevention methods. The study has significance for the Indian financial services since it may be used to create and apply AI to enhance customer experience, security protocols, and operational efficiency.

The integration of artificial intelligence (AI) into the financial sector represents the most significant paradigm shift in modern economic history, characterized by the convergence of advanced machine learning, massive data availability, and high-performance computing. This research paper evaluates the multifaceted impact of AI on finance, focusing on three core pillars: innovation in trading and banking, growth through operational efficiency, and excellence in risk management. By 2025, the global AI market in financial services is projected to exceed $35 billion, with generative AI (GenAI) alone poised to reach $358.4 billion by 2032, reflecting a CAGR of 39.8%. This study analyzes how high-frequency trading (HFT) now accounts for over 70% of U.S. equity trades, leveraging reinforcement learning to optimize alpha generation. Furthermore, it explores the democratization of credit through AI-driven scoring models, which have improved predictive accuracy by 15-25%, effectively integrating millions of "credit invisible" individuals into the formal economy, particularly in the Indian fintech ecosystem. The research highlights institutional case studies, such as JPMorgan Chase’s COiN platform, which processes 12,000 documents per second, saving 360,000 labour hours annually. In India, the implementation of YONO 2.0 by the State Bank of India has reached 80 million registered users, reducing customer acquisition costs to one-tenth of traditional methods. Despite these advancements, the paper addresses critical systemic risks, including algorithmic bias, which shows error rates of up to 20% in underrepresented datasets, and the necessity for robust regulatory frameworks like the EU AI Act and the Reserve Bank of India’s "Seven Sutras". The findings suggest that by 2030, agentic AI will transform banks into "10x" institutions, where autonomous agents manage cross-functional reasoning, yielding over $1 trillion in additional value for the global banking sector.

In India, a significant proportion of the population lives with speech, hearing, or physical disabilities that limit their ability to communicate effectively in daily life. These challenges become more pronounced during interactions with essential public and private services such as educational institutions, healthcare facilities, government offices, and workplaces. While digital communication and accessibility tools have increased in recent years, most existing solutions are prominently designed for English-speaking users. This language bias overlooks India’s vast linguistic diversity and overlooked disabled individuals who primarily rely on regional languages and local dialects for communication, thereby widening the accessibility gap. Recent growth in Artificial Intelligence (AI) offers promising solutions to address these challenges and enable more inclusive communication systems. This paper explores the potential of AI-driven communication technologies—including speech-to-text, text-to-speech, sign-language recognition, and image-based assistive tools—to support disabled users in the Indian context. Particular emphasis is placed on the development and application of regional language models for languages such as Marathi, Gujarati, Tamil, Bengali, and Rajasthani, as linguistic diversity remains one of the most critical barriers to accessibility in India. The study further examines the role of Data Science techniques, such as large-scale dataset creation, pattern recognition, and natural language modeling, in improving the performance of AI systems across diverse accents, dialects, and culturally specific vocabularies. The analysis highlights that AI tools demonstrate substantially improved usability and accuracy when trained on region-specific datasets and designed with simple, intuitive interfaces and multimodal interaction capabilities that combine voice, text, and visual inputs. The findings emphasize the urgent need for the development of open-source regional language datasets, increased community participation in data collection, and systematic evaluation of AI tools across diverse linguistic settings. Strengthening these areas can significantly contribute to the creation of robust, inclusive, and culturally adaptive AI-based communication systems, ultimately enhancing accessibility and social participation for disabled individuals across India.

The modern era, with new innovations and research, has introduced Artificial Intelligence. AI will influence the way education is offered and the ways available for students to learn. The impact of AI will be very useful to the student community. At the same time, it may make young minds more dependent not only on technology but also on ideas and thoughts. New things getting introduced in technology has both positive and negative effects. AI is also not an exception to this fact. Awareness sessions should be conducted to avoid negative effects and bring about more positive effects. Modern technology should be used for better efficiency and improvements, but not at the cost of physical and psychological health. There are challenges ahead in terms of data privacy and security. Precautionary measures must be taken to avoid these negative influences and overcome the challenges. Research methodology: Primary and secondary data

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.

AI-based promotion integrates machine learning, generative AI, and predictive analytics to create highly personalized, automated, and dynamic marketing campaigns. By shifting from mass marketing to individual, real-time engagement, the utilization of artificial intelligence (AI) is instrumental in influencing current promotional strategies, as it facilitates personalized and data-driven consumer engagement, particularly in the context of Generation Z's purchasing decisions and brand perceptions. Using a descriptive and analytical research design, this study investigates the influence of AI-based promotions on the buying behaviour of Gen Z consumers in Mumbai. Primary data were collected from 190 respondents using a structured questionnaire and analyzed using SPSS with non-parametric tests. The results suggested that buying behaviour are substantially influenced by AI-based promotions, particularly social media advertisements and personalized recommendations. The responsiveness to AI-based promotions was determined to be influenced by gender and frequency of purchase, while educational qualification and the amount spent did not demonstrate a substantial impact. The research also emphasizes that the development of brand trust and purchase intention is facilitated by engaging AI-driven interactions and accurate recommendations, while the necessity of ethical and transparent AI use is underscored by privacy and data security concerns.

The rapid integration of Artificial Intelligence (AI) in accounting has significantly transformed traditional accounting practices. This study explores students’ perceptions of the impact of AI on the accounting profession, focusing on employability, skill requirements, and learning outcomes. A survey-based approach using a structured questionnaire was employed to collect data from accounting students. The study aims to assess awareness, attitudes, and readiness toward AI-driven accounting systems. Statistical tools were used to test formulated hypotheses. The findings provide insights into how AI influences students’ career perceptions and academic preparedness, offering valuable implications for curriculum design and accounting education.

Traditionally Accounting and Financial reporting were tasks which had to be done by a person. The tasks were repetitive, tedious and took a lot of time to complete. However, the recent era has seen,the Integration of Artificial Intelligence (AI) in the scope of accounting and financial reporting. As a result of this development ,this thesis is on Impact of Ai on professionals working in the field of accounting and financial reporting. Through this research paper this report proposes to put forth the Developments in the accounting and financial reporting structure due to the Integration of AI. This Report also proposes to enumerate the differences in the level of efficiency, time taken in case of Professionals before and after the era of AI Integration. As a CA student myself , I found myself quite driven to this thesis as it will play a big role in my future career too. This report is done by taking help of various articles and secondary sources. The scope of this Research includes history and present times of accounting and financial reporting. The expected outcome of this research is to put forth the big roles played by AI in careers of professionals. This Research matters as it will help the future professionals update themselves as required by the future Scenarios and it will also help the organizations to train future staff. In essence, this study hopes to shed light on how AI is quietly reshaping the profession and to help both individuals and organizations prepare for a future where human expertise and intelligent technology grow side by side.

Civic mindset refers to the sense of responsibility that individuals show towards society, including maintaining cleanliness, following rules, protecting public property, and actively participating in community development. In many underdeveloped and developing regions, the absence of such a mindset has become a major reason for persistent civic problems such as improper waste disposal, poor sanitation, damaged infrastructure, traffic indiscipline, and lack of community participation. Despite the availability of government facilities and regulations, these issues continue due to weak civic awareness and low citizen involvement. This field project titled “The Missing Civic Mindset: Causes and Solutions” aims to study the reasons behind this behaviour and suggest practical measures to improve civic responsibility among citizens. The study focuses on understanding the everyday civic problems faced by local communities and analysing how people perceive their civic rights and duties. It also examines the social, educational, economic, and administrative factors that contribute to the lack of civic discipline. Primary data was collected through direct observation, informal interviews, and interaction with local residents, while secondary data was obtained from articles, government websites, online reports, and social media sources. The study adopts a descriptive approach to capture real ground-level conditions rather than theoretical assumptions. The findings reveal that although many citizens are aware of civic rules, they do not take them seriously in daily life. Cleanliness issues remain common due to irregular waste collection, lack of dustbins, and a general belief that maintaining public spaces is solely the government’s responsibility. Community participation is low, and people often feel that individual efforts do not bring meaningful change. Youth show relatively higher awareness levels, but active involvement in civic initiatives remains limited. The study highlights that improving civic mindset requires a combined effort of awareness, behavioural change, effective governance, and community participation. In recent times, Artificial Intelligence has emerged as a supportive tool in addressing civic challenges by enabling digital platforms for issue reporting, monitoring, and faster response from authorities. AI-based applications allow citizens to report problems easily using images and location data, increasing transparency and accountability. Such technology helps bridge the gap between citizens and authorities while motivating people to participate actively in civic improvement. The project concludes that the missing civic mindset is not only a social issue but also a behavioural one that can be improved through education, youth involvement, strict enforcement of rules, and the smart use of technology. When citizens understand that their individual actions matter and are supported by efficient systems, long-term civic improvement becomes possible. Small responsible actions, combined with community effort and AI-enabled solutions, can significantly contribute to building a cleaner, healthier, and more progressive society.

This study examines the controversy surrounding Grok, an artificial intelligence system, with a particular focus on the concept of visual harm. As generative AI models increasingly produce and interpret visual content, concerns have emerged regarding the ethical, social, and psychological impacts of such technologies. The controversy around Grok highlights issues such as the creation of misleading or offensive images, and the potential misuse of AI-generated visuals. This paper analyzes the problem with AI tools like Grok and raises a question of who should be held responsible? By exploring these challenges, the study emphasizes the need for responsible AI design, stronger governance frameworks, and ethical guidelines to mitigate visual harm while balancing innovation and freedom of expression

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.