Impact of Artificial Intelligence on the Academic Environment

The adoption of Artificial Intelligence (AI) in educational fields has developed a great potential and problem related to the academic progress, critical thinking, mental abilities, and final student results. The increased application of generative AI, intelligent tutoring machines, adaptive learning systems, predictive analytics, and AI-assisted learning tools have altered the traditional learning environment, although issues have been raised about over-reliance on technology, lower-level thinking, algorithm biases and academic dishonesty. This literature review was a systematic investigation of recent articles relevant to the topic of Artificial Intelligence in Education (AIEd) and its effects on student engagement, academic achievement, cognitive growth, and learning customization. The focus was on emerging trends as ChatGPT in education, machine learning in education, educational data mining, personalized feedback, and smart classrooms. The review established that AI-assisted learning and individualized instructional setting have a positive effect on knowledge retention, student motivation, self-regulated learning, digital literacy, and problem-solving abilities. Learning analytics and adaptive learning systems enhance the quality of academic outcomes by providing personalized learning channels and real-time feedback. The results suggest that over-dependence on AI tools can negatively affect critical thinking, creativity, metacognition, and independent reasoning in case the pedagogy does not carefully incorporate it. The concern increasing on algorithmic bias, cognitive load, ethical issues, and the impact of large language models on academic integrity were also identified as a major concern in the review.

Artificial Intelligence (AI) is increasingly transforming higher education across the globe by reshaping teaching, learning, research, and institutional management. In developing countries such as Tanzania, the integration of AI into Higher Education Institutions (HEIs) presents both significant opportunities and complex challenges. Despite the presence of infrastructural and resource limitations, AI has the potential to revolutionize the educational landscape by improving access to quality learning materials, supporting data-driven decision-making, and enhancing administrative efficiency. This study explores the multifaceted impacts of AI on Tanzanian HEIs, with a specific focus on its current applications, benefits, challenges, and policy implications. The research draws upon recent empirical studies and secondary data to analyze how AI technologies such as intelligent tutoring systems, predictive analytics, and automated assessment tools contribute to personalized learning experiences and improved academic outcomes. Moreover, the study identifies the major barriers to AI adoption, including inadequate technological infrastructure, limited institutional capacity, insufficient AI-related policies, and persistent ethical concerns surrounding data privacy and algorithmic bias. Findings reveal that although AI adoption in Tanzanian HEIs is on the rise, the implementation remains fragmented and uneven across institutions. To fully harness AI’s transformative potential, the study recommends the development of comprehensive national AI policies, investments in digital infrastructure, and targeted capacity-building programs for educators and administrators. Additionally, the research emphasizes the need for ethical frameworks that promote fairness, inclusivity, and transparency in AI utilization. Overall, this paper underscores the necessity of a strategic and policy-driven approach to ensure that AI contributes effectively and equitably to the advancement of higher education in Tanzania.

Artificial intelligence (AI) is altering the hiring, training and management of staff in companies, thus transforming decision-making in human resource management (HRM) from past data-driven to present. This paper examines how artificial intelligence is changing HR best practices and highlights the main factors influencing AI adoption and the consequent effects on organizational effectiveness. We investigate how employee’s digital abilities, artificial intelligence experience and self-confidence in using technology impact AI integration in HRM through exploratory factor analysis (EFA), correlation analysis, network analysis, and decision tree regression. Although age and education have just a small impact, the results reveal that digital competency is the largest driver of AI adoption followed by artificial intelligence experience and self-efficacy. However, the adoption of artificial intelligence is not without difficulty. The main obstacles for companies trying to include artificial intelligence into HR are algorithmic bias, openness issues and ethical dangers. Organizations should invest in AI-focused training programs, guarantee fairness in AI-driven hiring and evaluations, and maintain human supervision to balance automation with ethical decision making to maximize AI while avoiding these risks. Future studies should investigate how the acceptance of artificial intelligence in HR changes over time, how it affects other sectors and how employees really feel about collaborating with HR systems driven by artificial intelligence. AI has great potential to improve HR efficiency and decision making with proper application; yet its success depends on ensuring it remains inclusive, ethical and transparent even as such.

This research explores the integration and impact of artificial intelligence (AI) within Emirati media organizations, focusing on adoption determinants, obstacles, and interventions. Utilizing a mixed-methods approach, the study incorporates qualitative data from 21 in-depth interviews with media professionals and a thematic analysis of 30 scholarly articles published between 2019 and 2024. The research aims to provide a nuanced understanding of AI's transformative effects on the UAE media landscape. Key findings reveal that AI is significantly enhancing content creation, distribution, and audience engagement processes within Emirati media organizations. AI technologies such as machine learning, natural language processing, and predictive analytics are being leveraged to improve operational efficiency, streamline workflows, and deliver personalized content to audiences. These advancements are driving competitive advantages and elevating the overall quality of media output. Despite these benefits, several challenges hinder widespread AI adoption. Technological barriers, including limited access to advanced AI tools and infrastructure, pose significant obstacles. Additionally, there is a pronounced shortage of skilled personnel capable of implementing and managing AI systems, which exacerbates concerns over job displacement and workforce anxiety. Ethical considerations, such as ensuring unbiased AI algorithms and protecting user privacy, are also critical issues that require attention. The study underscores the importance of continuous training and upskilling for media professionals to adapt to the evolving technological landscape. It highlights the role of robust ethical guidelines and governance frameworks in promoting responsible AI use. Government support, through targeted policies, funding initiatives, and incentives, is identified as a crucial factor in facilitating AI integration and addressing adoption barriers. The research concludes by providing strategic recommendations for media organizations and policymakers. It advocates for the development of comprehensive AI adoption roadmaps that include capacity-building initiatives, ethical oversight, and collaborative efforts between industry stakeholders. By addressing these areas, Emirati media organizations can harness the full potential of AI to drive innovation, improve audience engagement, and maintain competitiveness in a rapidly digitizing global media environment. This study contributes to the growing body of knowledge on AI in media, offering valuable insights into the specific context of the UAE. It provides a foundation for future research on AI's role in media and serves as a guide for organizations seeking to navigate the complexities of AI adoption.

Artificial Intelligence (AI) has become a transformative force in education, significantly influencing students. This research explores AI's impact on learning experiences, academic performance, career guidance, motivation, self-reliance, social interaction, and AI dependency. Utilizing a descriptive-comparative design, 194 student respondents were chosen through stratified sampling. The results show that students generally perceive AI positively. Students agree that AI enhances personalized learning, engagement, and critical thinking, although practical hands-on learning experiences received less favorable feedback. Academically, students concur that AI helps them identify weaknesses, improve assignments, and track progress, despite some reservations about its efficacy in exam preparation. For career guidance, students agree that AI effectively matches skills with career options, recommends internships, and provides resources, though it is less effective for long-term planning. Students also believe AI boosts motivation through gamified learning and progress tracking and fosters self-reliance via self-directed learning and critical thinking support. Socially, students agree that AI facilitates collaboration, peer learning, and networking. Additionally, students demonstrate a reliance on AI for their learning processes. Notably, female students report a more significant impact on social interactions than male students. The type of device used (laptop vs. cellphone) significantly affects the learning experience, with laptop users reporting a more substantial impact. Differences in AI's impact are noted among various courses, particularly benefiting education students more than those in hospitality management and agriculture. However, age and family income do not significantly influence AI's overall impact. Keywords: Artificial Intelligence, descriptive-comparative, impact on learning, higher education

While artificial intelligence (AI) offers transformative benefits for businesses, the road to successful adoption is often filled with technical, financial, and cultural hurdles. This chapter explores the most common barriers to AI integration and provides strategic solutions to overcome them, ensuring that businesses—especially small enterprises—can effectively harness AI’s potential. The chapter first examines technical difficulties, including data management issues, AI system integration challenges, and a lack of in-house expertise. It outlines practical solutions, such as data enrichment, phased AI implementation, and workforce upskilling, to help businesses navigate these complexities. Financial constraints, such as high initial costs, uncertain return on investment, and ongoing maintenance expenses, are also analyzed. The chapter provides cost-effective AI adoption strategies, including scalable AI solutions, leveraging cloud-based AI services, and securing grants or partnerships to mitigate financial risks. Additionally, the chapter delves into cultural and organizational resistance, addressing employee concerns, leadership alignment, and the need for AI literacy. Strategies for fostering an AI-ready culture include transparent communication, structured training programs, and leadership-driven AI advocacy. Lastly, ethical considerations—such as AI bias, transparency, data privacy, and regulatory compliance—are discussed to ensure responsible AI adoption. By the end of this chapter, business owners and entrepreneurs will be equipped with actionable insights to overcome AI adoption challenges and drive ethical, efficient, and sustainable AI integration in their organizations.

Artificial Intelligence (AI) is increasingly transforming the regulatory compliance landscape in the oil and gas industry. This paper examines the profound impact of AI on ensuring adherence to complex regulatory frameworks governing this sector. Regulatory compliance in the oil and gas industry involves adhering to a myriad of environmental, safety, and operational regulations, often posing significant challenges due to the volume and complexity of data involved. AI technologies, including machine learning, natural language processing, and predictive analytics, offer innovative solutions to these challenges. AI enhances data management and analysis by automating data collection, processing, and reporting, thereby increasing accuracy and efficiency. Predictive maintenance and risk assessment tools powered by AI can identify potential compliance issues before they arise, allowing for proactive measures. Moreover, AI-driven compliance monitoring systems enable real-time tracking of regulatory adherence, reducing the risk of non-compliance and associated penalties. Automated auditing and inspection processes further streamline compliance checks, ensuring thorough and consistent evaluations. Case studies demonstrate successful AI implementations in regulatory compliance, such as automated reporting systems in offshore drilling and predictive maintenance in pipeline management, which have resulted in improved compliance rates and reduced operational risks. However, the adoption of AI is not without challenges. Issues related to data quality and integration, cybersecurity, and regulatory acceptance pose significant hurdles. Additionally, ethical and legal considerations surrounding AI deployment must be addressed to ensure responsible use. AI holds substantial potential to revolutionize regulatory compliance in the oil and gas industry by enhancing efficiency, accuracy, and proactive risk management. As AI technologies continue to evolve, their integration into compliance processes will likely become more sophisticated, offering greater benefits and addressing current limitations. The future of regulatory compliance in the oil and gas sector will be increasingly shaped by the advancements in AI, driving both operational excellence and adherence to stringent regulatory standards.

Purpose: This paper examines the evolving impact of artificial intelligence (AI) on management consulting through both bibliometric and systematic review. Most of the authors have used either bibliometric or SLR in the past. It aims to provide insights into AI’s influence on consulting practices, highlighting key themes such as operational efficiency, human-AI collaboration, and ethical challenges. Design/Methodology/Approach: A bibliometric analysis of 195 scholarly publications (2010– 2024) was conducted using VOSviewer to map research trends, thematic clusters, and collaborative networks. A systematic literature review was also performed on a subset of 37 studies to extract more profound insights into AI's role in consulting. Findings: The analysis identifies a significant increase in AI-related consulting research post 2018. Dominant themes include AI-driven decision-making, consultant client dynamics, and sustainability. Emerging trends emphasize the integration of AI into strategic decision-making and the development of frameworks for ethical AI governance. Originality/Value: This work combines systematic review methods with bibliometric visualization to provide a new synthesis of research on AI in management consulting. It fills critical knowledge gaps by exploring AI's long-term strategic influence on consulting practices, its role in promoting sustainability, and the development of consultant competencies. Additionally, this study addresses underexplored areas such as the ethical challenges of AI integration, interdisciplinary approaches to understanding human-AI collaboration, and regional disparities in AI adoption, particularly in emerging markets. By bridging these gaps, the paper offers actionable insights for practitioners and a robust foundation for future academic inquiry. Practical and Social Implications: The findings provide consulting professionals with actionable strategies for adopting AI responsibly, fostering innovation, and addressing ethical and societal challenges. The study underscores the need for sustainable AI practices that align with global goals.

Artificial intelligence (AI) is transforming the mental health care environment. AI tools are increasingly accessed by clients and service users. Mental health professionals must be prepared not only to use AI but also to have conversations about it when delivering care. Despite the potential for AI to enable more efficient and reliable and higher-quality care delivery, there is a persistent gap among mental health professionals in the adoption of AI. A needs assessment was conducted among mental health professionals to (1) understand the learning needs of the workforce and their attitudes toward AI and (2) inform the development of AI education curricula and knowledge translation products. A qualitative descriptive approach was taken to explore the needs of mental health professionals regarding their adoption of AI through semistructured interviews. To reach maximum variation sampling, mental health professionals (eg, psychiatrists, mental health nurses, educators, scientists, and social workers) in various settings across Ontario (eg, urban and rural, public and private sector, and clinical and research) were recruited. A total of 20 individuals were recruited. Participants included practitioners (9/20, 45% social workers and 1/20, 5% mental health nurses), educator scientists (5/20, 25% with dual roles as professors/lecturers and researchers), and practitioner scientists (3/20, 15% with dual roles as researchers and psychiatrists and 2/20, 10% with dual roles as researchers and mental health nurses). Four major themes emerged: (1) fostering practice change and building self-efficacy to integrate AI into patient care; (2) promoting system-level change to accelerate the adoption of AI in mental health; (3) addressing the importance of organizational readiness as a catalyst for AI adoption; and (4) ensuring that mental health professionals have the education, knowledge, and skills to harness AI in optimizing patient care. AI technologies are starting to emerge in mental health care. Although many digital tools, web-based services, and mobile apps are designed using AI algorithms, mental health professionals have generally been slower in the adoption of AI. As indicated by this study's findings, the implications are 3-fold. At the individual level, digital professionals must see the value in digitally compassionate tools that retain a humanistic approach to care. For mental health professionals, resistance toward AI adoption must be acknowledged through educational initiatives to raise awareness about the relevance, practicality, and benefits of AI. At the organizational level, digital professionals and leaders must collaborate on governance and funding structures to promote employee buy-in. At the societal level, digital and mental health professionals should collaborate in the creation of formal AI training programs specific to mental health to address knowledge gaps. This study promotes the design of relevant and sustainable education programs to support the adoption of AI within the mental health care sphere.

Real-World Surveillance of FDA-Cleared Artificial Intelligence Models: Rationale and Logistics.

BackgroundWork-related mental health and psychological injuries are important issues related to people's livelihood and wellbeing. Currently, digitalization and intelligent technology have an extremely large impact on the workforce. China is actively promoting the deep integration of artificial intelligence (AI) and manufacturing, which may have important implications for the mental health of manufacturing workers. However, existing researches have paid little attention to the influence of AI on the mental wellbeing of workers in China. There is a lack of relevant empirical research, and the findings in existing studies are inconsistent.MethodsUsing data from the 2018 China Labor Force Dynamics Survey, this paper studies the impact of AI on the depressive symptoms of manufacturing workers and uses stepwise and bootstrapping methods to test whether overtime work and the work environment exhibit mediating effects. Robustness tests were performed by using alternative measures for the dependent and mediating variables. Finally, the heterogeneity in the impact of AI by skill level and generation was examined.ResultsAI can reduce the psychological depression scores of manufacturing workers by 1.643 points, which indicates that AI promotes workers' mental health. Working overtime is not a mediator between AI and mental health. However, the work environment is a mediator between AI and the mental health of manufacturing workers: it explains 11.509% of workers' mental health. The impact of AI on the mental health of manufacturing workers varies by skill level and generation. AI improves the mental health of low-skilled manufacturing workers by 2.342 points and that of manufacturing workers born before the 1980's by 2.070 points.ConclusionsThe application of AI is conducive to improvements in the mental health of manufacturing workers. Improving the work environment is a powerful way to increase the positive effects of AI on workers' mental health. The impact of AI on the mental health of manufacturing workers varies by skill level and generation. The mental health of low-skilled workers and workers born after 1980 is affected more positively by the adoption of AI.

This study examines the impact of artificial intelligence (AI) adoption on economic growth and unemployment across G7 countries and India. As AI emerges as a transformative technology, there is a need to understand its effects on labour markets and develop appropriate policy frameworks. The research analyses historical patterns of automation and technological change to provide context for the current AI revolution. The study employs a mixed-methods approach, combining quantitative statistical analyses with qualitative assessments. Variables examined include GDP growth rates, R&D investment levels, AI adoption rates, and productivity gains. Descriptive statistics, correlation analyses, regression models, and cluster analyses were conducted to identify relationships between key variables. Results reveal a significant technology adoption gap between high-tech leaders and developing economies. A growth paradox was observed in developing tech economies, where rapid AI adoption did not necessarily translate to proportional economic gains. The research found complex relationships between technological advancement, unemployment rates, and investment levels across countries. Cluster analysis identified three distinct groups: Advanced Economies, Technology Leaders, and Developing Tech Economies. ANOVA and chi-square tests confirmed statistically significant differences between these clusters. Multiple regression analysis on unemployment rates provided insights into the factors influencing job displacement. Key findings include: 1) AI adoption shows potential to significantly boost GDP growth and productivity, though effects vary by country. 2) The relationship between technological progress and unemployment is nuanced, defying simplistic narratives. 3) Investment levels strongly correlate with technological advancement, but other factors also play important roles. 4) Leaders in AI adoption exhibit certain common characteristics, offering potential lessons for other ...

This study investigates the application of Artificial Intelligence (AI) in business management, assessing its contributions to decision-making, operational efficiency, and customer service. It highlights both the opportunities and challenges presented by AI, with an analysis of case studies from companies such as Airbnb and Nordstrom to illustrate AIs influence on business processes and innovation. The research concludes that AI confers substantial benefits, including cost savings and data-informed decision-making, while also identifying critical issues such as labor market disruptions and data security that necessitate attention. The adoption of AI is contingent upon a companys size and resources, necessitating bespoke strategies for both large enterprises and small and medium-sized enterprises (SMEs).

This paper investigates previous research concerning the applications of artificial intelligence (AI) technology in electrical engineering (EE) systems. The EE is one of the fields where AI research gets a lot more attention. This paper examines the potential for AI transformation in EE due to advancements in modern technology and the urgent necessity for innovations in the sector. Recent research papers sourced from open literature constitute the survey dataset, facilitating a comprehensive overview of AI application across key domains of EE. The research focuses on the disciplines of renewable energy (RE), power systems, control systems, and power electronics. The research findings illustrate the benefits and challenges associated with the adoption of AI in these specific EE fields. A survey of the primary AI methodologies encompasses their successful applications and prospective research avenues for future endeavors. This research aims to offer academic guidance for scholars interested in the application of AI in EE by elucidating existing methodologies and fostering future innovation in this ever-evolving domain.

In the age of digital governance, Artificial Intelligence (AI) has become a key driver for improving strategic decision-making in public organizations. This study examines how AI adoption influences decision quality in Saudi public organizations, emphasizing the mediating role of organizational data readiness. Within the Vision 2030 transformation, effective AI use depends not only on technology but also on the organization’s ability to manage and govern data efficiently. Using a quantitative approach and data from senior and mid-level employees across multiple government entities, the study employed structural equation modeling (SEM) to test its hypotheses. Results revealed that AI adoption significantly enhances decision quality, and this effect is strengthened when organizations demonstrate high levels of data readiness, especially in governance, integration, and data quality. The findings align with recent research highlighting the importance of data maturity in public sector digital transformation. The study recommends training leaders in AI and data readiness, strengthening data-ecosystem capabilities, adopting AI decision-support tools, and regularly assessing data maturity for continuous improvement. So, this study is original in combining AI, data readiness, and strategic decision support into one framework, applied within Saudi organizations where such research is limited.