Integration of AI into teaching methodologies in health training institutions in Tanzania

The integration of Artificial Intelligence (AI) into personal finance and wealth management has fundamentally reshaped financial behaviors and decision-making processes. The primary objective of this study is to evaluate the role of AI in influencing personal financial behaviors and wealth management outcomes. Specifically, it aims to determine how AI adoption, investment, and usage impact personal savings and net worth. This study adopts a quantitative approach, utilizing secondary data from trusted sources such as Our World in Data and the Federal Reserve Bank of St. Louis. The dataset spans from 2010 to 2022, capturing trends over a significant period of AI development and adoption. A multivariate regression model is employed to examine the relationships between the dependent variables, Personal Savings Rate and Change in Net Worth, and independent variables such as AI adoption rate, AI investment, and household debt-to-income ratio. Descriptive statistics, correlation analysis, and stationarity tests are conducted to ensure data reliability and model validity. Diagnostic checks, including heteroskedasticity tests and Durbin-Watson statistics, further validate the robustness of the results. The study reveals that AI adoption positively influences personal savings by encouraging disciplined financial behaviors, consistent with the findings of prior research. However, its impact on wealth accumulation is less direct, with AI investment showing a surprising negative association with changes in net worth. This indicates inefficiencies in resource allocation or lag effects in the benefits of large-scale AI investments. Traditional economic factors, such as household debt and spending habits, continue to play significant roles in shaping financial outcomes, highlighting the enduring influence of non-technological determinants. The study also underscores the role of macroeconomic variables, such as unemployment, in moderating AI’s impact, with precautionary savings behaviors emerging during periods of economic uncertainty. Based on the findings, several actionable recommendations emerge. For individuals, the adoption of AI-driven tools that promote financial literacy and track spending can enhance savings and improve overall financial health. Financial institutions should prioritize user-centric designs in AI platforms, ensuring accessibility and functionality for diverse demographics. Policymakers are encouraged to support initiatives that bridge disparities in AI adoption, such as digital literacy programs and affordable access to financial technologies. Moreover, strategic investment in AI tools that address wealth management complexities, such as portfolio optimization and risk assessment, is critical for improving long-term financial outcomes. Originality This study contributes to the growing body of literature on AI in finance by offering a dual focus on personal savings and wealth management. Unlike previous studies that often treat these domains independently, this research provides an integrated perspective, highlighting both the synergies and divergences in AI’s impact. The findings on the nuanced relationship between AI investment and financial outcomes offer a fresh lens for evaluating the effectiveness of technological advancements. Furthermore, the study’s emphasis on traditional economic factors alongside AI-related variables underscores its originality in bridging the gap between technological innovation and foundational economic principles. This approach provides a robust framework for future research and practical applications in finance.

Artificial Intelligence (AI) has the potential to reshape family medicine by enhancing clinical, educational, administrative, and research operations. Despite AI's transformative potential, its adoption is inconsistent, and strategic frameworks remain limited. This study explores current AI adoption, organizational policies, integration priorities, and budget allocations within family medicine departments. A survey of 218 family medicine department chairs in the US and Canada was conducted via SurveyMonkey from August 13 to September 20, 2024, as part of the Council of Academic Family Medicine (CAFM) Educational Research Alliance (CERA) omnibus project. Survey questions assessed current and planned AI utilization, presence of formal departmental or organizational policies (defined as written guidelines, strategic plans, or frameworks), integration priorities, and budget allocations. Data were analyzed using Chi-square tests, Wilcoxon Rank Sum tests, and Kruskal-Wallis tests, with a primary focus on bivariate comparisons. The survey achieved a 50.9% response rate (111/218). Current AI use was reported by 56.9% (62/109), while 37.6% (41/109) indicated formal organizational policies. Primary goals for AI integration included improving clinical operations (52.3%), administrative streamlining (16.5%), educational applications (11.9%), and research (4.6%). Budget allocations were minimal (median, 0%; mean 2.4%), though departmental budgets likely underestimate actual institutional investment in AI. Departments reporting AI use had significantly more full-time equivalent faculty (median, 40.0 vs 25.5, P = .023). Geographic and chair demographics were not significantly associated with differences in AI adoption. AI integration in family medicine departments is viewed as essential, though current adoption is limited by uncertain strategic planning and minimal departmental budget allocations, potentially reflecting reliance on centralized institutional information technology (IT) investments. While AI is widely viewed as important, structured policy frameworks and implementation strategies are still developing. Further research is essential to guide policy development and strategic investment to ensure AI's safe, efficient, and effective integration into family medicine.

Purpose This study aims to investigate the interplay between artificial intelligence (AI) integration, organizational digital culture, human resource management (HRM) practices and employee sustainable performance in luxury hotels in Malaysia. It seeks to elucidate how AI adoption influences organizational dynamics, shapes HRM practices and impacts employee sustainable performance over time. Design/methodology/approach Using a quantitative approach, survey questionnaires derived from prior research were utilized. Analysis using G*Power software determined an appropriate sample size, with psychometric evaluation validating scale development. Statistical analyses using Statistical Package for Social Sciences (SPSS) 28.0 and SmartPLS 4 confirmed data reliability and validity. Findings Out of the five hypotheses, three were supported. A positive relationship was found between AI adoption and employee sustainable performance, highlighting AI’s potential to enhance productivity and job satisfaction. However, the relationship between AI adoption and organizational digital culture was not supported. On the other hand, HRM practices positively influenced employee sustainable performance. In addition, organizational digital culture was positively associated with employee sustainable performance, underscoring the role of digital fluency in driving workforce productivity. Conversely, AI failed to moderate the relationship between HRM practices and employee sustainable performance. Research limitations/implications The study’s focus on luxury hotels in Malaysia and its reliance on cross-sectional data, suggesting the need for longitudinal designs and diverse organizational contexts in future research. Comparative studies across sectors and countries could offer insights into variations in AI adoption practices and their impact on organizational performance. Originality/value This study contributes to theoretical frameworks by empirically examining complex relationships between AI integration, HRM practices, organizational digital culture and employee performance, emphasizing the importance of considering organizational context and cultural factors in understanding the implications of AI adoption for sustainable performance enhancement.

The integration of Artificial Intelligence (AI) technologies in education has gained significant attention, particularly in the context of higher education, in recent years. Despite concerns about academic integrity, academics recognise the opportunity for AI to foster critical thinking and prepare students for real-world scenarios. However, its integration into courses requires careful consideration of course objectives and ethical implications. This study explores the utilisation of AI in higher education settings, focusing on its role as a learning tool. The study systematically reviewed 87 empirical studies from databases between 2014 and 2024 to investigate the benefits, challenges, and implications of incorporating AI into higher education. Additionally, it examines the potential impact of AI on teaching methodologies, student outcomes, and the overall learning experience. The findings of this study underscore the significant influence of AI integration in higher education on teaching methodologies. This integration promotes personalised and adaptive instruction, enhancing student engagement, performance, satisfaction, and overall learning experiences. However, the adoption of AI in higher education raises significant ethical concerns that demand careful consideration. These concerns include data privacy, algorithmic bias, intellectual property rights, and academic integrity. Academics' perspectives on AI adoption vary based on technological proficiency, pedagogical beliefs, and institutional support. Successful AI integration necessitates alignment with pedagogical theories such as constructivism, connectivism, and self-directed learning, ensuring a robust technical infrastructure and addressing ethical considerations to maximise benefits while minimising risks.

Introduction: The integration of artificial intelligence (AI) into higher education is swiftly revolutionizing pedagogical methodologies, in conjunction with learning processes and research paradigms. The interdisciplinary potential of AI within academic settings was examined in this study, employing a case study conducted at the University of Tirana. Through the utilization of bibliometric analysis and survey-based research, this study comprehensively investigates the swiftly emerging trends in AI applications, students' significant familiarity with AI technologies, as well as the substantial challenges impeding broader adoption. The bibliometric analysis highlights significant exponential growth in AI research, particularly within pivotal domains such as finance and accounting, thereby emphasizing the swiftly increasing relevance of blockchain and automation. The survey indicates a robust enthusiasm among students for AI in educational settings. More than 90 percent of students actively incorporate AI tools in their project work. Nonetheless, resource limitations and ethical considerations, including privacy, data security, and algorithmic bias, pose considerable challenges to the widespread adoption of AI, despite the prevailing enthusiasm. Objective: The aim of this research is to examine the incorporation of Artificial Intelligence (AI) within the realm of higher education. This analysis concentrates on the applications, advantages, and challenges associated with AI, with the intent of advancing interdisciplinary research and educational methodologies at the University of Tirana. Theoretical Framework: This research expands upon the principles of AI adoption within the educational sector, alongside an examination of ethical considerations and multidisciplinary collaboration. Theories pertaining to technological integration and adaptive learning systems serve as the foundational framework for comprehending the implications of AI in the realm of education. Method: The methodology employs bibliometric analysis to examine AI-related research trends utilizing data from SCOPUS and conducts a survey to assess students' familiarity with and perceptions of AI. Data collection was facilitated through bibliometric instruments and an online survey incorporating Likert-scale and open-ended questions. Results and Discussion: The results underscore an increased focus on artificial intelligence (AI) and blockchain within scholarly research, wherein students exhibit considerable engagement and interest in AI applications. Nevertheless, limitations in resources and ethical issues, including privacy and bias, persist as primary challenges. The discourse underscores the imperative for investment in infrastructure and the incorporation of ethical education. Research Implications: This research highlights the imperative for higher education institutions to integrate artificial intelligence tools, cultivate adaptive curricula, and address ethical considerations in order to adequately prepare students for a future shaped by AI. The implications of these findings also pertain to educational policy and the formulation of interdisciplinary research strategies. Originality/Value: The study contributes by delivering a comprehensive bibliometric analysis and gives insights into student engagement with artificial intelligence. Its significance resides in presenting actionable recommendations to enhance the integration of artificial intelligence in higher education.

The concept paper provides a detailed analysis of how strategic policy frameworks can facilitate the adoption and integration of artificial intelligence (AI) to drive economic and social development in Nigeria. This executive summary outlines the paper's key objectives, strategic frameworks, and anticipated outcomes, emphasizing the need for robust policies to harness the transformative power of AI. The primary objective of this paper is to identify and propose policy frameworks that can support the widespread adoption of AI across various sectors in Nigeria. It recognizes the potential of AI to revolutionize industries such as healthcare, agriculture, finance, and education, thereby significantly contributing to national development. The paper underscores the necessity for a structured approach to AI implementation, addressing the unique challenges and opportunities within the Nigerian context. Central to the paper is the exploration of policy frameworks that can facilitate AI adoption. It discusses the importance of establishing clear regulatory guidelines that ensure ethical AI use, protect data privacy, and promote transparency. The paper also highlights the need for policies that encourage investment in AI research and development, support startups and innovation hubs, and foster collaboration between the public and private sectors. The concept paper examines successful AI adoption models from other countries, drawing lessons that can be tailored to Nigeria's specific needs. It emphasizes the significance of creating a conducive environment for AI innovation, which includes investing in digital infrastructure, enhancing internet connectivity, and ensuring access to high-quality data. Moreover, it proposes the establishment of AI regulatory bodies to oversee the development and deployment of AI technologies, ensuring they align with national priorities and ethical standards. Addressing the practical challenges of AI adoption, the paper highlights issues such as the digital divide, lack of skilled workforce, and potential job displacement. It proposes strategies to overcome these challenges, including implementing educational reforms to incorporate AI and digital literacy into the curriculum, providing incentives for continuous professional development, and promoting AI awareness and literacy among the general population. The anticipated outcomes of implementing robust AI policy frameworks include improved efficiency and productivity across various sectors, enhanced service delivery, and the creation of new economic opportunities. These outcomes are expected to drive sustainable economic growth, improve the quality of life, and position Nigeria as a competitive player in the global AI landscape. The paper provides a comprehensive roadmap for integrating AI into the national development agenda. By establishing robust policies, investing in infrastructure, and fostering a culture of innovation, Nigeria can successfully leverage AI to achieve significant socio-economic progress. The paper calls for a collaborative effort from government, industry stakeholders, academia, and civil society to create an enabling environment for AI adoption and growth.

PurposeThis paper examines the integration of artificial intelligence (AI) within family businesses, focusing on how AI can enhance their competitiveness, resilience and sustainability. The study seeks to provide insights into AI’s application in family business contexts, addressing the unique strengths and challenges these businesses face.Design/methodology/approachA systematic literature review was conducted to synthesize existing research on the adoption and integration of AI in family businesses. The review involved a comprehensive analysis of relevant academic literature to identify key trends, opportunities, challenges and factors influencing AI adoption in family-owned enterprises.FindingsThe review highlights the significant potential of AI for family businesses, particularly in improving operations, decision-making and customer engagement. It identifies opportunities such as analysing customer data, enhancing brand building, streamlining operations and improving customer experiences through technologies like Generative AI, Machine Learning, AI Chatbots and NLP. However, challenges like resource constraints, inadequate infrastructure, low customization and AI knowledge gaps inhibit AI adoption in family firms. The study proposes an AI adoption roadmap tailored for family businesses and outlines future research directions based on emerging themes in AI use within these enterprises.Originality/valueThis paper addresses the underexplored area of AI integration in family businesses, contributing to the academic understanding of the intersection between AI and family-owned enterprises. The study offers a comprehensive synthesis of existing research, providing valuable insights and practical recommendations for enhancing the competitiveness and sustainability of family businesses through AI adoption.

The integration of artificial intelligence (AI) into the new product development (NPD) process has been shown to significantly enhance innovation success. Drawing on situated AI theory, this study investigates two core senior management behaviours—emphasis on AI adoption and reward systems for AI adoption – and their roles in catalysing AI-driven innovation and facilitating AI integration across the seven NPD stages. Based on empirical analysis of 558 AI-driven NPD projects, the findings reveal that both senior management’s emphasis on AI adoption and reward systems positively impact innovation performance. Notably, senior management’s emphasis on AI adoption, rather than reward systems, significantly improves innovation quality, innovation speed, and product innovativeness. The results further indicate that senior management’s emphasis on AI adoption facilitates AI integration across six of the seven NPD stages, with the exception of the product testing stage. In contrast, reward systems for AI adoption positively influence AI usage in four stages, primarily within the early (idea development, business analysis, product design) and late (operations management) phases of the NPD process. This research advances situated AI theory to elucidate the mechanisms through which senior management behaviours drive AI-driven innovation success and adoption, and guides manager in effectively leveraging AI’s potential within the NPD process.

Digital revolution and the resultant emergence of Industry 4.0 has driven the incorporation of Artificial intelligence (AI) in Industrial education to enhance skills development in the industry. However, there is a lack of adequate empirical evidence on the integration of Artificial intelligence in industrial education. To fill this gap, this study reviewed previous studies on the adoption of AI in Industrial education and examined the frequency of occurrence of variables obtained in the studies reviewed. Relevant literature was screened and reviewed to find empirical evidence to support findings. A systematic review of 14 studies provided insights into the current applications, benefit and challenges of AI integration in industrial education. The study found that the most cited applications of AI is Adaptive and personalised learning systems, which customise workers/learners’ information based on their interaction with learning content. Other applications are augmented simulators for real-time feedback, virtual mentors, and intelligent tutoring systems which replicate real-life interaction with professionals among others. Majority of the studies found increased engagement and improved learning outcomes and skills development as benefit of AI integration in Industrial education. Other benefits are promotion of early identification of learning challenges and timely intervention and feedback, improvement in administrative efficiency and support, personalisation of learning. Notable challenges were skills and capacity gaps, lack of infrastructure and AI resources, curriculum issues and difficulty in integrating AI into current curriculum, ethical and privacy concerns among others. Based on the findings of the study, it was recommended that the skill gap should be filled with training in AI applications and use, investment in AI infrastructural development should be explored, industry collaboration and partnership in the area of needs should be considered, AI marketing and literacy should be adopted in industries, all AI intervention should be a continuing and lifelong process to ensure sustainability.

This review delves into the profound impact of artificial intelligence (AI) integration on contemporary business paradigms. The paper meticulously explores diverse AI applications, including machine learning, natural language processing, and predictive analytics, illustrating how these technologies can revolutionize operational processes, augment decision-making capabilities, and foster unparalleled innovation within organizations. Drawing from case studies and industry examples across various sectors such as finance, healthcare, retail, and manufacturing, the study elucidates successful AI implementation strategies. It examines the importance of robust data governance frameworks to ensure quality and integrity, the acquisition of AI talent, and the imperative of fostering a culture of innovation and adaptability within organizations undergoing AI transformation. Furthermore, the paper addresses the nuanced challenges and risks inherent in AI adoption, spanning ethical considerations surrounding data privacy and bias mitigation, cybersecurity vulnerabilities, and the potential impact on the workforce. By providing a comprehensive overview of the opportunities and challenges associated with AI integration in business models, the study equips organizational leaders, policymakers, and stakeholders with invaluable insights to navigate the evolving landscape of AI-driven innovation. It underscores the significance of strategic foresight, cross-functional collaboration, and continuous learning in harnessing the full potential of AI technologies to drive sustainable growth and competitive advantage in the digital era.
 Keywords: AI, Business, Models, Strategies, Efficiency, Innovation.

IntroductionDigital health technologies including artificial intelligence (AI) have made tremendous progress in ophthalmology with abilities comparable to expert humans demonstrated in fields like diabetic retinopathy screening. Healthcare systems worldwide face...

This study explores Indian library and information science (LIS) professionals’ perspectives on the integration of artificial intelligence (AI) in academic libraries in India. It aims to evaluate their comprehension of AI, determine their perspectives, investigate AI utilization, assess advantages, identify influencing factors, and examine attitudes towards AI adoption. A quantitative research approach was employed, utilizing a structured questionnaire designed based on study objectives and reviewed by subject matter experts. Purposive sampling targeted individuals with relevant LIS knowledge. Data were collected through Google Forms from 259 respondents and analysed using descriptive and inferential statistics. Respondents generally exhibited positive perceptions towards AI integration in libraries. High mean scores were observed for statements such as “AI can bridge librarian performance gaps” and “AI does not make library staff lazy.” Librarians expressed willingness to learn about AI, interest in its ethical implications, and confidence in its potential to improve library services. The study highlights a cautious optimism towards AI adoption in Indian academic libraries, with recognition of its potential benefits tempered by concerns about employment and resource allocation. Librarians demonstrate proactive attitudes towards engaging with AI technology and understanding its implications for library services, indicating a readiness to embrace AI within the profession.

Artificial Intelligence (AI) is transforming the landscape of education, offering innovative solutions to enhance learning experiences. This review provides a comprehensive overview of how AI is revolutionizing education, focusing on its impact on learning outcomes, teaching methodologies, and the overall educational ecosystem. The adoption of AI in education has led to personalized learning experiences tailored to individual student needs. AI-powered adaptive learning systems analyze student performance data to create customized learning paths, ensuring that students receive content at their pace and level of understanding. This personalized approach improves student engagement and academic performance. AI is also reshaping teaching methodologies, providing educators with tools to streamline administrative tasks and enhance instructional strategies. AI-powered tools can automate grading, create interactive lessons, and provide real-time feedback to students. This allows teachers to focus more on facilitating learning and developing critical thinking skills in students. Furthermore, AI is revolutionizing the assessment process, moving beyond traditional exams to more dynamic and insightful evaluation methods. AI-powered assessment tools can analyze student responses in real-time, providing immediate feedback and insights into student comprehension and learning progress. The integration of AI in education also extends to administrative functions, such as student enrollment, scheduling, and resource allocation. AI-powered systems can optimize these processes, leading to more efficient and effective management of educational institutions. Despite the numerous benefits of AI in education, challenges remain, including concerns about data privacy, algorithmic bias, and the need for teacher training. Addressing these challenges will be crucial to maximizing the potential of AI in education and ensuring equitable access to quality education for all. In conclusion, AI is revolutionizing education by enhancing learning experiences, transforming teaching methodologies, and optimizing administrative processes. As AI continues to evolve, its impact on education is expected to grow, offering new opportunities to improve learning outcomes and prepare students for success in the digital age.
 Keywords: Revolutionizing, AI, Enhancing, Learning, Experiences.

Artificial intelligence in medical imaging: Utilization, challenges, and practitioner perceptions in Rwanda.

This study assessed the balance between the benefits and risks associated with artificial intelligence (AI) adoption in nursing practice across multiple healthcare centres, focusing on innovative potential and ethical considerations. AI integration into healthcare presents various ethical challenges, particularly for nurses. Thus, it is important to ensure that AI adoption optimises patient care without compromising ethical norms. This cross-sectional study assessed 246 nurses from three hospitals in Al-Kharj, Saudi Arabia, through stratified random sampling. Data were collected on 6 December 2024 in person using five validated surveys: the Healthcare Technology Adoption Survey, Ethical Issues in Technology Usage Survey, Nursing Practice Perception Survey, Technology Acceptance Model Survey, and Data Privacy and Security Assessment. Correlation and regression analyses examined the relationships between factors and provided insights into technological integration in nursing practice. Nurses reported a moderate level of AI use, noting its benefits for patient care and workflow efficiency. However, primary concerns include data privacy and the potential for job displacement. The perceived usefulness of AI and ethical awareness were predictors of fewer ethical concerns. This study emphasises balancing AI adoption in nursing by integrating ethics with technology for optimal patient care. Healthcare institutions must enhance their ethical training to help nurses address AI challenges. Policymakers should improve AI adoption regulations.