This study examines the application of agentic artificial intelligence (AI) frameworks within small, medium, and micro-enterprises (SMMEs), highlighting how interconnected autonomous agents improve operational efficiency and adaptability. Using the PRISMA 2020 framework, this study systematically identified, screened, and analyzed 66 studies, including peer-reviewed and credible gray literature, published between 2019 and 2024, to assess agentic AI frameworks in SMMEs. Recognizing the constraints faced by SMMEs, such as limited scalability, high operational demands, and restricted access to advanced technologies, the review synthesizes existing research to highlight the characteristics, implementations, and impacts of agentic AI in task automation, decision-making, and ecosystem-wide collaboration. The results demonstrate the potential of agentic AI to address technological, ethical, and infrastructure barriers while promoting innovation, scalability, and competitiveness. This review contributes to the understanding of agentic AI frameworks by offering practical insights and setting the groundwork for further research into their applications in SMMEs’ dynamic and resource-constrained economic environments.

Background: In higher education, artificial intelligence (AI) is the application of AI technology to support instruction, learning or decision-making in educational environments. Reducing burdens for educators and students while improving learning outcomes is one of the potential benefits. Nonetheless, there are several paradoxical problems with ethical considerations for AI-driven environments. Objectives: This study explores ethical concerns for an AI-driven environment in higher education to provide a feasible strategy to address ethical concerns. Method: A systematic literature review methodology was applied. The IEEE Xplore Digital Library database was used, with keywords such as ‘artificial intelligence OR AI’ AND ‘education’ AND/OR ‘ethics’. Only conference and journal papers published between the years 2022 and 2024 were filtered. Thirteen papers were identified as augmented and feasible after full-text review. Results: The findings revealed how AI in higher education provides insightful data about ethical concerns in the AI-driven environment. Utilising AI in education raises serious ethical problems about equity, privacy, autonomy and the importance of human contact in education. Conclusion: Artificial intelligence-driven environments for higher education must be aligned appropriately with educational contexts, protecting all learners’ fundamental rights and well-being. Institutions should use AI’s ability to enhance learning opportunities by tackling the above-mentioned ethical concerns to safeguard all learners’ fundamental rights. Contribution: An AI ethical considerations strategy that guides and prioritises learners’ well-being and rights while responsibly using AI’s potential is outlined. Significantly, a methodological contribution to AI in education, particularly by informing policymakers on how to prioritise learners’ well-being and rights effectively, is formulated.

Background: Artificial intelligence (AI) technology is transforming education through personalised learning and creates dynamic, adaptive learning environments that cater to each student’s unique strengths and challenges. Developed countries have largely integrated AI technologies into their learning institutions, while the discipline is in its infancy in developing countries such as South Africa (SA). Objectives: This study aims to contextualise and recommend the strategy that institutions of higher learning in SA can adopt to integrate AI into their institutions. Method: A systematic literature review (SLR) method was followed. Publications published between 2018 and 2024 in the Multidisciplinary Digital Publishing Institute (MDPI) and Taylor & Francis Online databases using the Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA). Following an initial search, 114 documents were retrieved, and, using inclusive criteria, 29 papers were chosen for analysis. The databases were selected because of their unique benefits in terms of accessibility, material breadth, and researcher-specific functions, unlike other sources. Results: Results show that to integrate AI, the following should be considered: planning, collaborations, training, and ethical standards to guarantee responsible use and productivity. This will enhance teaching and learning, well preparing students for a future whereby AI is widely used in the workplace. Conclusion: To integrate AI into learning institutions, a tailored approach needs to ensure that the AI technology improves teaching, enhances administrative procedures, and adheres to the institution’s rules and regulations. Contribution: This article forms a theoretical and methodological contribution to advancing knowledge that may inform policy and practice makers.

This study examines the application of agentic artificial intelligence (AI) frameworks within small, medium, and micro enterprises (SMMEs), emphasising the transformative potential of interconnected autonomous agents in enhancing operational efficiency and adaptability. Using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyzes) framework, this study rigorously identified, screened, and analyzed 63 peer-reviewed studies published between 2019 and 2024. Recognizing the constraints faced by SMMEs, such as limited scalability, high operational demands, and restricted access to advanced technologies, the review synthesizes existing research to highlight the characteristics, implementations, and impacts of agentic AI in task automation, decision-making, and ecosystem-wide collaboration. The findings underscore the role of agentic AI in fostering innovation, scalability, and competitiveness while addressing barriers such as technological, ethical, and infrastructural challenges. By advancing the understanding of agentic AI frameworks, this research provides actionable insights and sets a foundation for future explorations into their implications within resource-constrained and dynamic economic landscapes.

The global trend towards Industry 4.0 has raised demand to incorporate technology in the manufacturing industry. This new paradigm requires cyber-physical systems, the Internet of Things (IoT), and Artificial Intelligence (AI) to enhance the efficiency and competitiveness of traditional industrial methods. Industry 4.0 incorporates Smart Artificial Intelligence (SAI) to enhance efficiency, digitalise production, and automate the intelligent processing of commodities. Despite the benefits SAI technology carries, many South African industries struggle to realise its full potential due to resource and financial constraints. This chapter discusses the challenges of SAI adoption and how the manufacturing sector leverages SAI to enhance its productivity and competitiveness. A systematic literature review was conducted. ScienceDirect publications from 2022-2025 period were reviewed. Only the review and research papers, focusing on the SA manufacturing industry, were considered. The findings reveal how the use of SAI in South Africa (SA) is hindered, thereby constraining innovation and productivity. SAI promotes manufacturing in the country; inadequate infrastructure, a lack of funding are the biggest obstacles to implementing SAI in SA. A contribution about how SAI technology is leveraged in the SA manufacturing industry was established, advancing knowledge that may inform industry leaders.

Background: Online Admission Systems (OAS) are Internet-based platforms that are intended to improve efficiency, fairness and transparency of admissions processes and enable easy access to educational institutions. Nonetheless, several challenges, such as technical errors and user perceptions, remain issues in the education sector. Objectives: This scoping review explored the broad existing literature on OAS in the education sector and identified factors influencing their successes. Method: The scoping review was guided by the Joanna Briggs Institute methodology and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension of Scoping Review for reporting. Records were sourced from Google Scholar, ERIC, EBSCOhost, Science Direct, JSTOR, Elicit and Perplexity AI. A total of 18 records were analysed. Results: This scoping review explored the existing literature on OAS in the education sector, identified the investigated factors influencing them and determined the Information Systems theories that have been applied in this domain. Conclusion: There is a need for comprehensive evaluation approaches that account for identified factors influencing OAS in the Information Systems discipline in basic education. Future research should examine system stability, sustainability and social justice, while exploring the application of user-centred design principles to improve usability, accessibility and overall user experience. Contribution: A theoretical contribution to the need for OAS theories and models in the field of Information Systems, and a practical contribution that will assist decision makers in the education sector in assessing whether OAS achieved their goals.