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Artificial Intelligence (AI) is transforming higher education, offering both pedagogical advantages and potential academic risks for teaching and learning. This systematic review consolidates existing research to examine whether AI enhances or hinders pedagogical practices in universities. Results show that AI boosts efficiency, supports personalization, and promotes inclusivity through adaptive learning systems, intelligent tutoring platforms, and automated assessment tools. The findings, however, also indicate risks such as academic integrity issues, algorithmic bias, unequal access, and the potential deskilling of educators. Furthermore, policy gaps exacerbate these risks, subjecting institutions to ethical dilemmas and inconsistent practices. The review concludes that responsible integration, supported by strong governance frameworks, drives the impact of AI in academia. Recommendations for policymakers include introducing clear institutional policies for the use of AI in higher education, promoting equitable access to digital infrastructure, and mandating transparency and accountability to ensure trust and fairness in high-stakes decision-making. For higher education institutions, establishing comprehensive guidelines for AI use, investing in faculty training, and redesigning assessments can help ensure inclusivity, preserving creativity, and emphasizing originality and problem-solving skills less easily replicated by AI.

The use of artificial intelligence (AI) in higher education entrepreneurship education (EE) is an important advancement that integrates innovative technology with teaching methods.  Even so, the research output in this new field is still disjointed and unclear. This study delineates key trends, prominent authors, thematic evolution, and collaboration patterns using a bibliometric analysis of 50 publications from the Web of Science database (2016-2025). Drawing on the output from Biblioshiny software, the results indicate a rapid growth in research output after 2022. This is largely attributed to generative AI developments.  Key areas of research have, also, shifted over time from a general focus on pedagogy to groupings that focus on technology, such as using AI in education, focusing on student consequences, and entrepreneurial intentions. The results show a leading contribution to research output from China. International partnerships, on the other hand, are decentralised. Also, the overall network of co-authors is still fragmented. The study’s conclusion indicates that the field is transforming into a synthesis of technological and human-centric frameworks showing an interdisciplinary convergence of education, psychology, and computer science. It also points out more research avenues, such as the need for more qualitative studies on implementation and a closer look at the ethical side of AI in education.

The growing use of AI in higher education has heightened interest in genuine e-assessment to improve assessment efficacy, inclusivity, and academic integrity. Nonetheless, the implementation of AI-enhanced assessment techniques in South African higher education is inconsistent, influenced by structural disparities, ethical dilemmas, and differing degrees of institutional preparedness. This study offers a qualitative systematic literature analysis that investigates the implementation issues and pedagogical ramifications of AI-enhanced genuine e-assessment at South African Higher Education Institutions (SAHEIs). The evaluation, structured using a PICo-informed framework and according to PRISMA principles, synthesised evidence from 49 peer-reviewed publications published from 2020 to 2024. A narrative theme synthesis was utilised to examine persistent patterns concerning implementation obstacles, ethical considerations, assessment change, inclusion, and governance. AI-driven assessment technologies have significant advantages, such as higher grading consistency, scalability, tailored feedback, and increased student involvement. Nonetheless, infrastructural constraints, ongoing digital disparities, inadequate faculty readiness, and the lack of cohesive institutional policies hinder these advantages. Ethical concerns, especially algorithmic bias, data privacy, and academic integrity, exacerbate the implementation of AI in varied and resource-limited settings. This analysis finds hybrid evaluation models that integrate AI automation with human judgement as the most pedagogically sound and ethically responsible method. The study indicates that AI-enhanced authentic e-assessment possesses transformative potential for SAHE, contingent upon context-sensitive policies, ongoing professional development, equitable infrastructure investment, and strong ethical governance frameworks.

Emerging Artificial Intelligence (AI) technologies are transforming the global education sector, with Artificial Intelligence playing a crucial role in enhancing teaching, learning, and administrative processes. In South Africa, AI-driven innovations have the potential to bridge educational gaps, improve student engagement, and personalize learning experiences. Adaptive learning platforms, automated grading systems, and intelligent tutoring applications are revolutionizing education by making it more efficient and accessible. However, despite these advancements, the adoption of AI in South African education faces significant challenges. Key challenges include data privacy concerns, ethical considerations, and a shortage of AI-skilled educators. These challenges create an uneven distribution of AI’s benefits, limiting its potential to improve learning outcomes on a national scale. Highlighting the need to explore the opportunities and barriers associated with the adoption of emerging AI technologies in South African education. Therefore, this study adopts a comprehensive literature review aiming to explore emerging AI technologies in the educational context in south Africa. To achieve this, the research will address the following questions: What impact have new technologies had on South Africa’s development? and in what ways can emerging technologies supporting South Africa's development initiatives. The findings indicate that AI tools have shown success in improving learning outcomes, increasing student engagement, and streamlining administrative tasks in well-resourced schools. However, their impact is uneven due to infrastructural and systemic challenges; also, the widespread adoption is hindered by a persistent digital divide, limited internet access, and a shortage of AI-trained educators, especially in rural areas. Additionally, ethical concerns such as data privacy, algorithmic bias, and the absence of clear regulatory frameworks pose significant barriers.

Artificial Intelligence (AI) integration has become increasingly prevalent in various industries, transforming business processes and creating new opportunities. As AI continues to shape the future of work, organisations such as Higher Education Institutions (HEIs) must scrutinize their readiness to develop a workforce competent in AI. AI competencies are fundamental to successfully enacting and integrating these technologies. As a result, several organisations have invested in workforce readiness, albeit these efforts are affected by several actors and determinants. Consequently, organisations must examine the workforce readiness for AI integration as a technology of the future. This paper aims to explore the integration of AI in organisations such as HEIs to ascertain workforce readiness. Addressing three pivotal research questions, it delves into the extent to which the workforce in HEIs is developed and competent in AI, the contributing factors shaping the integration of AI into HEIs’ workforce, and the overall readiness of HEIs in fostering workforce development within the realm of AI. Employing a systematic content analysis review, the study provides pragmatic findings, serving as a reference point to elevate workforce readiness and pinpoint critical determinants. The study identified factors contributing to AI competency development that serve as valuable insights for other institutions aiming to design and implement effective workforce development programs in AI. Also, the findings provide significant implications for institutions seeking to enhance their workforce's competency in AI.   Keywords: Artificial Intelligence, Higher Education Institutions, HEIs, Workforce, Readiness.

Our way of life has evolved due to the advent of AI. Artificial intelligence (AI) is already having an impact on many parts of the industry and will likely find additional uses in the future. This research study investigates the students' readiness to adopt AI. An exploratory research design undergirds this investigation. A questionnaire (based on a prior study) survey was used to gather data. The SPSS and AMOS Graphics are used to conduct EFA and CFA. 387 respondents were taken into consideration for the study. In this investigation, the KMO value is 0.839. Factor loading is larger than 0.45, with 22 items. This study extracts five components: educational content (EC), institutional facilities (IF), artificial intelligence awareness (AW), acceptance barriers (AB), and usage/application (AP). The Eigen values of those components are 5.056, 2.420, 1.640, 1.411, and 1.000, explaining 52.4% of the total variation. The CFA model fit findings show that CMIN/DF=2.360, GFI= 0.0.913, AGFI= 0.884, CFI= 0.880, RMSEA=0.05, SRMR=0.05, Hoelter's N returns value at 5% significant level = 195 and at 1% significant level = 210, Chi square= 372.870, df=158, p=0.000. Today, AI may boost tertiary students' productivity. Engaging pupils with technology in our country helps determine their AI interests. The study shows how educational content, institutional facilities, artificial intelligence knowledge, acceptance hurdles, and usage/application affect students' preparedness to adopt AI. This research will help higher education authorities to introduce IT infrastructure, teach students to understand AI technology, and inspire future academics to contribute to this era of intelligent robots.

This study presents a systematic literature review analysing the impact of artificial intelligence (AI) on higher education, focusing on its methods, results, and implications. By synthesising a diverse range of academic papers, the review explores how AI technologies influence educational standards and practices in higher education institutions. Findings reveal that AI has the potential to enhance the quality of higher education by diversifying teaching responsibilities, customising learning experiences, and employing intelligent, adaptive teaching strategies. These capabilities position AI as a transformative tool for improving educational delivery and outcomes. However, the study also highlights significant challenges associated with integrating AI into higher education. These challenges include delineating the appropriate scope of AI use, addressing inequalities in access to digital resources, and ensuring adequate training and support for educators and students. The review underscores the importance of understanding these complexities to guide the development of effective strategies and policies that optimise AI's potential while mitigating its limitations. The review offers critical insights into the dual role of AI in higher education, where it can either advance or hinder educational standards depending on how it is implemented. By examining the advantages, limitations, and broader consequences of AI-powered instructional tools, this study provides a comprehensive perspective on the intricate relationship between AI and educational quality. The findings aim to inform educators, policymakers, and stakeholders about the opportunities and challenges of adopting AI in higher education, contributing to the development of inclusive, innovative, and sustainable educational practices.

Artificial intelligence has become an integral part of higher education, significantly transforming the landscape of higher education. This study aims to identify, analyse and visualise peer-reviewed academic research output on artificial intelligence (AI) and graduate attributes in higher education. Data was gathered from the Scopus database over a decade (2014-2024), with search terms related to artificial intelligence, graduate attributes, and higher education. Following the PRISMA method guidelines, 106 articles were deemed necessary for review. Bibliometric methods, content and thematic analysis were used to identify main themes, and VoSviewer software was used to analyse the data. The findings revealed research productivity, citation overview, the main subjects, the territory of the leading researchers, thematic choices and future research opportunities and directions. Themes such as the impacts of AI on graduate attributes emerged, which may assist policymakers, educational institutions, teachers and students in their strategies and choices for adopting and using AI. The study recognised research trends, provided insights into the current state of AI and higher education research, and identified potential gaps in the literature on the research landscape of AI, graduate attributes, and higher education. The study can guide future researchers on emerging thematic opportunities.