Exploring the effects of Artificial Intelligence on Student Learning and Instruction in Educational Training Providers (ETPS) in Botswana

The integration of Artificial Intelligence (AI) in South African universities presents both opportunities and challenges, particularly within the context of curriculum transformation and decolonisation. This paper critically examines the relevance of AI in relation to the #FeesMustFall movement, which advocates for equitable access to education, and explores how these themes intersect with decolonisation efforts in South Africa. Although AI technologies promise advantages like tailored learning experiences, improved administrative processes, and enhanced research capabilities, they also present issues related to epistemic bias, digital disparities, and the reinforcement of Western-centric knowledge systems. Grounded in empirical research, this study investigates whether AI serves as an aid or an obstacle in South African higher education, with a specific focus on Historically White Universities (HWUs) and Historically Black Universities (HBUs). Using the Diffusion of Innovation (DOI) theory as a framework, the research explores disparities in AI adoption across institutions, analysing infrastructural constraints, policy gaps, and the broader implications of AI for knowledge production. The findings reveal that while HWUs have made significant strides in AI integration due to better funding and international collaborations, HBUs continue to face systemic barriers that hinder equitable access to AI-driven learning tools. Moreover, AI's reliance on Western datasets and epistemologies risks perpetuating digital colonialism, complicating ongoing efforts to decolonise the curriculum. This paper underlines the urgent need for Afrocentric AI models that align with local contexts and values, inclusive policy frameworks that address the needs highlighted by #FeesMustFall, and targeted investments in digital infrastructure. By doing so, it aims to ensure that AI contributes meaningfully to higher education curriculum transformation and decolonisation in South Africa.

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.

The transformative potential of Artificial Intelligence (AI) in higher education is widely acknowledged, yet its adoption remains limited in emerging economies due to infrastructural, pedagogical and institutional constraints. This study investigates the determinants of AI adoption and its influence on educational effectiveness within the context of Pakistani higher education institutions. Grounded in the Technology Acceptance Model (TAM), Task-technology Fit (TTF) and Institutional Theory, the research offers an integrated framework to understand how individual perceptions, task alignment and institutional support collectively shape AI integration. A cross-sectional survey was conducted among 750 academic stakeholders, including students, faculty and administrators across diverse public and private universities. Using Partial Least Squares Structural Equation Modelling (PLS-SEM), the study tested a moderated mediation model involving perceived usefulness, perceived ease of use, TTF, digital literacy, institutional support and AI adoption. Results show that AI adoption significantly mediates the relationship between key antecedents and educational effectiveness. Notably, digital literacy and institutional support enhance this relationship, serving as critical enablers. Theoretically, this study extends existing technology adoption models by embedding contextual institutional factors, offering a nuanced understanding relevant to resource-constrained settings. Practically, it underscores the need for targeted investments in digital infrastructure, literacy programs and supportive governance to maximise AI’s pedagogical value. By addressing a critical research gap in an underrepresented context, this study provides actionable insights for policymakers, university leaders and educators aiming to foster inclusive and effective AI integration in higher education.

The healthcare landscape in Africa is undergoing a transformative shift driven by the integration of Artificial Intelligence (AI), telemedicine, and advanced computing. This Research Topic, an initiative linked to the Digital Health Africa 2024 Conference (R. Benecke et al, 2024), brings together a collection of studies that examine how these technologies address complex healthcare challenges exacerbated by underfunding and resource limitations. While the potential for innovation is immense, the contributions highlight a recurring tension: the gap between policy aspirations and the current reality of infrastructure.One of the most immediate impacts of AI is its ability to reduce the manual burden of labour-intensive tasks. Abogunrin et al. provide a pragmatic review quantifying the workload efficiencies of AI in evidence synthesis, especially for systematic literature reviews. Their findings indicate that AI can cut overall processing time by more than 50% and reduce abstract screening time by a factor of 5 to 6, thereby accelerating the pace at which evidence-based medicine informs policymaking.Beyond research, AI is reshaping professional training.Almaghaslah investigates the use of ChatGPT-4 in pharmacy education to combat the global phenomenon of grade inflation. This study demonstrates that AI-generated multiple-choice questions yield higher reliability (KR-20=0.83) than those generated by human (KR-20=0.78). By generating more moderate and difficult questions, AI helps ensure that student evaluations accurately reflect true mastery rather than superficial learning.Implementation in resource-limited settings remains the final frontier for digital health. Two studies in this collection offer critical insights into the operationalization of these tools in Sub-Saharan Africa. Nkangu et al. describe the integration of the World Health Organization's (WHO) Digital Adaptation Kit (DAK) for antenatal care into the BornFyne-Prenatal Management System in Cameroon. By utilizing "machine-readable" guidelines, the project ensures that digital health interventions are not only innovative but also standardized and interoperable with preexisting systems like District Health Information Software 2 (DHIS2).However, technological readiness is often stymied by physical realities. Stenhouse et al. analysed the development of a mobile health (mHealth) system for symptomatic management of COVID-19 in rural Ethiopia. They identified infrastructure and digital access, specifically unreliable electricity and internet outages, as primary barriers to sustainable implementation. The also note clinicians' concerns that remote monitoring could miss critical findings detectable only through physical examination.The logistical hurdles of vaccine distribution in Africa, characterized by "last mile" visibility gaps and cold chain failures, present another opportunity for AI intervention. Musa et al. discuss the leverage of AI for predictive analytics to optimize vaccine stock levels and delivery routes. They highlight successful implementations of autonomous drone delivery services, such as Zipline, in Rwanda and Ghana, which have significantly reduced delivery times and improved vaccine availability even in remote locations.As these technologies proliferate, the need for robust regulatory oversight becomes urgent. While global bodies like the Food and Drug Administration (FDA), European Medicines Agency (EMA), and the National Institute for Health and Care Excellence (NICE) are establishing guiding principles for AI, local implementation remains fragmented. In this collection, Abogunrin et al. highlight how the NICE AI position statement provides a foundation for evidence generation, while Botes examines the specific regulatory challenges in South Africa, focusing on mental health applications. The study warns of inadequacies in the regulation regarding the commodification of sensitive neural data and the ethical risks posed by AI-driven tools that lack clinical validation. Without explicit guidelines for third-party data sharing and AI transparency, users remain vulnerable to privacy breaches and psychological harm.Discussion: The Human-in-the-Loop and Future Directions A common thread across all submissions is the indispensable role of human oversight. Whether it is the meticulous review required to ensure the accuracy of AI-generated exam questions or the structured protocols needed for automated evidence synthesis, technology cannot yet replace the nuanced judgment of a human professional. Furthermore, the digital gap remains a significant obstacle. For digital health to be equitable, interventions must confront low digital and health literacy levels prevalent in many rural communities. Successful implementation therefore requires a bottom-up approach that involves healthcare providers and patients from the planning stage onwards, ensuring interventions are culturally relevant and contextually appropriate.The articles in this Research Topic collectively demonstrate that Digital Health innovations in Africa offer a promising path toward universal health coverage and more resilient supply chains, bridging the gap between advanced technological capabilities and the continent's physical and systemic realities. However, technology alone is not a panacea. Realizing their full potential requires strategic frameworks that balance innovation with rigorous data protection and substantial government investment in digital infrastructure. Only by grounding Africa's transformative healthcare shift on reliability, ethics, and sustainability can we secure lasting impact.

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.

Purpose: This study examined the integration of Artificial Intelligence (AI) tools in science education in selected primary schools in Bududa District, Uganda. It focused on implications for teacher education and professional development within resource-limited contexts. Guided by constructivist and cognitive load theories, the research investigated how AI affects learner engagement, instructional practices, and classroom dynamics. Methodology: A mixed-methods approach was used, incorporating pupil interviews, teacher questionnaires, and classroom observations. This provided a comprehensive understanding of how AI tools were being adopted and experienced in real classroom settings. Findings: Artificial Intelligence (AI) tools such as educational simulations and interactive quizzes improved learner motivation, collaboration, and conceptual understanding. However, implementation was inconsistent due to inadequate digital infrastructure and limited teacher training. While many teachers expressed a willingness to adopt AI, they lacked the necessary digital skills and support systems to use these tools effectively. Unique Contribution to Theory, Practice and Policy: Theoretically, the study links AI-enhanced learning to constructivist and cognitive load principles in low-resource environments. In practice, it highlights the need for teacher education programs that develop AI literacy, pedagogical adaptability, and context-sensitive strategies. On a policy level, the study recommends revising teacher training curricula to include AI integration, alongside increased investment in digital infrastructure and professional development. These measures are critical for advancing equitable and effective science education in rural Ugandan schools.

The integration of Artificial Intelligence (AI) in education has transformed teaching methodologies, personalized learning experiences, and improved student engagement. However, this advancement has also exacerbated the digital divide, creating disparities in access to AI-powered educational tools. This review explores the challenges associated with the digital divide in AI-driven education, including technological infrastructure gaps, socio-economic barriers, digital literacy deficiencies, and policy constraints. It further examines the disproportionate impact on marginalized communities, rural populations, and underfunded educational institutions, limiting their ability to benefit from AI-enhanced learning. To address these inequities, various solutions are proposed, including increased investment in digital infrastructure, the development of affordable AI-based learning tools, and the implementation of inclusive policies that prioritize equitable access to technology. The role of public-private partnerships, government interventions, and AI-driven adaptive learning models in bridging this gap is also analyzed. Additionally, fostering digital literacy through teacher training programs and community initiatives is highlighted as a critical strategy to ensure inclusive adoption of AI in education. This paper emphasizes that while AI has the potential to revolutionize learning experiences, its benefits must be universally accessible. The study underscores the need for a multi-stakeholder approach involving policymakers, educators, technologists, and social organizations to create a more equitable AI-powered educational ecosystem. Future research directions are recommended to explore innovative frameworks that mitigate the digital divide, ensuring AI-driven education is inclusive and accessible to all learners, regardless of their socio-economic backgrounds.

In recent years, the rapid advancement of Artificial Intelligence (AI) has had a profound and transformative impact across a wide range of sectors, including education and scientific research. As the primary environment for the generation, exchange, and preservation of knowledge, academia finds itself at the forefront of this technological shift. AI presents a dual reality for academic institutions: it brings forward unprecedented opportunities for innovation and efficiency, while simultaneously introducing complex ethical, pedagogical, and operational challenges. This article aims to examine the multifaceted influence of AI on higher education, particularly focusing on the evolving role of educators. While numerous studies have explored AI's applications in learning management systems, automated grading, and research data analysis, this paper places a particular emphasis on the human dimension namely, how academic staff are responding to the integration of AI tools into their teaching and research practices. Through an analytical overview, the paper identifies both the benefits and risks associated with AI implementation in academia. On the one hand, AI offers the potential to enhance personalized learning, automate repetitive administrative tasks, and improve access to educational resources. These advancements could significantly increase productivity and support more inclusive educational practices. On the other hand, the growing reliance on AI raises serious concerns related to academic integrity, data privacy, algorithmic bias, and the potential deskilling of educators. The article highlights the urgent need for clear institutional strategies that include professional development for educators, ethical guidelines for AI use, and investment in digital infrastructure. Without these measures, the integration of AI could lead to fragmentation and inequality within the academic system. Educators must not only adapt to technological innovations but also actively shape the discourse around the responsible and meaningful use of AI in education. In conclusion, while AI undoubtedly holds transformative potential for academia, its successful and ethical implementation depends on a balanced approach one that values both innovation and the enduring principles of academic freedom, critical thinking, and human-centered learning.

This study explores the integration of artificial intelligence (AI) in adaptive learning within higher education, focusing on its effectiveness, challenges, and strategic implementation. The objective is to assess how AI-driven technologies—such as machine learning, natural language processing, and learning analytics—support personalized education and improve student outcomes. The methodology involved a narrative review of peer-reviewed literature sourced from Scopus, PubMed, and Google Scholar, using a targeted Boolean search strategy and strict inclusion criteria. Studies were selected based on their empirical focus, educational context, and relevance to AI-enabled adaptive learning. The findings reveal that AI technologies significantly enhance student engagement and academic performance by tailoring content delivery, monitoring progress, and enabling real-time feedback. However, institutional readiness varies greatly between developed and developing countries. While well-resourced institutions have successfully embedded AI into their pedagogical systems, many universities in Southeast Asia struggle with limited infrastructure, faculty preparedness, and policy support. Systemic barriers—such as lack of funding, inadequate infrastructure, and insufficient training—emerge as critical challenges. To overcome these barriers, the study suggests coordinated policy efforts, investment in digital infrastructure, faculty training, and inclusive design approaches. Future research should address the long-term impacts of AI in education and ethical considerations related to data use. These efforts are essential to ensure equitable, effective, and sustainable AI adoption that can transform higher education globally.

The integration of Artificial Intelligence (AI) into renewable energy systems represents a transformative step in enhancing the efficiency, reliability, and sustainability of clean energy technologies. This review explores the roles and applications of AI techniques—including Machine Learning (ML), Deep Learning (DL), Reinforcement Learning (RL), and ensemble models like XGBoost—in predictive maintenance and energy optimization. Through a comprehensive analysis of recent studies, the review highlights how AI improves system performance by enabling early fault detection, optimizing energy distribution, and managing storage efficiently. Predictive maintenance driven by AI can reduce unplanned downtime by up to 35% and enhance energy output by approximately 8.5%. In energy optimization, AI models forecast demand and control load distribution, significantly contributing to smart grid development. However, several challenges remain, particularly in Indonesia, including limited high-quality data, high computational demands, system interoperability issues, and a lack of regulatory and human resource readiness, reducing unplanned downtime by up to 35% and increasing energy output by approximately 8.5%, as reported in previous studies. The review concludes that successful implementation requires strategic investment in digital infrastructure, inter-sectoral collaboration, and pilot projects to ensure sustainable AI adoption in Indonesia's renewable energy sector.

The integration of Artificial Intelligence (AI) into healthcare holds transformative potential for developing countries, yet its application remains underexplored in many such contexts. This study addresses the research gap by examining the current state, challenges, and ethical considerations of AI adoption in Tanzania’s healthcare system. Combining a systematic literature review with semi-structured interviews involving 32 key stakeholders, including healthcare practitioners, policymakers, and technologists, the study provides a comprehensive, context-sensitive analysis. The findings reveal that AI is increasingly applied to diagnostics, predictive analytics, mental health support, and health education. However, widespread adoption is constrained by infrastructural deficits, organisational readiness gaps, limited workforce capacity, and low digital literacy, particularly in rural areas. Ethical concerns such as data privacy, algorithmic bias, and lack of transparency also emerged as critical barriers. Policy fragmentation and the absence of AI-specific implementation guidance further complicate integration efforts. Despite these challenges, stakeholders expressed optimism about AI’s potential to improve diagnostic accuracy, reduce provider burden, and expand access, especially through mobile-based tools. This study’s unique contribution lies in its integration of the Technology-Organisation-Environment (TOE) and Ethical AI frameworks to assess AI adoption holistically, linking global evidence to local realities. The study recommends decentralised policy development, inclusive design processes, and sustained investment in digital infrastructure and training. These actions are essential for fostering ethical, inclusive, and sustainable AI-driven healthcare transformation in Tanzania and similar low-resource settings.

The integration of Artificial Intelligence (AI) into education offers transformative possibilities for enhancing design and communication pedagogies, particularly in resource-constrained settings. This study examines the adoption, impact, and barriers to AI use in Ghanaian secondary and technical education, with a focus on design-related subjects. Using a quantitative cross-sectional design, data were collected through structured questionnaires administered to 108 teachers. Findings highlight a pronounced disparity between teachers’ recognition of AI’s pedagogical potential and its actual implementation. Respondents widely acknowledged AI’s capacity to support student-centered learning, enrich teaching strategies, and improve outcomes (all p < 0.001). However, adoption remains limited. Reported barriers include inadequate ICT infrastructure (62%), lack of formal training (only 23.1% had received any), ethical concerns, and poor alignment with existing curricula. Logistic regression further identified postgraduate qualification, AI-specific training, adequate ICT resources, and engagement with technical subjects as significant predictors of adoption. The results underscore a systemic digital divide that continues to constrain the pedagogical use of AI in Ghana. While its value for fostering innovation in design and communication education is evident, sustained progress requires coordinated policy and institutional support. A multi-pronged strategy is essential prioritizing investment in digital infrastructure, comprehensive and discipline-specific teacher training, ethical frameworks, and curricular reforms tailored to local contexts. Addressing these gaps will enable more equitable and effective AI integration, advancing both educational quality and technological capacity within resource-limited environments.

Background and Aim: The integration of artificial intelligence (AI) in education has significantly transformed language learning, particularly in blended learning environments. AI-powered platforms such as FLIT provide personalized learning experiences, real-time feedback, and data-driven insights, making them valuable tools for Business English instruction. However, factors such as gender and geographical background may influence student performance in these AI-driven environments. This study examines the impact of gender and geographical background on Business English proficiency (listening, speaking, reading, and writing) within an FLIT-based blended learning framework, and it explores the potential interaction between these factors. Materials and Methods: Using a quasi-experimental design with pre-test and post-test assessments, 240 English major students from a science and technology university in northeastern China participated in ten weeks of FLIT-based instruction. Pre-tests and post-tests were administered using the Cambridge Business English Certificates (BEC) exam. Data were analyzed using ANCOVA to assess the main effects of gender and geographical background, and MANCOVA to investigate interaction effects. Results: Results indicate that geographical background significantly influences Business English proficiency across all four language skills. For example, ANCOVA revealed that geographical background had a significant effect on reading performance (F (3,232) = 17.31, p < 0.001, partial η² ≈ 0.19), with urban students scoring approximately 10% higher than their rural counterparts. In contrast, gender did not exhibit a statistically significant effect (all p > 0.05), and no interaction effect between gender and geographical background was observed. Conclusion: The findings underscore the need to address regional disparities in AI-powered language learning environments. Practically, these results suggest that targeted interventions—such as enhanced digital literacy programs and increased allocation of educational resources to rural areas—are essential for bridging the performance gap. Policy-wise, investments in digital infrastructure and tailored educational technology are recommended to ensure equitable learning opportunities across diverse regions. Future research should investigate the long-term impacts of AI-powered instruction and consider additional learner variables such as motivation and learning styles.

Despite growing interest in artificial intelligence (AI) in South African education, limited research has examined how rural educators perceive and navigate AI integration. This study explores educators' perspectives, adaptive strategies, and lived realities in under-resourced rural schools. Eight educators from Eastern Cape, Limpopo, Mpumalanga, and North-West provinces were purposefully selected. Data were collected through written responses and semi-structured online interviews, and were analyzed thematically. Ethical safeguards included informed consent, pseudonyms, and confidentiality. Findings reveal that AI integration is hindered by inadequate digital infrastructure, unreliable connectivity, and limited access to devices. Educators also face insufficient digital literacy and a lack of professional development, leaving them underprepared for AI-supported teaching. Weak institutional support and gaps between policy and practice further constrain adoption. Moreover, AI tools often remain linguistically and culturally misaligned, reducing learner engagement. Equity and ethical concerns—access, data privacy, and algorithmic bias—raise the risk of exacerbating educational inequalities rather than reducing them. This study underscores the need for targeted investment in digital infrastructure, contextualized teacher training, and inclusive AI design that reflects local languages and cultures. The findings extend beyond South Africa, contributing to global debates on equitable AI adoption in education across the South.

As the utilization of artificial intelligence (AI) and generative AI (GenAI) is expanding in the educational field, it presents profound implications for STEM disciplines, particularly chemistry and chemical engineering. This Perspective explores the integration of AI in education, drawing from UNESCO guidelines and global recommendations from 2022 to 2025, underscoring the imperative of a human-centered pedagogical approach. The analysis highlights the transformative potential of AI in educational practices, focusing on enhanced personalized learning, teacher training, and academic management, all of which are seen as possibly contributing to advancing sustainable development goal 4 (SDG 4, quality education). It also discusses the risk of epistemic drift, where reliance on opaque algorithms may detach scientific inquiry from a causal understanding. We show examples of prompt engineering techniques for scientific illustration generation in the fields of chemistry and physical chemistry, and discuss its advantages, and limitations. Furthermore, the rapid development of AI technologies has outpaced the policy debates in most academic institutions, creating a significant policy gap in higher education. This is coupled with global disparity, where most academic institutions in high-income countries have implemented AI-driven tools by 2025, while access in low-income regions remains constrained. We argue that to harness the potential benefits of AI, the chemical education community must move beyond technical adoption to foster critical AI chemical literacy. This involves targeted investments in digital infrastructure and the development of assessments that prioritize human reasoning over algorithmic output. We conclude that the responsible integration of AI requires a shift from a content delivery model to a knowledge creation model guided by the high-level ethical frameworks proposed by UNESCO.