This article proposes a novel conceptual framework that integrates Artificial Intelligence (AI) with Cognitive Load Theory (CLT) and the Cognitive Theory of Multimedia Learning (CTML) to enhance Open Distance eLearning (ODeL) systems. By bridging the gap between traditional cognitive theories and cutting-edge AI technologies, this framework supports adaptive cognitive load management, AI-mediated schema creation, and human-AI collaborative learning. A refined critique of Twabu's (2023) PhD study, based on conceptual analysis of its theoretical scope and limitations, highlights the absence of AI integration. This article presents a unique contribution by synthesizing established cognitive theories with the dynamic potential of AI, a perspective underrepresented in current literature. Practical examples, policy recommendations, and ethical considerations are consolidated to offer a comprehensive path forward. The study proposes an expanded framework incorporating AI-enhanced cognitive load management, AI-mediated schema creation, and human-AI collaborative learning. The Literature review and theoretical framework emphasises AI's role in simplifying cognitive processes by dynamically adjusting content presentation based on learner needs and performance. This includes AI-mediated adjustments to manage cognitive load and enhance schema development through personalised feedback and tailored content delivery. Such integration promises to create a more effective learning environment by aligning multimedia content with individual learner profiles, thus addressing the challenge of cognitive overload and improving knowledge retention and understanding.

This study explores the development and implementation of a design thinking Artificial Intelligence (AI)-driven auto-marking/grading system for practical assessments and accurate feedback aimed at alleviating the workload of lecturers at an Online Distance eLearning (ODeL) institution in South Africa. The study adopts an iterative approach to designing and prototyping the system, ensuring alignment with the unique needs and challenges at an ODeL higher learning institution (HLI). The study outlines a Design thinking framework for developing the AI system, emphasizing empathy with user needs, clear problem definition, ideation, prototyping, testing, and iterative improvements. Integrating such a system promises to enhance operational efficiency, ensure fair and unbiased grading for assessments, and provide students with consistent, timely, personalized feedback. Tapping on theorists such as Michael Foucault and Joseph Schumpeter, this study contributes to the ongoing discourse on innovative solutions for educational challenges in South Africa by employing a design thinking framework and qualitative research methods. It provides insights for developing and implementing AI-driven auto-marking/grading systems in higher education settings. Cognizant of data privacy laws, the study will highlight the essential adherence to ethical guidelines in automated assessment processes and the successful implementation of AI-driven auto-marking/grading systems in ODeL. Additionally, this study aligns with several Sustainable Development Goals (SDGs), such as Good Health and Wellbeing (SDG 3), Quality Education (SDG 4), Decent Work and Economic Growth (SDG 8), Industry, Innovation, and Infrastructure (SDG 9). The study will have a follow-up article that will report on the data collected, and we will conduct another study where we seek the users’ feedback regarding the system.

This study explores the intersection of Joseph Schumpeter’s Innovation Theory and the integration of Artificial Intelligence (AI) in higher education, specifically focusing on disruptive and incremental innovation, institutional transformation, and strategic policy adaptation. AI can potentially reshape pedagogical models, administrative efficiency, and knowledge production, aligning with Schumpeter’s concept of creative destruction. This paper critically examines how universities navigate AI-driven transformations, balancing technological advancements with regulatory and ethical considerations. The study investigates how AI fosters interdisciplinary collaboration, influences academic productivity, and creates new economic models within the higher education sector. By positioning AI adoption within Schumpeterian economics, this research provides a theoretical foundation for institutional adaptation strategies, offering insights for educators, policymakers, and researchers in leveraging AI for sustainable academic innovation.

This study investigates how Artificial Intelligence (AI)-based Tutoring Systems can be used to provide secondary school students with personalized learning feedback and to enhance their academic performance. The two main empirical objectives were to assess individualized formative assessment in classroom settings and to examine teachers’ and students’ perceptions of AI adoption. A mixed-methods quasi-experimental design was employed, integrating quantitative pre-/post-tests, interviews, and focus groups across six schools (n = 600). This approach enabled triangulation between measurable learning outcomes and contextual perception data for robust validation. Quantitative data were analyzed using Python (t-tests, ANCOVA), while thematic coding in NVivo was applied to qualitative data. Expert review, pilot testing, and Cronbach’s α (>0.80) were used to validate the instruments and ensure reliability, including pre-/post-tests and engagement scales. Findings revealed that students who received AI-based interventions achieved significantly higher academic performance (Cohen’s d = 1.05) and engagement (d = 0.72) compared with control groups. Teachers with AI exposure reported greater preparedness (mean = 3.4) and fewer perceived barriers. The study provides empirical evidence on the pedagogical viability of AI tutoring in under-resourced contexts, contributing to self-regulated and socio-technical learning theories. It also recommends enhanced systemic teacher education, ethical leadership, and structural support to foster equitable adoption of AI in education. The findings carry strong implications for policy development and educational innovation in promoting data-driven, inclusive learning within Nigeria’s secondary education system

This paper explores the complex interplay between artificial intelligence (AI) and education through Michel Foucault's governmentality theory. It investigates how AI affects power dynamics, knowledge dissemination, surveillance, and individual agency in educational contexts. By applying Foucault's concepts of biopower, disciplinary power, and control, the paper uncovers the intricacies of AI in learning environments. It stresses the importance of ethical governance, critical reflection, and responsible decision-making in utilising AI for education. The analysis highlights the need to understand AI's influence on knowledge formation, educational hierarchies, and student experiences. It examines surveillance in AI-powered education, revealing control and regulation nuances. The paper emphasises addressing power imbalances, ensuring privacy, and promoting transparency in AI-driven education. It contributes to discussions on technology's ethical and practical implications in education, calling for strategies that prioritise student well-being, equity, and empowerment as technology evolves.

The study investigates the application of predictive analytics model in adaptive teaching within Open and Distance Learning (ODL) institutions. The aim of the study lies in addressing the ongoing challenges of high dropout rates and low student engagement, particularly in developing countries. The research gap is the underutilisation of predictive analytics to personalise interventions and enhance learning outcomes in ODL environments. The study employs mixed-method research design including machine learning algorithms with Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and XGBoost, in predicting students at risk of academic failure and providing personalised interventions. A dataset of 5,000 students from the National Open University of Nigeria was used to trained and test the model. Model validation metrics used includes: accuracy, precision, recall, F1-score, ROC-AUC, and PR-AUC. More so, (n=1050) participants took part in the experimental and control group including semi-interview, enabling real world application of predictive model. Key findings indicated that Random Forest had the highest ROC-AUC (98.38%), followed by XGBoost (97.76%). Nevertheless, Logistic Regression and SVM outperformed the others in accuracy (97.43%), precision (97.65%), recall (95.95%), and F1-score (96.79%). These results show that adaptive teaching, supported by predictive analytics, is associated with improved student engagement and contributes to reducing dropout rates. The challenges such as data quality, privacy, trust and algorithms bias should be addressed. The study suggest that predictive analytics is capable of transforming teaching methods in ODL institutions, improve personalised and effective learning. Future study should focus on model optimisation and integration with other educational technologies.