The intersection of AI and learning analytics: Enhancing institutional performance

Integrating Artificial Intelligence (AI) and Learning Analytics (LA) in educational settings signifies a significant shift in leveraging data to enhance institutional effectiveness. This paper investigates the merging of these technologies, highlighting their capacity to revolutionise educational practices, improve resource management, and better student outcomes. AI-powered learning analytics provide immediate insights into student performance, facilitating tailored learning experiences and prompt interventions. The paper addresses the challenges faced and suggests strategies to overcome these obstacles to ensure the ethical and fair use of AI and learning analytics in education. Underpinned by computational learning theory, which emphasises understanding the performance and resource needs of machine learning algorithms, this study focuses on a sample from a rural university in the Eastern Cape. Data were gathered from the experiences and views of 65 students through questionnaires. Within the framework of a positivist paradigm, it was found that the introduction of AI has fostered the development of robust evaluation and assessment techniques, leading to increased faculty engagement. The research indicates that factors such as perceived risk, performance expectations, and awareness significantly influence work engagement and the adoption of AI in higher education, mediated by attitudes and behaviours. It is recommended that university administration establish clear ethical guidelines and policies governing AI and learning analytics and provide training and professional development for faculty to enhance their data literacy skills.