article · Heliyon
A systematic review of 64 studies published between 2017 and 2023 examines how artificial intelligence influences student academic performance in open and distance learning settings. The analysis shows that machine learning and classical statistical techniques each represent 29.69 percent of the reviewed research, with machine learning notably used to forecast student achievement. Nonempirical approaches, such as theoretical reviews, account for 40.63 percent of the literature, while hybrid approaches appear in just 3.13 percent. The synthesis reveals that while artificial intelligence offers measurable performance advantages, existing scholarship lacks structured, process-based frameworks capable of predicting educational impacts across diverse regions and genders. To address this deficiency, a new framework is outlined to help guide educational institutions in evaluating artificial intelligence integration while supporting equitable access to distance education aligned with international development goals.
Open and distance learning relies increasingly on digital technologies, yet institutions need clear evidence regarding how automated systems affect learner success. Understanding current analytical methods helps education providers recognise where artificial intelligence improves results, highlighting the need for structured tools that support students fairly across varied regional settings and demographic groups.
The findings represent early-stage conceptual research rather than a deployable technology product. Educational technology developers and distance education providers could use the proposed framework as a basis for designing student achievement forecasting systems. However, real-world application remains distant, as the framework still requires formal design, refinement, and empirical testing across diverse student populations before integration into commercial learning platforms.
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The role of artificial intelligence (AI) in education has been extensively studied, focusing on its ability to enhance learning and teaching processes. However, the precise impact of AI adoption on academic performance in open and distance learning (ODL) remains largely unexplored. This systematic literature review critically evaluates AI's impact on academic performance within ODL environments. Drawing from a curated selection of 64 papers from an initial pool of 700, spanning from 2017 to 2023 and sourced from Scopus, Google Scholar, and Web of Science, this study delves into the multifaceted role of AI in enhancing learning outcomes. The meta-analysis reveals a diverse methodological landscape: machine learning methods, employed in 29.69 % of the studies, stand out for their ability to predict academic achievement, which is matched in prevalence by classical statistical methods. Although less common at 3.13 %, hybrid methods are a burgeoning area of research, while a significant 40.63 % of works prioritise nonempirical methods, focusing on theoretical analysis and literature reviews. This investigation highlights the critical factors driving AI adoption in education and its tangible benefits for student performance. It identifies a crucial literature gap: the absence of a process-based framework designed to forecast AI's educational impacts with greater precision, especially across gender and regional lines. By proposing this framework, this study contributes to the academic discourse on AI in education. It underscores the urgent need for structured methodologies to navigate the challenges and opportunities of AI integration. This framework, aligned with UNESCO's 2030 educational objectives, promises to bridge educational divides, ensuring equitable access to quality education across diverse demographics. The findings advocate for future research to design, refine, and test such a framework, paving the way for more inclusive and effective educational technologies in ODL settings.
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DOI: 10.1016/j.heliyon.2024.e40025
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