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Artificial Intelligence (AI) has increasingly garnered attention within the global education landscape due to its potential to transform the teaching and learning process in a comprehensive manner. AI is capable of offering more personalised teaching approaches by adapting content and delivery methods according to individual students' proficiency levels, interests, and needs. It can analyse student performance in real time and provide immediate, targeted feedback, thereby enhancing students’ motivation to remain focused during learning sessions.This systematic literature review aims to critically examine and analyse the influence of Artificial Intelligence (AI) on the teaching and learning process, as well as its impact on student motivation. Scholarly databases such as Google Scholar, the Educational Resource Information Centre (ERIC), and Semantic Scholar were utilised to access relevant articles published within the last five years (2020–2025). A total of 156 articles were identified during the initial screening phase, and 13 were selected for synthesis as they met the specific contextual criteria of the systematic literature review, conducted in accordance with the stages outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) model.Findings from the review indicate a notable increase in both intrinsic and extrinsic motivation among students engaged in learning sessions that incorporate AI elements, particularly through the use of digital applications such as ChatGPT, Chatbot, Moodle, Ask AI, Image AI, Padlet, and others. These findings further substantiate the assertion that AI significantly contributes to enhancing students’ motivation during the teaching and learning process.The scope of this study specifically focuses on primary-level students to examine the influence of AI in the classroom context, thereby demonstrating the capacity of Artificial Intelligence to serve as a paradigm shift in fostering student motivation and cultivating a more productive teaching and learning environment.
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Masdin et al. (2026) studied this question.
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