Leveraging Artificial Intelligence and Large Language Models for Enhanced Teaching and Learning: A Systematic Literature Review

Artificial Intelligence (AI) and Large Language Models (LLMs) significantly have changed educational paradigms, enabling a significant reinvention in how teaching and learning happens today. Through this systematic literature review, the paper aims to offer a deep understanding of how AI and LLMs are implemented in educational settings today, providing examples of where these tools were used for personalization (both targeting students’ academic performance and choosing content that aligns best with learner’s aspirations), accessibility enhancements as well as their potential applications related to resource management automation. The central themes probed include the application of AI to help deploy appropriate learning strategies according to individual learner profiles. LLMs for improved responsive and interactive education tools, and the potential for autonomous assessment machines with associated feedback mechanisms.The opportunities for the aid of AI and LLMs to improve teaching and learning are vast, but this review will also question some associated challenges such as ethical issues[1], data privacy concerns and risks of algorithmic bias[2]. It is concluded by suggesting future research and application directions for the ethical development of inclusive technologies, which are secure from a privacy point of view too. This is followed by a reflection on the strategic implications of such technologies for those in charge of education policy and development hoping ideas related to how AI-LLMs rejoice more efficacious learning experiences could be born out.