Generative Artificial Intelligence and the Future of Education: A Systematic Review of Trends, Trustworthiness and Technological Evaluation

This systematic review aims to investigate the current landscape of AI in Education (AIEd) through a comprehensive analysis of empirical studies related to AIEd published since 2022 and indexed in the Web of Science. Following the PRISMA 2020 guidelines and using thematic analysis, this study reviews 103 studies selected by one primary researcher based on the defined research questions, with four researchers independently verifying the selection. These research questions focus on the prevalent AIEd technologies, their reliability, evaluation frameworks, and the potential impact of AI in educational contexts, aiming to serve as a roadmap for researchers, developers, educators, and policymakers navigating the rapidly transforming landscape of AIEd. The findings highlight the prominence of classical machine learning models in published studies, while recent Generative AI (GenAI) models, such as Large Language Models (LLMs), have not been comprehensively experimented with, despite their potential. Findings show that studies utilising LLMs typically relied on more general-purpose systems, such as ChatGPT. Recognising these trends and analysing surveys on stakeholder intentions (educators and learners), the review identifies existing gaps in AIEd research, including the absence of adequate evaluation mechanisms, the lack of specialised LLM-based systems, and notable concerns about transparency, trustworthiness, and ethical implications. Based on the identified gaps, predominantly from a technical perspective, the study recommends research on specialised GenAI-based systems, the integration of AI tools into current pedagogical approaches, comprehensive ethics-aware evaluation frameworks, and addressing issues regarding the trustworthiness of AIEd systems.