A meta systematic review of artificial intelligence in higher education: a call for increased ethics, collaboration, and rigour

Artificial Intelligence (AI) has existed since the 1960s and its adoption in education, particularly with the early introduction of intelligent tutoring systems, has become a substantive research domain (AIEd). Despite the growing realisation of the potential for AI within education, influenced by educational evidence-based policy, including education departments and international organisations (e.g., OECD, 2021), it has arguably only now transitioned from work in labs to active practice in classrooms, and broken through the veil of public discourse. The introduction of ChatGPTFootnote 1 and DALL-E,Footnote 2 for example, has both captured our imagination and shocked in equal measure (Bozkurt et al., 2023), requiring schools, universities, and organisations to respond to generative AI’s growing capabilities, with increasing numbers of publicly available AI chatbots on the horizon (e.g., Google’s BardFootnote 3 and LLaMAFootnote 4). The uptake of these tools has given rise to a debate in education about readiness, ethics, trust, impact and value add of AI, as well as the need for governance, regulation, research and training to cope with the speed and scale at which AI is transforming teaching and learning. Globally, governments are putting measures in place to respond to this unfolding phenomenon, for example in Europe they introduced the EU AI Act, which they claim is the world’s first comprehensive AI law.Footnote 5 Australia established a taskforce to outline a framework for generative artificial intelligence in schoolsFootnote 6 and in the United States, the Department of Education calls for an AI bill of rights to develop a comprehensive approach towards the adoption of AI in education.Footnote 7 Needless to say, it is important that these actions are based on a solid foundation of research and conceptual grounding. Even though there is a vibrant AIEd research community, much of this foundational work is still in development. This tertiary review,Footnote 8 which is the first of its kind in AIEd, provides the foundation for future conceptualisation and utilisation of AI in higher education.

Contribution of this review

Whilst evidence synthesis is a welcome approach to gaining insight into effective applications of AI in education, there is a risk of ‘research waste’ in every field of research due to a duplication of efforts, by conducting reviews on the same or similar topics (Grainger et al., 2020; Siontis & Ioannidis, 2018). This can occur when researchers do not give enough consideration to work that has already been published, costing valuable time, effort, and money (Robinson et al., 2021). In order to help avoid research waste, and to map the state of the AIEd field in higher education (AIHEd), this review is the first to undertake a tertiary review approach (Kitchenham et al., 2009). A tertiary review is a type of research that synthesises evidence from secondary studies, such as systematic reviews, and is sometimes known as a review of reviews or as an overview (Sutton et al., 2019). This method allows researchers to gain an overarching meta view of a field through a systematic process, identifying and analysing types of evidence and key characteristics, exploring how research has been conducted, and identifying gaps in the literature to better guide future field development (Polanin et al., 2017). Given the current interest around the uptake of generative AI, now is the perfect time to take stock of where we have been, in order to provide suggestions for where we might go in the future.

Research questions

Against this background, the following research question and sub questions guide this review:

• 1. What is the nature and scope of AIEd evidence synthesis in higher education (AIHEd)?