1. Introduction

Generative AI (GenAI) is disrupting education. GenAI refers to a broader set of technologies that can generate new and unique content, in various formats such as text, images, audio, code, text, simulations, 3D objects, and videos (Moorhouse et al., 2023). The concept of GenAI is closely tied to Large Language Models (LLMs), which are explicitly designed to facilitate the creation of text-based materials. The advent of free, relatively easy-to-use, online LLM based conversational interfaces such as ChatGPT has quickly transformed students’ use of AI in education. Specifically, tools such as ChatGPT can automatically generate text in response to a human prompt and have raised implications for assessments (Sharples, 2022). As a result, many educators worry about new forms of academic dishonesty as students can simply copy and paste content generated by these technologies potentially engaging in uncredited use of AI-generated text, misrepresenting one’s abilities, and neglecting essential learning processes (Padillah, 2023; Mohammadkarimi, 2023; Habib et al., 2024). Hence, use of GenAI by students can undermine principles of fairness in education and diminish the value of personal achievements (Padillah, 2023).
Ethical concerns related to the use of GenAI such as bias, accessibility, and privacy have also been raised (Sabzalieva & Valentini, 2023). The issue of bias in produced content is a significant ethical challenge (Ferrer et al., 2021; Zhou, et al., 2023) as the models learn from data, and if that data reflects societal biases, the generated content can introduce or reinforce these biases in its outputs (Akter et al., 2021). Another concern is the potential for inequality arising from varying resources and prior knowledge required to access and utilise advanced functionalities of AI language models effectively (Ng et al., 2021). The digital divide, already a societal challenge, could be worsened as those with more resources and technical know-how could disproportionately benefit from these technologies. There are also concerns related to individual privacy and intellectual property rights. Using AI language models involves processing vast amounts of user data (Gupta et al., 2023) and copyrighted materials – but often without adequate notification or consent (Lucchi, 2023). The need for data input to train and fine-tune these models presents a risk to individual privacy, with questions arising about the security and responsible handling of sensitive information (Wu et al., 2023) by the private enterprises offering public access to GenAI technologies.
In response, several universities attempted to temporarily restrict or ban access to GenAI tools (Moorhouse et al., 2023). However, concerns have been raised about the impact of restricting access in academic contexts, considering the impact on students’ AI literacy and their readiness for a society increasingly powered by artificial intelligence (Chiu, 2024; Chiu 2023). Further, the disruptiveness of GenAI does not rest solely with its potential to interfere with student learning but also in its promise for enhancing student learning and the effectiveness of educators. A comprehensive guide on using ChatGPT and Artificial Intelligence in higher education published by UNESCO explains how AI tools used both by students and educators can enhance learning experiences. The authors of the guide present diverse roles AI can fulfil such as those of a personal tutor, co-designer, and motivator (Sabzalieva & Valentini, 2023). Furthermore, Miao (2021) highlights the importance of collaborative intelligence between humans and machines and describes four main areas in which there are emerging and potential applications of AI in education: (1) Education management and delivery, (2) Learning and assessment, (3) Empowering teachers and improving teaching, and (4) Promoting lifelong learning. Recognizing opportunities for disruption becomes essential for not only improving the effectiveness of education but also for preparing students with the skills required for employment in a world increasingly influenced by AI integration (Alekseeva, et al, 2021).
The rapidly evolving landscape of GenAI tools, with frequent releases of new tools and improvements to existing ones, necessitate continuous vigilance and adaptation (Gill et al., 2022). This dynamism requires stakeholders to stay up to date with developments to address emerging issues effectively, creating additional complexity in managing the responsible and ethical use of GenAI. As society faces these concerns, there is a need to find a balance that allows the benefits of advanced AI language models to be received while addressing the associated ethical and societal challenges. Considering the opportunities, concerns, and the immediacy of the disruption to university education, a process is needed that can gather insights into how AI is being used, assess whether such uses are effective or not, and bring stakeholders together to define acceptable uses of AI in education (Beardsley et al., 2024). Student learning agreements may offer a flexible, student-centred approach that gathers informative data while providing opportunities for students to become more thoughtful in their decision making related to the use of innovative technologies such as GenAI.