Conference Program

Around the world, the large language models, LLMs, first introduced with ChatGPT in 2023, have caused turbulence in education (Jin et al., 2025; OECD, 2026).

Governments are developing guidelines and recommendations for Higher Education Institutions (HEIs) on how to handle Artificial Intelligence (AI).

Universities are developing new teaching, learning and assessment models, including dimensions related to privacy and ethics. For instance, the Russell Group (2023), which represents 24 UK research-intensive universities, has established a set of guiding principles to empower both staff and students to navigate this evolving landscape. These core principles focus on fostering AI literacy to help users understand the tool's limitations and ethical risks, such as bias, privacy, and plagiarism. Furthermore, universities commit to equipping staff with the skills to support students, adapting teaching and assessment methods to ensure fairness and academic rigour, and collaborating across the sector to share best practices.

Students, too, are concerned with avoiding cheating, plagiarism, and fair assessments. They are viewed as active participants who must become AI-literate to navigate an increasingly AI-enabled world. They are expected to understand not only the opportunities of generative AI but also its significant limitations, such as potential bias, privacy risks, and inaccuracies. While these tools can personalise learning and enhance critical reasoning, students remain accountable for the accuracy of any AI-generated information they use.

International bodies such as UNESCO (2023, 2024) provide frameworks for understanding generative AI in education. Grounded in a humanistic vision that places human agency at the centre of AI integration, such frameworks stress the importance of equipping education systems with the skills to use AI effectively and emphasise the need for policies and practices that balance technological innovation with ethical responsibility. More specifically, higher education institutions should support students and staff in using AI ethically and transparently rather than implementing bans. Universities should redesign assessments to prioritise human values like creativity and complex problem-solving, while utilising AI as a “coach” for research planning and data exploration. Ultimately, institutions should leverage students' metacognitive skills to critically verify AI outputs and safeguard human agency.

In this paper, our aim is to further explore how national HEI systems are grappling with these challenges. By looking at Italy and Norway, two countries with slightly different organisation of their HEIs, we will identify their governmental approaches towards the implementation of AI in higher education. Informed by the analysis of national steering documents and green papers, our aim is to examine the extent to which the dimensions outlined in UNESCO’s framework are reflected in national guidelines for implementing AI in higher education. We ask: To what extent are the dimensions of UNESCO’s framework reflected in national guidelines for the implementation of AI in higher education? This involves examining the priorities of national policies, such as ethical governance, transparency, teacher and institutional capacity-building, and responsible innovation, and how closely these align with UNESCO’s recommendations for integrating AI in a safe, equitable and pedagogically meaningful way.