The current state of AI in Teaching and Learning in Higher Education
Markus Schatten. Faculty of Organization and Informatics, University of Zagreb, HR
The integration of Artificial Intelligence (AI) in teaching and learning is rapidly transforming higher education. This keynote will explore the current state of AI adoption, focusing on three key areas: policies, tools, and future directions. Current policies around AI usage vary significantly across institutions, with some universities developing comprehensive frameworks to ensure ethical use and data privacy, while others lag behind, leading to inconsistencies in AI deployment. The session will highlight leading policies and recommend strategies to create a cohesive regulatory environment that fosters innovation while safeguarding academic integrity.
On the tools front, AI-driven applications are revolutionizing student engagement and administrative processes, from intelligent tutoring systems and adaptive learning platforms to AI-powered academic advising. However, widespread adoption faces challenges, including faculty readiness, resource allocation, and concerns around the dehumanization of education.
Looking ahead, the keynote will outline critical policy shifts needed to address these challenges and maximize the potential of AI in higher education. By harmonizing policies across institutions and enhancing AI literacy among educators, the future of AI in teaching and learning can be more inclusive, effective, and aligned with the diverse needs of the academic community.
Articulation of the concerns from an educational manager regarding the impact of AI in teaching and learning
Janna Pietikäinen and Hanna-Riitta Kymäläinen, Faculty of Agriculture and Forestry, University of Helsinki. FI
The use of AI has increased recently, and especially easy-to-use services such as ChatGPT have brought the use and discussion of AI to universities. As the AI system is a machine-based system that generates outputs such as predictions, content, recommendations or decisions that can affect physical or virtual environments (EU AI Act, 2024), AI outputs can also be used in different activities and tasks in higher education for example in design and implementation of teaching. In this presentation, we will address, from the perspective of education managers, the reflections and concerns raised in the university community regarding the use of AI in teaching and learning. The authors of the presentation are the Vice-dean of Education and the Director of the bachelor’s programme in Agricultural Sciences at the Faculty of Agriculture and Forestry, University of Helsinki. We discuss the views of different groups in the university community, i.e. teachers and students, on the use of AI in teaching and learning and in the assessment of learning outcomes. We also present the University of Helsinki's guidelines and policies on the use of AI in teaching and assess how the university as an organisation has responded to the challenge of the changing teaching methods and content posed by AI. The presentation is based on discussions held in AI-related events at the University of Helsinki, internal discussions in various committee meetings, interviews, student surveys and workshops, and the University's public policies and guidelines.
How Ghent University is addressing the use of generative AI in teaching and learning
Mieke Uyttendaele and Evelyne De Caluwé, Faculty Office of Educational Support, Faculty of Bio-Science Engineering, Ghent University, BE
Ghent University opts for responsible use of generative AI (GenAI) tools in the teaching practice. In the spring of 2023, Ghent University developed generic guidelines on using GenAI tools in teaching with a priority on its application in written assignments, including bachelor and master dissertations and their evaluation. A faculty-led working group further discussed these with a focus on discussion of the impact of GenAI tools on the student’s learning and the validity of the assessment. In addition, a successful inspiration session was held at the Faculty of Bio-Science Engineering (FBE) by FBE-lecturers to FBE lecturers about using GenAI tools in everyday teaching practice. The main message was: i) GenAI tools are an extra classmate for both teachers and students; ii) they can be used to stimulate analytical thinking and critical reflection in the classroom; iii) oral discussion in assessment demonstrates the importance of one's own (subject-related) knowledge and literature search is (re)gaining importance. The remaining concerns are the ethical implications of GenAI, such as the potential to cause inequality and its impact on research integrity and confidentiality in handling research results. The latter was highlighted in a survey of employees at the Faculty Bio-science Career event. Next on the agenda is investing in generative AI literacy and enabling academic staff and students to use GenAI tools effectively in their lecturing or learning style. University-wide learning paths and faculty-specific workshops are being conducted to ensure a consistent level of AI literacy among educators and students. Looking ahead, the faculty aims to integrate generative AI (or, more broadly, digital competencies) into its curriculum. It acknowledges that while generative AI offers new opportunities, it also pushes us to the fundamentals of academic education and research.
Reimagining authentic assessment: the power (& some peril) of generative AI
Nigel Francis, School of Biosciences, University of Cardiff, UK
Generative AI (GenAI) tools are evolving rapidly, leading to a rethinking of existing assessment modalities across the higher education (HE) sector. Traditional forms of assessment, including take-home essays, exams, and quizzes, are all increasingly susceptible to GenAI's capabilities. This raises significant concerns about academic integrity and the authenticity of student work. GenAI is often seen as dual-edged, being both a threat and an enabler. Different assessment types that are most susceptible to AI will be explored to highlight the growing need for educators to reconsider how student learning is evaluated; however, rather than viewing AI as a threat, the focus will be on its potential to enhance educational outcomes. Authentic assessments allow educators to harness the power of AI, embedding it into assessment design to create more meaningful, engaging and reflective learning experiences for future cohorts