Guidance in Education

Future Developments in Knowledge within AI

To meet the organization's needs for support and guidance, a diverse working group has been assembled to discuss and propose examination structures that counteract cheating while remaining meaningful in relation to students' learning and the development of knowledge, especially in critical and creative academic writing.

Together, we need to develop knowledge and approaches that contribute to maintaining high-quality education in light of changes in our contemporary context. In the long term, we see that the development of generative AI raises questions about what constitutes important knowledge today and in the future, as well as how we support learning in a broad sense to develop these skills. This is an area that is evolving rapidly, and we need to keep the discussion alive at all levels within the organization.

Three Important Questions for Higher Education

• Regarding students' competence: How do we ensure that students develop judgment and skills in relation to AI so that they are prepared for a professional life and citizenship that involves AI?
• About learning processes: How do we create significant and meaningful learning experiences for students so that they do not consciously or unconsciously misuse AI and other tools in a way that undermines the intended learning?
• Regarding examination, deception, and cheating: How do we design knowledge assessment and examinations in a way that captures the student's knowledge and knowledge expression, as opposed to products and expressions that students submit that do not reflect an individual's ability or competence?

Challenges with AI in Education

A significant challenge for us as an authority is to ensure that students are provided with a purposeful and supportive learning environment and that they do not use generative AI unethically or misleadingly during examinations. To address the challenge posed by AI, Bjelobaba (2020) refers to four approaches. These approaches illustrate how one can deal with this challenge in different ways:

- Preventive Culture

- Pedagogical Methods

- Monitoring

- Deterrence

The first two approaches primarily focus on learning, while the latter two have a greater focus on controlling students' behavior. An example of working with a preventive culture could be that teaching teams within courses and programs collectively agree on the approach and guidelines related to AI that are communicated to students. The goal is to support students' professional attitudes toward AI and highlight what is meaningful for their learning. The pace of change within AI will require continuous review of resources and support for students and staff.