Higher education institutions have actively seized the challenges and opportunities brought about by artificial intelligence (AI) as part of higher education and have systematically started to develop joint solutions. From the point of view of education, it is essential to maintain the high quality of education, ensure that curricula are up to date, and develop learning outcomes, taking into account the use of AI as part of assessment of competence. Joint solutions of universities of applied sciences (UAS), such as guidelines for the use of AI (Arene, 2024a), traffic light model (Arene 2024b) and joint training concepts (Puuska et al., 2024), support the utilisation of the opportunities offered by AI in education.

Recently, the use of AI in relation to the assessment of competence has been a particular topic of discussion. The EU Artificial Intelligence Regulation 2024/1689 aims to regulate the use of AI on a risk-based basis, where the use of AI in the access to education and soring of exams is classified as a high-risk use case (EU, 2025). Teachers have raised concerns about the loss of basic skills when AI makes it easy to find answers to the basic questions of the subject being studied. It is therefore essential to identify the things that the student needs to know by heart and those in which AI can be used critically by evaluating the information it produces. Critical assessment of information and the knowledge base and practices of the study field are key generic competencies of UAS students (Arene, 2022), the importance of which becomes even more topical in the AI era.

In universities of applied sciences, teachers can utilize the traffic light model to define the use of AI in assignments, and the assessment of assignments in relation to the use of AI. More specifically, teachers are recommended to identify on each course:

• learning assignments where the verification of basic competences is essential and where the use of AI should be prohibited (red traffic light)
• learning assignments where the development of critical thinking is essential,and where the use of AI is allowed (yellow traffic light) or required (blue traffic light).In these, the use of AI must be communicated, and it has an impact on the assessment. Critical thinking skills are also required when AI can be used freely in a learning assignment (green traffic light) and the assessment process does not guide the student to examine the use of AI as part of the assignment.

It is also advisable to integrate the traffic lights into the virtual learning environmentso that both teachers and students can use it as a tool for teaching and learning.

Teachers can also utilise AI in their own assessment work. AI-assisted assessment can be realised, for example, in the planning and implementation of assessment and feedback, or in compiling one’s own assessment message or feedback. In this case, the teacher is responsible for the actual assessment, but uses AI to help create, compile, refine and revise the material. The initial assessment of the possibility of cheating can be carried out with the help of AI, but it is the teacher’s responsibility to determine the actual situation. The principles for the responsible use of artificial intelligence (Figure 1) support the planning and implementation of an AI-assisted assessment process.

The use of AI for fraudulent demonstration of competence has increased with the user experiences of AI tools. AI has been used incorrectly, for example, in text production and information search. In this case, the student’s ability to think critically, search for information and field-specific competence are not demonstrated, not to mention the ethical principles of scientific research. However, it should be noted that AI is also a new tool for students, and growing into a higher education learner requires active development of AI literacy as part of their studies. Teachers are also looking for tips on how to support students’ use of AI. The implementation of the use of AI can be supported with clear instructions for students (Figure 2), contributing to the development of AI literacy.

Companies expect good information management skills from future graduates. With the help of AI, information can be accessed, edited and evaluated, but it is only useful for the company if the user is able to create a professional assessment and apply the information by utilising the core skills. Companies are adopting AI tools in an agile manner and actively training their staff, but the cycle of renewing curricula in higher education works slowly compared to the needs of corporate world. It is therefore important thattheresponsible use of AI tools is a significant part of the courses to ensure that learners gain an understanding of the field-specific needs and applications of AIand can apply their skills as part of their internships and thesis work.

The development of AI literacy is part of digital education and digital citizenship (Government, 2025). In the future, highly educated AI experts will be needed in every industry, and every university graduate must have AI literacy. Cooperation between higher education institutions can contribute to the growth of digital education and responsible digital courage, which is required of both teachers and learners. It is important to share experiences and best practices openly at the local, national and international levels to increase and strengthen competence. Cooperation across levels of education could support the development of basic competence and AI literacy when moving to higher education. New AI innovations are currently being developed also for assessment of competence. Through cooperation, Finland can become a model country for the responsible use of artificial intelligence as part of higher education.

Marjo Joshi, Dean / Seinäjoki University of Applied Sciences, marjo.joshi@seamk.fi

Digital pedagogy experts have participated in the production of this article: Henri Annala (TAMK), Linda Kantola (HAMK) and Sami Simpanen (HAMK).

Microsoft CoPilot  AI translation tool was utilised in the production of the English translation and checked by the original author M Joshi.

Figure 1. Principles of responsible use of artificial intelligence as part of the evaluation process (Annala et al., 2025)

Figure 2. Student guidance at the heart of the AI-assisted learning and assessment process – tips for teachers (Annala et al., 2025)

Sources:

Arene (2022). Recommendation on the common competencies of universities of applied sciences and their application. The Rectors’ Conference of Finnish Universities of Applied Sciences Arene ry. https://arene.fi/julkaisut/suositus-ammattikorkeakoulujen-yhteisista-kompetensseista-ja-niiden-soveltamisesta-2022/

Arene (2024a). Arene’s recommendations on the utilisation of artificial intelligence for universities of applied sciences. https://arene.fi/wp-content/uploads/PDF/2024/Teko%C3%A4lysuositukset/Arenen%20suositukset%20teko%C3%A4lyn%20hy%C3%B6dynt%C3%A4misest%C3%A4%20ammattikorkeakouluille%202024.pdf?_t=1730467050

Arene (2024b). Using artificial intelligence in learning tasks – traffic light model https://arene.fi/wp-content/uploads/PDF/2024/Teko%C3%A4lysuositukset/ARENE_AI_liikennevalomalli%20.pdf?_t=1730467050

European Union EU (2025). Shaping Europe’s digital future. https://digital-strategy.ec.europa.eu/policies/regulatory-framework-ai

Annala, H., Joshi, M., Kantola, L. & Simpala, S. (2025). Tekoälyn vaikutus arviointiin. Arenen Koulutuksesta vastaavien vararehtoreiden ja johtajien seminaaripäivät, Kuopio, 20.3.2025.

Puuska, H., Kallio, A. & Mäki, A. (2024). AI as a driving force in the RDI and Education activities in Universities of Applied Sciences. Blog 26.11.2024. AI as a driving force in the RDI and Education activities in Universities of Applied Sciences – CSC

Valtiovarainministeriö (2025). Artificial intelligence literacy as part of digital education – public administration outlooks. Discussion series on digital education 27.3.2025. https://okm.fi/tapahtumat/2025-03-27/tekoalylukutaito-osana-digitaalista-sivistysta-julkishallinnon-nakymat