Page 77 - International Perspectives on Effective Teaching and Learning in Digital Education

AI in Higher Education: Analysis of Relevant Practices and Their Potential for Green Transition

Table 1 Total Number of Reported Cases of AI Implementation in Different Areas
of Educational Activities (KLASIUS–P).
Areas of educational activities (KLASIUS–P) Total number of
reported practices
KLASIUS–P1: Education sciences and teacher training 4
KLASIUS–P: Arts and humanities 8
KLASIUS–P3: Social sciences, business, law, and administration 5
KLASIUS–P4: Natural sciences, mathematics, and computer science 
KLASIUS–P5: Engineering, manufacturing technologies, and construction 4
KLASIUS–P6: Agriculture, forestry, fisheries, and veterinary science 
KLASIUS–P7: Health and social care 1
KLASIUS–P8: Services 
Total 6

gogical process at the University of Ljubljana and how the use of AI in educa-
tion and sustainable development influences each other.

RQ1 In which areas of educational activities according to the KLASIUS-P
classification is AI used as part of the study programmes at the
faculties of the University of Ljubljana?
The results of the analysis for the first research question show that AI tools are
currently being used in various areas of educational activities.
The data presented in Figure 1 shows that AI is used most frequently in
KLASIUS-P with 8 reported cases, indicating a growing interest in the use of
AI tools for tasks such as language processing, content analysis and creative
support. The potential of AI to improve both student engagement and edu-
cational resources in this traditionally qualitative area may be one reason for
its prominent use.
Closely followed by KLASIUS-P3 with 5 reported cases. In these areas, AI is
likely to be used to support data-driven research, improve decision-making
processes and enable interactive learning experiences.
In the areas KLASIUS-P1 and KLASIUS-P5 there are 4 reported cases each
for the use of AI. In the educational sciences, AI can be used to personalise
learning, improve instructional methodologies, etc. In engineering, AI tools
are suitable for core aspects of these fields, such as creating simulations,
modelling and optimising problem-solving skills.
The results also show that KLASIUS-P4 and KLASIUS-P6 each have  report-
ed cases of AI implementation. These areas often require complex data anal-
yses and predictions modelling, which can be well supported by AI. However,

77