“Slovak Collaborative Open Research and Education Ecosystem for AI”
Project KEGA 008ŽU-4/2024 is supported by the Cultural and Educational Grant Agency MŠVVaŠ SR (KEGA).
The Challenge
Over the past decade and more, education has increasingly been shifting from the setting where the teacher communicates knowledge to students to a setting where students are much more actively engaged in the learning process. This is particularly relevant in areas such as artificial intelligence (AI) and machine learning (ML), where the half-life of knowledge (the time in which half of what we learn is no longer valid) is now estimated by experts to be just several months. We need to put much more emphasis on building meta-skills that will allow students to engage with content on their own. The role of the teacher is to facilitate the learning process, make it more effective and to give expert advice and feedback to students.
This transformation places very high demands on educators in AI and ML, who not only need to change their philosophy of teaching at a time when the field itself is evolving rapidly, but must now more than ever also be deeply immersed in research in the field – to be able to effectively accompany their students and communicate valuable experience to them. Crucially, the switch to the new teaching paradigm also places high demands onto preparation, creation of new educational content and experimenting with new approaches to best fit the profile of the students, etc. To provide an instance of this – when switching some classes to the flipped classroom format, students need to be provided with some material ahead of time and this is typically significantly different from what would have been used for the same class in a standard lecture format.
Consequently, also given the limited amount of experts in AI and ML in Slovak academia, it will not be sustainable for individual departments, faculties and universities to act as isolated islands when teaching these topics. On the contrary: it is crucial that there be effective cooperation across the entire network of institutions and their individual units – it is this kind of cooperation that we want to support in SCORE4AI.
The Collaborative Open Research and Education Ecosystem
The main goal of SCORE4AI, then, is the creation of the ecosystem necessary for the implementation of this type of cooperation. In particular, the following high-level means are to be used for this purpose:
- A. Creation of a network of experts: an association of experts dedicated to education in the field of artificial intelligence and machine learning.
- B. A common educational platform: the creation of a common electronic educational platform, which will allow the sharing of learning material, tools, forms and procedures, thereby significantly increasing the discoverability and findability of already created content, and will allow its parts to be recombined in new ways when updating existing courses and creating new ones.
- C. Educational activities: implementation of educational activities using the previous two means, including experimentation with innovative forms of education (flipped classroom, tutorial teaching, discussions, …), organization of inter-institutional seminars and lectures, involvement of students in research teams, management of student team projects across institutions, the sharing of material, best learning practices, etc.
- D. Supporting research activities: one of the basic theses of the project is that, within the emerging paradigm, high-quality education can only be performed in a strong connection with research in the same field, therefore the project will also support synergistic research activities.
Planned Outputs
| Output Title | Year |
| Report on the network of experts I, II | 2024, 2025 |
| Technical report regarding the platform I, II | 2024, 2025 |
| Educational activities (workshops, summer school, etc.) I, II, III | 2024, 2025, 2026 |
| HW/SW equipment updated I, II, III | 2024, 2025, 2026 |
| Initial educational content for the platform | 2025 |
| A demonstration course (created by recombining the initial content) | 2025 |
| Research outputs | 2026 |