1. The Role of AI in Modern Informatics Education
Artificial Intelligence (AI) is rapidly transforming education, influencing teaching methodologies and learning processes. Within the overall mission of developing innovative approaches for informatics education, the DIGITAL FIRST project also focuses on defining the necessary competences for informatics teachers within this evolving educational landscape. While the AI competences are not at the core of our study, it is crucial to address those required by the increasing integration of AI in education.
Although the functional approach of DIGITAL FIRST does not mandate specific tools or techniques, AI will have a profound impact on how we will communicate with computers and how natural language will be used. Consequently, the competences outlined in our project must reflect this shift, ensuring that informatics teachers are equipped to navigate and leverage AI effectively in their practices.
To this end, the first step was reviewing the state of the art in this realm, as this is a very relevant topic for most educational administrations worldwide, and some remarkable advances must be taken into account. One conclusion that we can extract from such revision, is that teacher competences on AI have been mainly faced for K-12 levels, that is, for secondary school teachers (Kim et al, 2021; Chiu et al, 2024, Ng et al, 2023). Such a formal definition for primary school and university levels is still scarce (Kim, 2023; Zao, 2021; Mah, 2024). A second conclusion is that they have been defined for general teachers, not specifically for informatics ones, and the consequence is that they have been focused on the competences that teachers require to use AI tools in their practice, assuming a non-technical background (Mikeladze et al, 2024).
Figure 1 – Three levels of AI education, AI report, European Digital Education Hub
2. Defining AI-Related Teacher Competences
I.Understanding AI Education Levels
The European Digital Education Hub’s AI report categorizes AI education into three levels that influence teacher competences (see Figure 1):
Education for AI – Ensuring students can engage critically and safely with AI technologies. This foundational level fosters critical thinking and responsible interaction with AI-driven environments, to live in a world surrounded and shaped by AI. This level should be compulsory for all students, as it prepares them for the next ones.
Below education for AI, we can find two parallel levels:
Education with AI – Incorporating AI-based tools into teaching and learning. Educators and students must understand these tools’ functionalities, benefits, and limitations to integrate them effectively into teaching and learning.
Education about AI – Training students as AI developers, focusing on technical aspects such as machine learning, reasoning, or perception.
This organization of AI education levels is key, as the first one provides students with a solid background to face the other two, which are more specific.
II. Core AI competences: user perspective
Considering these 3 levels, we can face the definition of teacher competences more clearly. The education for AI and the education with AI levels are more general, and every teacher should incorporate them as part of their digital competence set. This is the idea followed in UNESCO’s AI competency framework for teachers, which is focused on lifelong professional development, offering a reference framework for national competency development and training programmes. It aims to ensure that teachers are equipped to use AI responsibly and effectively while minimizing potential risks to students and society.
The UNESCO framework establishes five key competency areas:
(1) A human-centered mindset
(2) Ethics of AI
(3) AI foundations and applications
(4) AI pedagogy
(5) AI for professional development.
Of course, the informatics teachers engaged in the DIGITAL FIRST project must include these competences in their training, as they are also users of AI tools, and in many cases, they will have the responsibility of introducing students in this technology.
III. Technical AI competences: developer perspective
If we move to the education about AI level, things are more complicated. The UNESCO framework for teachers does not include such a set of more technical competences clearly, so it cannot be taken as a reference for our project. There are some remarkable initiatives from educational researchers as (Kim et al, 2021) that have been facing this issue taking as inspiration existing educational programmes in this scope, as those from USA (AI4K12), China, India or Korea (Unesco, 2022). They include core AI topics like machine learning, representation, reasoning, natural interaction, or perception, which are adapted to the level to introduce them in classes, mainly through computational programming activities (Bellas et al, 2023). A draft proposal for the competency set can also be found in the AI report of the European Digital Education Hub (p. 17), but deeper research is required in this realm to establish the required competences, mainly in pre-university levels.
Such a group of teacher competences related to the education about AI are especially relevant in the DIGITAL FIRST project, as the technical knowledge they imply in topics related to computer science can be faced only by informatics teachers at schools. The current situation in most European countries is that such technical courses about AI are not present in educational plans, but this situation will change in the future. For instance, the Galician region in Spain, already has an official course about “AI Technologies” that includes computer vision and machine learning topics through Python language and, currently, only informatics teachers can face them with confidence.
A formal approach towards this definition of AI technical competences could take the UNESCO AI competency framework for students as a reference. It was designed to guide policymakers, educators and curriculum developers in equipping students with the necessary skills, knowledge and values to engage with AI effectively. It focuses on four core competencies: (1) A human-centred mindset (2) Ethics of AI (3) AI techniques and applications (4) AI system design. The key difference with the teachers’ one, is that this framework includes specific competences about AI, mainly those related to points 3 and 4, as we have summarized in Table 1. Consequently, by analyzing such expected competences for students, we could define those required for the teachers.
Table 1 – AI competences for students, UNESCO framework
| Competency Area | Description | Key Techniques | System Design Considerations |
| Developing AI Tools | Students should be able to apply knowledge of data and algorithms to customize existing AI toolkits and create task-based AI tools. They should integrate human-centred and ethical considerations. | AI model customization, low-code and full-code approaches, data analysis | Task-based customization, ethical AI assessment, teamwork |
| Customization and Creativity | Encourage students to creatively apply AI knowledge to customize AI toolkits and modify programming codes, including open-source options, for solving real-world problems. | Dataset enhancement, AI development platforms, open-source AI modification | Creative AI design, leveraging AI toolkits, problem-solving variations |
| Enhancing AI Performance | Support students in modifying datasets, using automated data collection tools, preprocessing data, and customizing AI models for crafting AI tools. | Data scraping, AI preprocessing, task-specific AI customization | Real-world dataset applications, automated data collection, computational efficiency |
| AI System Evaluation | Guide students in testing AI applications using adapted performance metrics (e.g., accuracy, precision, F1 score, confusion matrices, ROC curves) and simulating user feedback for ethical compliance. | Performance testing metrics, model evaluation, ethical auditing | Robustness testing, ethical feedback integration, visualization tools |
| Optimization and Ethical Consideration | Develop students’ skills to optimize, reconfigure, or shut down AI systems based on performance testing and user feedback. Foster awareness of corporate social responsibility and ethical AI governance. | Algorithm optimization, reconceptualization of AI models, AI system governance | Human-centered AI optimization, decision-making on AI modifications |
| AI Co-Creation and Community Engagement | Encourage students to engage with AI creator communities, participate in collaborative AI tool development, and analyze long-term societal impacts of AI applications. | Community-driven AI projects, open-source collaboration, AI policy discussions | Collaboration with AI communities, societal impact discussions, governance framework analysis |
3. Aligning AI Competences with DIGITAL FIRST
To conclude, specific teacher competences related to AI will also be included in the competences defined by DIGITAL FIRST for teachers. Of course, they will be framed in the functional approach that is the core contribution of the project, and they will be particularized for informatics teachers. But we will cover the 3 educational levels associated with AI education.
To this end, the UNESCO AI Competency Framework for teachers will be used as the reference in terms of education for AI and education with AI, and the UNESCO AI Competency Framework for students will be analyzed to define those in terms of education about AI.
The set of teacher competences will be validated through specific teacher training, and later using the final student piloting. Therefore, our project results and contributions will be aligned with the new educational framework imposed by the impact of AI in our society.
Stay tuned to our dissemination channels to get the updates!
