Application of Artificial Intelligence-Based Digital Technologies in Education – BrAIn

Development of Digital Competencies Based on Scientific Insights

• Creation, Revision, and Implementation of Curricula for Extracurricular Activities and Elective Subjects for the Development of Students’ Digital Competencies

In this activity, curricula for extracurricular activities and elective subjects were created with the goal of developing critical thinking about the impact of emerging technologies, including artificial intelligence, and preparing students for practical work with emerging technologies. It is recommended that the extracurricular activity be implemented in the 7th and 8th grades of primary school, while the elective subject should be offered in the 2nd and 3rd years of secondary school. The curricula can also be implemented in other grades depending on the possibilities of each individual school.

The curriculum for extracurricular activities in primary schools is available here. (in Croatian)

The curriculum for elective subjects in secondary schools is available here. (in Croatian)

Experimental implementation of the curricula will take place during the 2024/2025 school year. Simultaneously, research will be conducted to gather feedback on the experiences of applying the curricula.

The curricula will be revised during the 2025/2026 school year based on feedback from participants in their experimental implementation.

Regular implementation of the curricula for extracurricular activities and elective subjects will begin in the 2025/2026 school year and continue until the end of the project (31st August 2029).

• Creation of Digital Educational Content Based on the Curricula for Extracurricular Activities and Elective Subjects

The implementation of the curricula for extracurricular activities and elective subjects will be supported by the development of corresponding digital educational content for learning and teaching, particularly covering topics that are poorly available in the Croatian language.

• Educational Programme for School Education Staff

Users (primarily teachers, but also professional associates and school principals) will be educated through various formats of education, including live and online training, such as workshops organised at locations, webinars, and e-courses.

The education will also cover topics for implementing the developed curricula.

Research

Within the research activities, continuous dissemination of results/recommendations for promoting well-being related to the use of technology for all key stakeholders (educational policymakers, educators, students, parents) is planned. The research will be carried out in collaboration with partners selected through a public call. It is expected that partners will participate in all phases of the research.

The research will be conducted in two phases:

• Pilot Research – This will include an analysis of the current situation through focus groups and semi-structured interviews, needs analysis, and a review of relevant empirical research results and scientific publications. During this phase, the methodology for the large-scale research will also be developed.
• Large-Scale Research – An in-depth longitudinal study. Based on the pilot research, relevant research areas will be identified on which different research teams will work. The research will cover all three forms of education and all stakeholders in the educational process of children and adolescents: (1) formal education; (2) non-formal education; (3) informal education, as well as all stakeholders in the education system: (1) students, (2) educators, (3) parents/guardians.

The intended result at the activity level is the completion of research on the impact of digital technologies on students and the curricula for extracurricular activities and elective subjects aimed at developing students’ digital competencies.

The goal of this activity is to develop a system of smart recommendations that will provide better insights into student achievements and propose a personalised approach to the development of each student, with the aim of discovering students’ affinities and optimising their learning outcomes. The following activities are planned to achieve this goal:

Analysis of Needs for Key Stakeholders and End Users

The analysis of the needs of key stakeholders and end users will be carried out in the context of building the smart recommendation system and the necessary upgrades in the form of focus groups with key stakeholders and end users. The research conducted will create the concept for the final solution of the smart recommendation system, with the initial concepts to be tested during the project using the same methodology, aligning system development with improved concepts.

Development of Ethical and Pedagogical Standards

Interventions in educational processes and systems can have long-term effects on the development of society, and therefore each such intervention must be carried out with particular care. This is especially important for systems that aim to improve the educational system and the personal development of students through automation, particularly in the area of artificial intelligence that develops self-learning systems. AI-based systems rely on large volumes of data collected, so special attention must be paid to privacy protection and its integration into all parts of the system from the start.

Upgrading Existing Systems to Build Standards and Open Data Sources

CARNET has established or controls systems and applications that can be valuable sources of data on behaviour and learning outcomes. Some of this data is already integrated into CARNET’s analytical ecosystem, but certain systems will require adjustments to allow data collection in a standardised manner using open and standardised protocols.

By developing a communication platform, CARNET will establish a system that enables efficient and secure communication between all stakeholders in the education system and the exchange of messages among users of CARNET’s educational services. In addition to providing a secure environment for educational system stakeholders, the communication platform will contribute to a better understanding of user needs and habits, further enriching data sources.

Building the Smart Recommendations System

The smart recommendations system for teaching and learning will rely on data mining and predictive analytics methods, which will allow data analysts and researchers to find relationships between data, identify predictors using data mining tools, develop and test predictive models, and apply these models to real-world data. Based on available data sources, the system will assist teachers in tracking students’ digital achievements and allow a more personalised approach to each student, primarily through a better understanding of their habits in the educational process and their achievements. The system will enable each student, as the end user, to better understand their own achievements relative to their peers and understand the recommendations that can help them focus on areas where they are most motivated, but also to master learning outcomes in areas of lesser interest. Parents, teachers, and school staff will be given access to student achievements and be alerted to behavioural deviations, sending early warnings when necessary.

Education of Key Stakeholders

To avoid misinterpretation of the smart recommendations system, special attention will be given to educating key stakeholders and end users about the system’s capabilities and how to use it. Education will be conducted through a combination of online and live training, tailored to each specific group.

Building and Upgrading the Computational and Communication Infrastructure of Smart Recommendations

To ensure that the smart recommendations system meets its intended purpose, appropriate infrastructure must be provided in CARNET’s data centres to host applications, store large volumes of data, and support other project-related services.

AIOps (Artificial Intelligence for IT Operations) systems combine large-scale data collection and machine learning to automate IT operational processes, including event correlation in networks, anomaly detection, and fault cause identification.

Procurement of AIOps Systems:

• Current Service Snapshot and Business Process Analysis
• Implementation of AIOps Systems

Training CARNET Staff on AIOps Systems

The AIOps system is aimed at CARNET network users in schools (teaching staff and students), solving issues by correlating data through machine learning. The AIOps system detects and resolves faults before the end-user notices any issues, thereby improving the user experience within the CARNET network.

The goal of implementing the system is to automate operations and increase business efficiency, ultimately improving the user experience for staff and students in schools.

A system handling confidential data requires appropriate cybersecurity and protection. The project includes the development of educational materials that will be available in an online environment, which will be connected to various student data and, in combination, will provide users with automated recommendations to deepen knowledge and/or eliminate gaps and misunderstandings. It is therefore necessary to develop automated proactive and reactive security processes that will ensure continuous verification of network security and, in the event of computer security incidents, handle them according to relevant procedures and protocols.

Development of the Automated Cybersecurity Incident Resolution System

The development of the conceptual and main design for the automated and intelligent resolution of cybersecurity incidents is based on a detailed and precise analysis of internal data, knowledge, and incident resolution processes, as well as the correlation of data with external data. Key activities will address new threats and sophisticated attacks, including the creation of the main design and related concepts, system development, and the creation of guidelines for use and maintenance.

Upgrading the System for Early Detection of Vulnerabilities and Potential Incidents

With the rapid development of website technologies, vulnerabilities in new technologies, and new methods used in cyberattacks, there is a need for continuous upgrades to the system that scans all HR websites for vulnerabilities. The system will be integrated with the automated cybersecurity incident processing system. Activities under this segment will involve the creation of conceptual and technical documentation for upgrading the system that scans the HR web space to identify vulnerable or compromised websites, and then eliminate the discovered vulnerabilities.

Aligning Incident Reporting and Processing with EU and National Regulations

In light of the new NIS 2 Directive (EU) 2022/2555, it is necessary to align incident reporting and processing procedures with the EU directive and the corresponding national legislation. A snapshot of the current situation and identification of key services will be created for the alignment process. Additionally, this activity will align procedures and adapt the capacity of the National CERT to meet the requirements of Directive (EU) 2022/2555 as the relevant CSIRT in the prescribed sectors.