Introducing AIOS: AI-Driven Open Science – a new Erasmus+ curriculum
29/09/2026Artificial Intelligence is rapidly changing how research is conducted, analysed and communicated. At the same time, Open Science is reshaping how scientific knowledge, data and research outputs are created, shared and reused. Bringing these developments together creates significant opportunities, but also raises important challenges for universities, researchers and research-driven organisations.
AIOS - an Erasmus+ Cooperation Partnership in Higher Education – brings together five organisations from four countries, combining complementary expertise in higher education, Artificial Intelligence, Open Science, digital learning, software engineering and industry engagement that responds to this emerging landscape by helping develop the knowledge, skills and educational resources needed to use Artificial Intelligence effectively and responsibly within Open Science
The partnership comprises the University of Thessaly, which coordinates the project, the Hellenic Open University, Alma Mater Studiorum - Università di Bologna, the Norwegian University of Science and Technology (NTNU) and the Kharkiv IT Cluster, which contributes a strong industry and innovation ecosystem perspective. Together, the partners will develop, test and disseminate the project's tools and educational resources through curriculum development, training activities, pilot implementation, stakeholder engagement and multiplier events, with the aim of creating results that can be used well beyond the participating institutions.
The final goal is to deliver a complete, openly licensed Moodle-based MOOC that can be used, reused and adapted by learners and institutions beyond the AIOS partnership.
The Open Science Officer
Technology and infrastructure alone are not enough to implement Open Science successfully. Institutions also need people who understand open-access policies, FAIR data principles, research data management, AI-supported research workflows, ethical and legal requirements, institutional strategy and the practical challenges researchers encounter in their everyday work.
A central element of AIOS is therefore the development of the Open Science Officer (OSO) role. AIOS is developing a dedicated curriculum to support this interdisciplinary competence profile and prepare an initial cohort of AIOS Open Science Officers.
These professionals will be equipped to support researchers, connect institutional policies with everyday research practices, promote responsible uses of AI and contribute to a stronger Open Science culture within their organisations, whether in academic, research or business environments.
From universities to research and industry
Although higher education is at the centre of AIOS, the relevance of Open Science and AI competences extends well beyond universities. Research centres, high-technology companies and other research-driven organisations are also increasingly required to manage data responsibly, collaborate openly and understand the opportunities and limitations of AI-supported research.
AIOS therefore aims to strengthen connections between academia, research and industry, while engaging researchers, educators, university leaders, students, policymakers, professionals, citizen-science communities and the wider public.
By bringing together educational, research and industry perspectives, the project aims to contribute to a more open, collaborative and responsible research ecosystem in which AI can support knowledge sharing, research quality, reproducibility and wider access to scientific results.
Curriculum Logic
The AIOS curriculum is built around two closely connected ideas: learning should progress in a clear and meaningful way, and Artificial Intelligence should be integrated into Open Science rather than treated as a separate topic.
The curriculum is therefore designed as a connected learning pathway rather than simply a list of modules. Learners begin with a common foundation and gradually move towards more specialised and applied competences. Each part of the curriculum builds on knowledge and skills developed earlier, helping participants understand not only individual Open Science practices, but also how they fit together in real research, education and professional settings.
This progression is represented in Figure 1, which shows how competences, learning outcomes, Learning Journeys, assessment and the technical learning environment are connected. The aim is to make the curriculum easier to navigate and to ensure that learners develop the right foundations before moving to more advanced or specialised activities.
A second key principle is the close relationship between Open Science and Artificial Intelligence.
Open Science seeks to make scientific knowledge more accessible, transparent, reusable and collaborative. It includes practices such as open-access publishing, research-data management, Open Educational Resources, reproducible research and stronger engagement between research communities and society.
Artificial Intelligence can support many of these activities. AI-based tools can help researchers explore scientific literature, organise and analyse data, generate metadata, document research processes and manage increasingly complex information.
However, the use of AI also introduces new responsibilities. Researchers and professionals need to consider the reliability of AI-generated outputs, privacy and sensitive information, bias, transparency, intellectual property and human accountability.
For this reason, AIOS approaches AI and Open Science from two complementary perspectives:
Open AI for Everyone focuses on making the use of AI more accessible, understandable, transparent and responsible.
AI for Open Science explores how AI can support research workflows, open-data analysis, knowledge discovery, documentation and scientific collaboration.
AIOS therefore does not simply teach participants how to use individual AI tools. The objective is to develop the competences needed to decide when, where and how AI can be used responsibly within Open Science practices.
A Connected Learning Experience
The AIOS curriculum is designed to move from understanding to practice. Learners are not only introduced to concepts, but are also asked to apply them through realistic activities.
For example, someone learning about FAIR data may prepare basic documentation, select an appropriate repository or develop a data-management plan. A learner working on responsible AI may evaluate AI-generated content, document how a tool was used and identify possible risks or limitations.
This practical approach helps learners connect knowledge with real professional tasks and allows the same curriculum to serve different backgrounds and roles.
The Preliminary AIOS Curriculum
The current AIOS curriculum proposal is organised into five thematic sections and twenty modules, corresponding to an indicative full-course workload of 8 ECTS credits.
The five sections are:
- Section A – Open Science Fundamentals. Introduces the principles and values of Open Science, research-data management, FAIR principles, transparent and reproducible research, and the foundations of Artificial Intelligence.
- Section B – Open Science Resources, Repositories and Data Workflows. Focuses on practical skills for selecting repositories, managing and documenting research resources, working with metadata, preparing and analysing data, and evaluating quality and reusability.
- Section C – Management Literacy and Implementation Planning. Addresses the organisational aspects of Open Science, including institutional readiness, stakeholder engagement, project planning, resource allocation, sustainability and the implementation of Open Science initiatives.
- Section D – Legal and Ethical Open Science. Explores privacy, intellectual property, licensing, data governance, ethical responsibilities and the legal and organisational implications of using AI in research and education.
- Section E – Applied Open Science and AI Practice in Educational and Professional Contexts. Translates the knowledge acquired in previous sections into practical activities, including open educational resources, real-world applications and hands-on use of AI tools in Open Science workflows.
AI is integrated throughout the curriculum rather than confined to a single section. Particular attention is given to practical applications, responsible use and the verification of AI-generated results.
The consortium will develop 10 new core training modules, complemented by 10 Open Educational Resource modules adapted from the partners' existing educational portfolios. Together, these will form the AIOS Training Kit.
All 20 modules will be made available in five languages, English, Greek, Norwegian, Italian and Ukrainian, helping make the project's educational resources accessible to a wider European and international audience.
The curriculum and educational resources will subsequently be validated through pilot training and assessment, with feedback from learners, trainers and experts contributing to their further refinement.
Learning Journeys for Different Professional Needs
Not everyone working with Open Science needs the same competences or the same level of specialisation. To address this diversity, AIOS proposes five personalised Learning Journeys, each combining mandatory, recommended and optional learning activities:
- Athena – Institutional AI-Driven Open Science Expert. Designed for prospective Open Science Officers, university managers and institutional support staff. It focuses on coordinating Open Science initiatives, developing institutional strategies and supporting responsible AI adoption.
- Apollo – Open Academic Research and Scholarly Communication. Intended for researchers, doctoral candidates and academic staff. It concentrates on integrating Open Science and AI into research activities, data management, reproducibility, publishing and scientific communication.
- Hephaestus – Research Data, Reproducibility, Education and AI Practices. Addresses research-support professionals, data specialists, educators and trainers. It combines practical data and repository workflows with open educational resources, teaching activities, communication and responsible AI use. An optional Train-the-Trainer extension supports professionals who will train others.
- Prometheus – Open Science for Business and Innovation. Targets R&D professionals, innovation managers and research-driven businesses. It explores how Open Science, open data, open-source resources and AI can support innovation while respecting intellectual property, confidentiality and commercial requirements.
- Themis – Open Science for Policy and Public Decision-Making. Designed for policymakers, public administrators and professionals involved in research and innovation policy. It focuses on transparent decision-making, evidence-based policy, institutional governance and public engagement.
These Learning Journeys share a common educational foundation but place different emphasis on specific modules and practical activities. They will allow learners to assess their existing competences and follow training that corresponds to their individual needs. Participants may complete the full curriculum, follow personalised pathways based on selected modules, or use individual modules within existing educational and training programmes. An optional Train-the-Trainer extension will also prepare partner staff to deliver the AIOS curriculum effectively.
The way forward
Ultimately, AIOS aims to help universities, researchers and research-driven organisations move from awareness of Open Science and Artificial Intelligence towards their practical and responsible implementation.
By combining curriculum development, competence assessment, personalised learning journeys, Open Educational Resources, pilot training and stakeholder engagement, AIOS seeks to create tools and resources that remain useful beyond the formal lifetime of the project.
Over the coming months, the AIOS website will share project developments, educational resources, events and opportunities to participate in the initiative.
Follow AIOS as we explore how Artificial Intelligence can help make science more open, collaborative, responsible and accessible.
