How to use the AIOLIA course
ModulesHow to use the AIOLIA course
What is this about?
Why is this important?
What will I accomplish by taking this course?
By taking this course, you will develop knowledge and competences in AI ethics tailored to your background, role, and learning needs. You will learn about the fundamental concepts of AI ethics, the requirements of Trustworthy AI, and the AIOLIA AI Ethics Operationalisation Workflow, which provides a structured approach to addressing ethical challenges associated with AI systems.
Depending on your selected learning pathway, you will explore how to identify AI ethics issues, understand relevant ethical requirements, and consider appropriate approaches to addressing ethical challenges in different contexts, including research, AI development, policy and governance, education, and everyday use of AI systems.
This introductory module will help you identify the learning pathway most relevant to your needs, understand which modules and activities you should complete, and navigate the course effectively to achieve your learning objectives.The AIOLIA course is organised into six learning pathways, each designed for a specific target group. To make the most of the course, follow the steps below.
1. Select your learning pathway
Choose the learning pathway that best matches your professional role, background and learning needs. The pathways are designed for Ethics Reviewers, Policy Makers, Social science/legal researchers, Computer science/AI researchers, Ethics Educators and Teachers, and General Public.
2. Identify the modules recommended for you
Each learning pathway includes a specific selection of modules and activities. Follow the recommendations provided for your pathway, as not every learner needs to complete all the modules. Some modules introduce foundational concepts, while others focus on identifying ethical issues and applying the AIOLIA AI Ethics Operationalisation Workflow.
3. Work through the learning materials
Open the recommended modules and study the presentations, explanatory materials and interactive activities. Complete the quizzes, exercises and reflection activities provided to check your understanding and apply what you have learned.
4. Learn at your own pace
You can work through the materials at your own pace, revisiting sections whenever you need to review a concept or complete an activity again. Follow the suggested sequence for your pathway to build your understanding progressively.
5. If you are an educator or teacher
Use the Participant tab to study the learning materials and complete the activities. The Trainer tab provides guidance on how to use and adapt the materials for your own teaching or training activities.
Remember: You do not need to complete every module. Start by identifying your learning pathway and follow the recommendations for your target group.
The course in short
The AIOLIA course consists of six main modules covering the foundations of AI ethics, the AI Ethics Operationalisation Workflow, the identification of ethics issues, the selection of practical measures, the identification of ethics tensions, and the Ethics Check.
Module A is divided into four submodules, while Module C consists of two submodules.
- Module A: Foundations of AI Ethics
- Module A.1 – AI System Definition: Understand the definition of an AI system according to the EU AI Act.
- Module A.2 – Core AI Concepts: Learn about core AI concepts.
- Module A.3 – Ethics-by-design Concept: Understand the concept of ethics-by-design.
- Module A.4 – HLEG Guidelines for Trustworthy AI: Learn about the European Commission's Ethics Guidelines for Trustworthy AI.
- Module B: AI Ethics Operationalisation Workflow: Learn about and explore the AIOLIA AI Ethics Operationalisation Workflow.
- Module C: Ethics Issues Identification
- Module C.1 – Introduction to ALTAI Requirements: Learn about the ALTAI requirements for Trustworthy AI.
- Module C.2 – Ethics Issues Identification: Identify ethics issues associated with AI systems.
- Module D: Selection of Practical Measures: Explore practical measures for addressing identified ethics issues.
- Module E: Ethics Tensions Identification: Identify tensions between different ethics requirements.
- Module F: Ethics Check: Learn about the Ethics Check.
Overall Learning Pathway
The overall AIOLIA learning pathway consists of 10 steps, mapped to the corresponding modules and submodules. However, not all steps apply to every learning pathway, as the content is tailored to each target group's prior knowledge, expertise and learning objectives.
Learning Pathway for Ethics Reviewers
The Ethics Reviewers learning pathway is designed for ethics reviewers, Ethics Advisors, ethics board members and research ethics committee members involved in the assessment and governance of AI systems and AI-enabled research. It aims to develop advanced competences in ethics assessment, risk evaluation, governance oversight and the validation of ethics documentation. Participants are expected to be familiar with AI ethics principles, the ALTAI requirements, the EU AI Act and research ethics procedures. No prior experience with the AIOLIA framework or tools is required.
Learning Pathway Structure
The pathway progresses from understanding AI systems, core AI concepts and ethics-by-design to applying the AIOLIA AI Ethics Operationalisation Workflow, identifying ethics issues, selecting practical measures, analysing ethics tensions and conducting an Ethics Check. As participants are expected to be familiar with the EU HLEG Guidelines for Trustworthy AI and the ALTAI Framework, Steps 4 and 6 are omitted from this learning pathway, and Submodules A.4 and C.1 are therefore not included. The pathway combines theoretical materials with practical activities, including mock-review exercises, H5P knowledge checks and reflective scenario-based activities, to support structured and evidence-based ethics reviews.
Learning Pathway for Policy Makers
The Policy Makers learning pathway is designed for policymakers, regulatory officers, science policy professionals, AI standardisation bodies, National Contact Points and professionals involved in AI governance initiatives. It aims to develop competences in translating AI ethics principles into regulatory, institutional and strategic governance practices, with a focus on accountability, compliance and evidence-based decision-making. Participants are expected to have a good understanding of public policy processes and institutional governance structures. Prior familiarity with AI ethics principles and regulatory frameworks, such as the EU AI Act and GDPR, is beneficial but not required.
Learning Pathway Structure
The pathway progresses from understanding AI systems, core AI concepts, ethics-by-design and the EU Guidelines for Trustworthy AI to understanding the AIOLIA AI Ethics Operationalisation Workflow and identifying ethics issues using the ALTAI requirements. Steps 8, 9 and 10 are not included, and Modules D and E are therefore excluded, as they focus on the practical selection of measures and identification of ethics tensions. Module F is also excluded, as independent ethics checks fall outside the scope of policy and governance roles, although it may be consulted as an optional resource. The pathway combines theoretical materials with mock-review exercises, H5P knowledge checks and reflective scenario-based activities, emphasising illustrative examples and outcomes to support policy development and governance decision-making.
Learning Pathway for Social science/legal researchers
The Social Science and Legal Researchers learning pathway is designed for social scientists, cognitive scientists, legal researchers, ethics researchers and other researchers working on AI systems and their societal, ethical or legal implications. It aims to develop competences in the ethical assessment of AI systems, the analysis of societal and fundamental rights impacts, and the interpretation of AI-related risks in interdisciplinary research contexts. Participants are expected to have a working understanding of academic research methods and disciplinary research practices. Familiarity with AI ethics principles and regulatory frameworks, such as the EU AI Act and GDPR, is beneficial but not required.
Learning Pathway Structure
The pathway progresses from understanding AI systems, core AI concepts, ethics-by-design and the EU Guidelines for Trustworthy AI to applying the AIOLIA AI Ethics Operationalisation Workflow, identifying ethics issues and analysing ethics tensions. Steps 8 and 10 are not included, and Modules D and F are therefore excluded, as selecting practical measures and conducting formal Ethics Checks fall outside the scope of this pathway. Module D may be consulted as an optional resource when relevant to specific research needs. The pathway combines theoretical materials with mock-review exercises, H5P knowledge checks and reflective scenario-based activities to support structured ethical analysis and interdisciplinary impact assessment.Slider_Learning Pathway for Social science/legal researchers
Learning Pathway for Computer science/AI researchers
The Computer Science/AI Researchers learning pathway is designed for AI researchers, software and robotics engineers, data scientists, industrial engineers, and master’s or PhD students in computer science, AI and related engineering disciplines. It aims to develop competences in integrating ethics-by-design principles throughout the AI system development lifecycle, identifying and mitigating ethical and societal risks, and implementing accountable development practices to support trustworthy and responsible AI. Participants are expected to have a solid technical foundation in computer science, artificial intelligence or a related field. Prior expertise in AI ethics, governance or regulatory frameworks, such as the EU AI Act and GDPR, is not required, as these topics are introduced progressively throughout the pathway.
Learning Pathway Structure
The pathway progresses from understanding AI systems and ethics-by-design to exploring the EU Guidelines for Trustworthy AI, applying the AIOLIA AI Ethics Operationalisation Workflow, identifying ethics issues, selecting practical measures and analysing ethics tensions. Step 2 is omitted, and Submodule A.2 is therefore excluded, as participants are expected to have the necessary AI literacy and technical background. Step 10 is also omitted, and Module F is not included, as conducting an independent Ethics Check falls outside the scope of technical development roles, although Module F may be consulted as an optional resource by participants working at the interface between AI development and ethics appraisal. The pathway combines theoretical materials with ethics-by-design exercises, H5P knowledge checks and reflective scenario-based activities to support the integration of ethical considerations and safeguards into AI system design and development.
Learning Pathway for Ethics Educators/Teachers
The Ethics Educators and Teachers learning pathway is designed for professionals involved in the design, delivery and assessment of AI ethics education across formal and non-formal learning contexts, including educators, teachers, research ethics trainers, and members of research ethics and integrity organisations and networks. It aims to develop competences in translating AI ethics principles and regulatory frameworks into pedagogical practice, including curriculum design, the facilitation of ethical reasoning and the assessment of learner competences. Participants are expected to have a working understanding of educational practice and curriculum design within their respective contexts. Prior familiarity with AI ethics principles or European regulatory frameworks is beneficial but not required, and no prior experience with the AIOLIA framework or tools is necessary.
Learning Pathway Structure
The pathway progresses from understanding AI systems and core AI concepts to exploring ethics-by-design, the EU Guidelines for Trustworthy AI, the AIOLIA AI Ethics Operationalisation Workflow and the ALTAI requirements, before moving on to identifying ethics issues. Steps 8, 9 and 10 are omitted, as selecting practical measures, identifying ethics tensions and conducting formal Ethics Checks fall outside the scope of this pathway. Accordingly, Modules D and F are not included, as they focus on technical and operational safeguard implementation and independent ethics review and validation, respectively. The pathway combines theoretical materials with H5P knowledge checks, reflective scenario-based activities and ethical dilemmas. Educators and teachers are expected to consult the Trainer’s tab, which provides guidance to support the use and adaptation of the course materials for teaching, curriculum development and learner assessment.
Learning Pathway for General Public
The General Public learning pathway is designed for citizens with no specific professional background in AI, digital technologies or regulatory frameworks. It aims to build AI literacy, awareness of individual rights and basic critical reasoning skills, supporting informed and responsible engagement with AI-enabled products, services and decision-making systems encountered in everyday life. No prior knowledge of AI, AI ethics or European regulatory frameworks is required, and no previous experience with the AIOLIA framework or tools is assumed. Concepts are introduced progressively through accessible language, everyday examples and practical scenarios, prioritising clarity and practical relevance over technical depth.
Learning Pathway Structure
The pathway progresses from understanding AI systems and core AI concepts to exploring ethics-by-design, the EU Guidelines for Trustworthy AI, the AIOLIA AI Ethics Operationalisation Workflow and the ALTAI requirements. Steps 7, 8, 9 and 10 are omitted, as identifying ethics issues in depth, selecting practical measures, analysing ethics tensions and conducting formal Ethics Checks involve advanced assessment activities beyond the scope of a non-specialist audience. Accordingly, Submodule C.2 and Modules D, E and F are not included. The pathway combines foundational theoretical materials with H5P knowledge-check activities to support awareness of AI systems, their societal implications and the rights and protections available to individuals when using AI-enabled technologies.
