Бюлетин на проекта GenAISA #2: Вътре в новия курс за висше образование на GenAISA
Europe is entering a decisive phase in its digital transformation. As Generative AI becomes embedded across industries, the demand for new AI-driven competences – and new job roles – is rising rapidly. The GenAISA consortium is responding to this need by developing 2 comprehensive Higher Education (HE) and Vocational Education and Training (VET) curricula in Generative AI, designed to equip learners with the knowledge and applied skills required in the evolving labour market. At this newsletter we shortly present the HE curriculum that includes 5 courses, while at the next newsletter we will present the VET curriculum.
This newsletter provides an inside look at the training materials currently being developed – and shows how they directly align with two key emerging professions:
- Бърз инженер
- Generative AI Application Developer
Both profiles have been formally identified and described in the project’s job mapping work package, with detailed competences and responsibilities included in the supporting materials.
Training Materials for Future AI Professionals
The GenAISA HE course is built around five training modules, each shaped to support real job requirements identified by the consortium. Below is an overview of the modules and how they map to the two emerging AI professions.
1. Introduction to Generative AI
This topic introduces students to the foundations of Generative AI, tracing its evolution from early GANs and VAEs to today’s transformer-based and diffusion-based systems. It also covers the ethical, social, and transparency challenges surrounding modern AI (bias, reliability, data provenance, explainability).
Connection to job profiles:
- Prompt Engineers need a solid grounding in model behaviour, limitations, and linguistic patterns to design effective prompts.
- Generative AI Application Developers require understanding of model types to select the right architecture for each task.
2. NLP, Image & Video Generation in Generative AI
This topic focuses on the multimodal capabilities of modern GenAI systems – how they process language, how they generate images or video, and how humans interact with these systems. Students explore practical use cases such as text-to-image tools, conversational assistants, and multimodal reasoning.
Connection to job profiles:
- Prompt Engineers learn how language and visual cues influence model output, supporting skills in prompt optimisation and testing.
- Generative AI Application Developers gain insights into multimodal APIs and learn how to integrate them into applications.
3. Generative AI Models
A more technical module covering GANs, VAEs, Diffusion Models, Latent Diffusion, and hybrid architectures. It draws on state-of-the-art surveys and landmark publications across deep generative modelling, providing students with a structured understanding of how generative systems are implemented and improved.
Connection to job profiles:
- Prompt Engineers benefit by understanding model constraints, bias mechanisms, and failure modes.
- Generative AI Application Developers gain essential knowledge of model operation, fine-tuning, and optimisation workflows.
4. Deep Learning Basics for Generative AI
This topic introduces students to the building blocks of deep learning – from neural architecture components to embeddings and optimisation techniques. It prepares learners to understand or implement AI systems using modern frameworks.
Connection to job profiles:
- Prompt Engineers build the analytical skills needed to evaluate the strengths and weaknesses of model outputs.
- Generative AI Application Developers acquire the technical grounding needed to work with APIs, perform fine-tuning, and integrate models into applications.
5. Management of Generative AI Transformation
Generative AI adoption requires organisational readiness, management skills, governance, and ethical decision-making. This module provides practical methods, process models, and frameworks for responsible AI design and deployment. It also covers AI transformation in business and industry, as well as change leadership. The content is based on recent research and case studies from multiple sectors.
Connection to job profiles:
- Prompt Engineers must understand the basics of AI design processes, compliance, safety, and governance to design responsible prompts.
- Generative AI Application Developers need awareness of AI design and development processes, security and change management practices when integrating models into products.
Connecting Training to Real Job Opportunities
The HE and VET course has been intentionally designed to map onto two fast-growing professions identified by the project’s Job Profiles report:
Бърз инженер
A mid-level AI specialist who designs, tests, and optimises prompts for LLMs and other generative systems.
Essential skills: NLP, prompt optimisation, testing methodologies, critical thinking, awareness of bias and safety.
Typical career paths: AI product teams, EdTech, customer experience automation, legal/medical prompt design.
Generative AI Application Developer
A technical role focused on integrating generative AI models into real applications, ensuring security, performance, and usability.
Essential skills: API integration, model orchestration, backend/frontend development, AI-ready DevOps, compliance-aware programming.
Typical career paths: AI software development, SaaS, corporate R&D, digital health, creative industries.
Гледайки напред
The HE learning materials are now in the refinement stage, integrating real-world examples and case-based assignments developed by the project partners. Once completed, they will be made available as open educational resources through the GenAISA Online Learning Platform, ensuring accessibility across Europe’s academic and training ecosystems.
In the next phase, the consortium will pilot-test these materials in selected higher education institutions, collecting feedback from educators and learners to ensure quality, inclusivity, and real-world relevance.
