Second GenAISA project newsletter: more about the higher education training course

Europe is entering a crucial phase of its digital transformation. As generative artificial intelligence (AI) is becoming increasingly prevalent in all sectors, the demand for new AI-related competences and new professional roles is rapidly increasing. The GenAISA consortium is responding to this need by developing 2 comprehensive curricula for higher education (HE) and vocational education and training (VET) in the field of generative AI, designed to equip learners with the knowledge and applied skills needed in the evolving labour market. In this newsletter, we briefly present the HE curriculum, which includes 5 courses, and in 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 are directly related to two key emerging professions:

• Prompt engineer
• Generative AI application developer

Both profiles are formally identified and described in the project's job mapping work package, with detailed competencies and responsibilities included in the supporting materials.

Training materials for future professionals in the field of artificial intelligence

The GenAISA HE course is built around five training modules, each designed to support real-world job requirements identified by the consortium. Below is an overview of the modules and how they align with the two emerging professions in the field of artificial intelligence.

1. Introduction to Generative AI

This topic introduces students to the fundamentals of generative AI, tracing its evolution from early GANs and VAEs to today's transformer- and diffusion-based systems. It also covers the ethical, social, and transparency challenges associated with modern AI (bias, trustworthiness, data provenance, explainability).

Link to professional profiles:

• The engineers need a solid foundation in model behavior, constraints, and linguistic models to design effective prompts.
• Developers of generative AI applications They need to understand the types of models to choose the right architecture for each task.

2. NLP, image and 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.

Link to professional profiles:

• Prompt engineers learn how language and visual cues influence model results, supporting optimization and prompt testing skills.
• Developers of generative AI applications gain knowledge about 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 research and landmark publications in the field of deep generative modeling, providing students with a structured understanding of how generative systems are implemented and improved.

Link to professional profiles:

• Prompt engineers benefit from understanding model limitations, bias mechanisms, and failure modes.
• Developers of generative AI applications acquire basic knowledge of model operation, fine-tuning, and optimization workflows.

4. Fundamentals of Deep Learning for Generative AI

This topic introduces students to the fundamentals of deep learning, from neural architecture components to embedding and optimization techniques. It prepares students to understand or implement artificial intelligence systems using modern frameworks.

Link to professional profiles:

• Prompt engineers develop the analytical skills needed to evaluate the strengths and weaknesses of model results.
• Developers of generative AI applications acquire the technical fundamentals needed to work with APIs, fine-tune, and integrate models into applications.

5. Managing the transformation of generative AI

Implementing generative AI requires organizational readiness, management skills, governance, and ethical decision-making. This module provides practical methods, process models, and frameworks for responsible AI design and implementation. 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 various sectors.

Link to professional profiles:

• Prompt engineers must understand the fundamentals of AI design processes, compliance, safety, and governance to design responsible prompts.
• Developers of generative AI applications must be familiar with AI design and development processes, as well as security and change management practices when integrating models into products.

Connecting training with real job opportunities

The higher education and vocational training course is specifically designed to match two fast-growing professions identified in the project's occupational profiles report:

Prompt engineer

Mid-level AI specialist who designs, tests, and optimizes prompts for LLM and other generative systems.
 Basic skills: NLP, prompt optimization, testing methodologies, critical thinking, bias awareness and safety.
 Typical career paths: product teams in the fields of artificial intelligence, EdTech, customer experience automation, prompt design in the legal and medical fields.

Generative Artificial Intelligence Application Developer

A technical position focused on integrating generative artificial intelligence models into real-world applications, ensuring security, performance, and usability.
Required skills: API integration, model orchestration, backend/frontend development, AI-ready DevOps, compliance-oriented programming.
Typical career paths: artificial intelligence software development, SaaS, corporate R&D, digital health, creative industries.

A look to the future

The higher education teaching materials are being refined, integrating real-world examples and case-based tasks developed by the project partners. Once completed, they will be made available as open educational resources through the online learning platform GenAISA, thus ensuring accessibility in academic and training ecosystems across Europe.

In the next phase, the consortium will pilot test these materials in selected higher education institutions, collecting feedback from faculty and learners to ensure quality, inclusiveness, and real-world applicability.

Stay with us

Follow GenAISA's journey as we continue to support the European education sector with AI-ready skills and innovative learning solutions.

👉 Visit our website: www.genaisa.eu
👉 Follow us on LinkedIn: https://www.linkedin.com/showcase/genaisa-project/
👉 Follow us on Facebook: https://www.facebook.com/genaisa.project/