Why the focus group matters

The rapid uptake of generative AI is transforming how audio-visual content is created and shared. Alongside new creative opportunities, the sector is facing concrete risks that increasingly affect education, vocational training, and professional practice. The German focus group confirmed three challenges that require urgent and practical responses:

• Limited deepfake awareness and detection skills among educators and creative professionals, increasing vulnerability to manipulated media, fraud, and reputational harm.

• Insufficient applied understanding of ethical and legal aspects of AI use—especially copyright, consent, authorship, and accountability in AI-supported production.

• Erosion of trust in digital and creative media, making transparency and verification skills essential across the industry and education ecosystems.

Expert dialogue moderated by Prof. Dr. Petyo Budakov

The session was moderated by Prof. Dr. Petyo Budakov and brought together a group of experts representing key perspectives at the intersection of AI and the audio-visual sector, including education, media studies, ethics, and related fields.

The discussion was structured as a focused, practice-oriented exchange designed to capture both strategic priorities and concrete training needs. Participants explored how deepfakes and generative AI are already influencing day-to-day work and learning environments, and what competences and tools are most needed to respond effectively.

What we learned: insights that will shape the training programme

The German experts provided targeted input that will directly inform the development of the project’s core outputs. The session validated the relevance of the proposed module structure and highlighted where practical emphasis is needed.

Key insights included:

1) Competence needs must be framed as applied skills, not abstract concepts
Participants emphasized that AI literacy must be actionable—combining critical thinking, media analysis, and professional judgement. Detection is not only a technical matter; it is also about decision-making under uncertainty, documentation of checks, and communicating verification outcomes responsibly.

2) Detection training must integrate tools, methods, and workflow thinking
Experts highlighted the need to train educators and learners on how to combine multiple signals—context, metadata, artefacts, cross-checking sources, and verification routines—rather than relying on a single tool. The strongest learning outcomes come from repeatable workflows that mirror real professional practice.

3) Ethical and legal questions must be translated into everyday production scenarios
There was strong demand for clear guidance on consent, copyright, authorship, disclosure, and accountability—anchored in realistic case studies. Educators and professionals need examples they can immediately use in classrooms, workshops, and creative teams.

4) Trust and transparency require both standards and communication
Participants underlined that restoring trust is not only about identifying deepfakes; it also depends on transparent practices such as disclosure, provenance thinking, and responsible communication when content cannot be fully verified.

How the findings will be used

The outcomes of the German focus group will feed directly into the project’s development work in four concrete ways:

• Refining the structure and content of the training programme modules on deepfakes and ethical AI in the audio-visual sector

• Defining priority competences for VET and higher education contexts, including critical media analysis, ethical reasoning, and responsible AI use

• Collecting practical examples and cases that can be converted into hands-on learning materials (scenarios, exercises, and verification tasks)

• Supporting cross-country relevance, by enabling comparison with the upcoming focus groups in Luxembourg and Bulgaria to ensure the training resources remain adaptable across European contexts

Next steps

With the German focus group successfully completed, the partnership continues Activity 1 with focus group interviews in Luxembourg and Bulgaria. The combined results will be consolidated into a coherent evidence base that supports the co-design of:

• the project training curriculum,

• practical guidelines and case studies, and

• validation criteria for responsible AI use in creative production.

The project’s approach is intentionally collaborative: by co-creating with experts, educators, and practitioners, DeepTech for Creative Trust is building training resources that are credible, relevant, and ready for use in real learning and professional environments.

If you are interested in contributing expertise or staying informed about the project’s training development, follow the project updates on the DeepTech website and partner channels.

Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or Anefore asbl. Neither the European Union nor the granting authority can be held responsible for them.
Pr. Nr: 2025-2-LU01-KA210-VET-000371932