Humboldt-Academy
AI Skills for Media and Publishing Professionals
Description
This badge certifies demonstrated competence in applying artificial intelligence (AI) safely, critically and productively in editorial, publishing and product-related work processes.
Learning Outcomes
Participants are able to:
- select suitable AI tools for different tasks in the media and publishing context and use them purposefully,
- critically assess the benefits, quality and limitations of AI applications,
- use AI to increase efficiency and creativity in professional work processes,
- take legal, ethical and regulatory requirements into account when using AI,
- analyse work processes, identify potential for the use of AI and optimise workflows in a targeted manner.
Workload
150 hours / 5 ECTS credits
Award Criteria
This badge is awarded upon successful completion of the certified continuing education programme and demonstration of the described learning outcomes through an ungraded portfolio assessment.
Issuing Institutions
Humboldt-Universität zu Berlin & Axel Springer Academy
Lecturers
- Prof. Dr. Niels Pinkwart
Photo: Julia Baier Niels Pinkwart has been head of the Chair of "Computer Science Education / Computer Science and Society" at Humboldt-Universität zu Berlin since 2013. His research interests lie at the intersection of digitalisation, education and societal/organisational change. In addition to his work at HU Berlin, Prof. Pinkwart is Scientific Director of the Educational Technology Lab at DFKI Berlin. Prof. Pinkwart has an established academic record with more than 300 publications and numerous research projects funded by, among others, the DFG, BMBF, BMAS and various foundations. He is a member of the programme committees of several academic conferences, currently serves as Associate Editor of the International Journal of Artificial Intelligence in Education and is a member of the Editorial Board of the Journal of Educational Data Mining.
Contribution to the programme:
As academic director, Niels Pinkwart is responsible for quality assurance and the assessment of the practical projects. In the session "AI Fundamentals and Prompting", he explains how artificial intelligence works, introduces structured prompting and presents first applications for the media context.https://www.informatik.hu-berlin.de/de/forschung/gebiete/cses/members/niels.pinkwart/niels-pinkwart
- Prof. Dr. Alan Akbik
After completing his doctorate in computer science in 2016, Alan Akbik worked at IBM and Zalando before moving to Humboldt-Universität zu Berlin in 2020 to take over the Chair of Machine Learning. He and his team develop methods that enable machines to understand and use natural language. A key focus is on resource-efficient methods for developing and training large language models. Alan Akbik and his team also work on automated knowledge extraction from large volumes of text. All research results are made available as free software, public datasets and documentation.
Contribution to the programme:
In the sessions "Data and Machine Learning" and "Generative AI", Alan Akbik teaches the core fundamentals of modern AI technologies. He explains how language models work, how they are trained and how they can be used, and highlights their relevance for applications in the media and publishing context. He combines theoretical concepts with practical demonstrations and discusses the limitations and ethical implications of generative systems.https://www.informatik.hu-berlin.de/de/forschung/gebiete/ml/welcome
- M.Sc. Patrick Haller
Patrick Haller is a computer scientist and doctoral candidate at the Chair of Machine Learning led by Prof. Dr. Alan Akbik. After completing his master's degree in Applied Computer Science at the University of Bamberg in 2022, he joined Humboldt-Universität zu Berlin. His research focuses on efficient and data-efficient language models. He investigates how such models can be trained with limited data and computing resources and specifically adapted to different requirements and deployment conditions. Another focus of his work is the systematic evaluation of the performance and reliability of these models, particularly under resource-constrained conditions.
Contribution to the programme:
In the session "Agentic AI", Patrick Haller provides an introduction to developing AI-powered agents and presents the platforms n8n and Copilot Studio. Both make it possible to connect AI with workflows, tools, data sources and external applications, thereby enabling automated processes. The focus is on how AI agents can be integrated into existing work processes in a targeted way in order to automate recurring tasks and make workflows more efficient and scalable.- Prof. Dr. Herbert Zech
Photo: Wilhelm Böttcher Herbert Zech has held the Chair of Civil Law, Technology and IT Law at Humboldt-Universität zu Berlin since 2019 and is a Director at the Weizenbaum Institute for the Networked Society. After his habilitation in Bayreuth, he worked at the University of Basel from 2012 to 2019, initially as Associate Professor of Private Law (with a focus on Life Sciences Law) and from 2015 as Full Professor of Life Sciences Law and Intellectual Property Law. After passing his bar examination, Herbert Zech also studied biology. This is the source of his interest in the interface between the natural sciences and law. His research focuses on technology law, intellectual property law and the law of digitalisation.
Contribution to the programme:
In the session "Law and Ethics", Herbert Zech presents the key legal frameworks for the use of artificial intelligence, including the EU AI Act and copyright issues. Using concrete use cases from the media sector, he shows which legal and ethical aspects are relevant when using AI-generated content.
Curriculum
- Module 1: Introduction to Using AI in the Media Context – Fundamentals, Application, Responsibility
Participants have the skills to work with AI applications while taking legal frameworks into account. They understand the basic operating principles of AI systems and can formulate structured prompts. In practice-oriented use cases in the media context, participants are able to take legal and ethical aspects into account.
- Module 2: Practical Use of AI Tools
Participants have basic skills in using Copilot in the media context and implement concrete workflows for editorial work, analysis and communication. They can compare key AI tools and assess their potential uses and limitations. Participants are familiar with basic approaches to agent-based systems and are able to identify potential for process automation and efficiency gains in the publishing context.
- Module 3: AI: Applications, Innovations and Networks
Participants can classify AI-powered assistants as well as generative and multimodal AI systems in a business context. Based on exchanges with a variety of stakeholders, they are able to gain an overview of current AI applications, potential uses, fundamental research topics and technological developments, and to evaluate them at a basic level.
- Module 4: Change and Transfer – Practice-Oriented Project Development
Participants are able to describe and analyse AI applications in the media context using the AI Use Case Canvas. They master the systematic documentation, critical reflection, presentation and discussion of their learning and work results, as well as their transfer to their respective work context.