2.0 Introduction

✍️ Authors: Lille Bernstein (MMKH, Head of Data Protection & Data Protection Officer), Jens O. Brelle (MMKH, Copyright) and Katrin Schröder (MMKH, Communications)
⏱ You will need around 90 minutes for this chapter

 

The use of Artificial Intelligence (AI) in higher education raises a wide range of legal, data protection and ethical issues. Whilst AI systems such as ChatGPT, Copilot or HAWKI enable new forms of collaboration, automation and creativity, they also touch upon fundamental legal frameworks – ranging from data protection and copyright to examination law. At the same time, universities face the challenge of establishing ethical guidelines to ensure transparency, fairness and accountability in the use of these technologies.

In this chapter, you will gain a practical and nuanced overview of key normative issues in the higher education context. You will learn how to use AI in a legally sound, data protection-compliant and ethically responsible manner. This ties in with four key learning objectives:

✅ Acting in accordance with copyright law: You will understand that AI-generated content does not generally enjoy copyright protection – although the underlying training data may well be protected. Furthermore, you will be able to describe how higher education institutions should deal with licensing issues, labelling requirements and contractual arrangements.

✅ Observe data protection: You will receive a practical introduction to the fundamentals of data protection law when using AI systems. You will be able to explain the essential principles of the General Data Protection Regulation (GDPR) – such as data minimisation, purpose limitation and transparency – and assess when, for example, a data protection impact assessment is necessary.

✅ Apply examination law: You will be able to describe the legal requirements applicable to the use of AI in examinations – particularly with regard to transparency, traceability, independent achievement and the protection of personal data. You will also be able to describe the requirements for high-risk AI systems under the AI Act (from 2026).

✅ Ethical reflection: You can explain why ethical principles such as autonomy, fairness, transparency, sustainability and accountability play a central role in the design of AI-supported processes in a higher education context. You can identify measures through which higher education institutions can embed these principles institutionally – e.g. via guidelines, training or ethics committees.

The responsible use of AI in higher education means reconciling technical possibilities with legal obligations and ethical values. This is particularly important where AI handles sensitive information, such as in application processes, during examinations or in communication with students. Higher education institutions bear a dual responsibility here: they not only protect rights and data, but also shape the ethical stance of the next generation of professionals in their dealings with AI.

 

📖 This chapter comprises the following sections

2.1 Copyright

AI-generated content, rights to training data and legally compliant use

2.2 Data protection

Law-compliant use of AI within the framework of the GDPR

2.3 Examination law

AI in examinations – what is permitted, what becomes mandatory

2.4 Ethical and social principles and reflection

Value-oriented use of AI at higher education institutions

At the end of the course overview, you will find ChatGPT 4o to try out! Please observe data protection guidelines and do not enter any personal information.