TY  - GEN
U1  - Sonstiges
A1  - Fraenkel-Haeberle, Cristina
T1  - AI in Universities between data bias and development of new skills
N2  - The contribution explores the various implications of artificial intelligence (AI) in higher edu-cation. It begins by defining AI, highlighting its evolution and the challenges in establishing a universally accepted definition. The presentation emphasizes the critical relationship be-tween the quality of training data and AI performance, noting that biases in data can lead to skewed outcomes, particularly regarding sensitive societal issues.
The discussion extends to legal and ethical considerations, particularly around data protec-tion laws such as the GDPR, which pose challenges for AI applications like ChatGPT in educa-tional settings. The contribution also examines the dual role of AI as both a tool for enhan-cing learning and a potential facilitator of academic dishonesty, raising questions about the integrity of examination processes.
In conclusion the presentation argues for a balanced approach that encourages the respon-sible use of AI in universities, advocating for clear regulations that define permissible uses while fostering critical engagement with these technologies among students. The conclusion posits that rather than outright bans, educational institutions should focus on integrating AI into curricula to prepare students for a future where such technologies are ubiquitous.
KW  - Artificial
KW  - Data Bias
KW  - New Skills
KW  - Artificial Intelligence
KW  - Data Bias
KW  - New skills
Y1  - 2024
ER  -