The Good, the Bad and the Limitations of AI in Education
Date Thursday, Nov 24 Time – Room Charlottenburg I
This panel recognises the potentials and pitfalls of using AI and machine learning to improve teaching and learning, but also shows its limitations and where the human brain wins the battle. Whether you curse AI or believe it’s a blessing, this session is for you!
Lecturer, University of Naples Parthenope, Italy
Antonio Maratea has a Master's Degree in Statistics and a PhD in Information Engineering. Since 2008 he works at University of Naples "Parthenope", teaching mainly Databases and Data Science at the graduate and post graduate level. His recent research focus is on Rough-Fuzzy methods, Machine Learning and XAI.
Head of Department, Federal Institute for Vocational Education and Training
Michael Heister was born in Bonn in 1961. After his studies of economic sciences and business administration he did receive his doctor´s degree in the field of economics and social policy at the University of Cologne.
From 1992 to 2009 he did join the Federal Ministry of Labor and Social Policy as a member of staff in the fields of basic policy issues of social policy strategies, advisory services, bilateral co-operation with Eastern European Countries and for human resource management. In February 2003 Michael Heister became Head of Department „EQUAL, XENOS and transnational political action”. He took over responsibility for those political actions of the Ministry which were funded by the European Social Fund (ESF).
Since December 2009 he is head of department „Initiatives for Vocational Education and Trainihg “ in the Federal Institute for Vocational Education and Training (BIBB), Bonn. His department covers topics like intercompany vocational training centres, sustainable development and AI in the field of VET. He is member of the Working Group 2 “Future of Work and Humane-Machine Interaction” of Germanys Platform for Artifical Intelligence”
Since 2010 Michael Heister is Honorary Professor at the University Bonn-Rhein-Sieg teaching human resource management.
Sara de Freitas
BT Professor, Director of the Digital Futures Institute and DigiTech Centre, University of Suffolk, UK, Wey Education Plc, United Kingdom
Sara is a leading international educator in online and game-based education. She is the Executive Director of Education at the award-winning Wey Education PLC. Sara has held senior roles in research and education in 6 universities in the UK and Australia, as Director of Research, Pro and Deputy Vice-Chancellor. She has held professorships for 15 years, leading 58 Research and development projects. As an author and speaker, she has published seven books and 100 scientific papers. Sara is a Non-Executive Board member for Sunderland University, and has received honors and awards from the Royal Society, University of London and the Business Excellence Institute.
The Elastic Mind: Challenging AI, Antonio Maratea
This presentation will walk you through the current limitations of AI technologies, the specificities of the human brain and the skills needed in the future not to replace, but to exploit computers at their best.
With today's computational power and the stunning performances of Artificial Intelligence (AI) and Machine Learning (ML) algorithms, the human brain is being challenged and its uniqueness continuously eroded.
While algorithms are able to answer queries in natural language, drive cars or translate quite accurately from one language to another, it has become clear that no human being can compete with computers and AI to perform large scale specific tasks.
As a consequence, factual knowledge has come to an end: facts have become quickly accessible by ubiquitous technological devices, and the traditional effort to "learn" as an attempt to memorise specific facts or actions of a certain domain has quickly and hopelessly become obsolete.
Still, the human brain retains its primacy in the ability to transfer knowledge from a domain to another, with minimum effort and very few examples (incredibly few, if compared with ML algorithms). This means that for future generations "learning" should concentrate on Transfer Learning (TL), i.e. abstraction, adaptability and domain swap.
Educated people should be able to quickly collect all factual data they need, in the moment they need them, to rank and comprehend them, to evaluate their trustworthiness, and to finally transfer the general principles they studied previously in that domain, so to reach the best decision. Continuous learning should be built on knowledge that lasts, especially when facts change.
- See the learning process from the point of view of a computer scientist.
- Discover the specificities of the human brain, hard to simulate even for a powerful computer.
- Look differently at factual knowledge.
AI – Curse Or Blessing for Learners?, Michael Heister
This presentation will invite participants to position themselves on ethical issues about AI. It will focus on the perspective of the learner, especially in the area of inclusion.
In the field of learning, AI very often meets ardent admirers or complete rejection. This does not do justice to reality. Especially when it comes to questions of inclusion for people with disabilities, it becomes clear that differentiated views are needed. For visually impaired people for example, AI-supported assistance systems can be used to offer concrete help.
AI can also have a supportive effect on learners, as it can be used for learning analytics that can support individual learning.
On the other hand, the dangers should not be underestimated, as there is a risk of paternalism and surveillance of learners. And we have a duty to inform learners about risks and opportunities.
- Understand the importance of information about AI.
- Assess the opportunities and risks of artificial intelligence to support inclusion.
- Accept the high importance of ethical issues in the field of AI.