Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
AI didactics in language teaching: Staff professional development • AI-resilient tasks • Bias & hallucination
0
Zitationen
19
Autoren
2026
Jahr
Abstract
This Handbook is the result of a week-long Blended Intensive Programme, convened by the Language Centre at European University Cyprus to examine how language teaching can respond to the arrival of generative artificial intelligence (GenAI). From the beginning, the aim was to create a practical resource for university language centres, which are facing an unprecedented challenge to established ways of teaching and assessing. Throughout the week, delegates delivered presentations and carried out workshop activities designed to foreground the issues and explore potential solutions. We then made use of GenAI tools (specifically ChatGPT 5.2, Gamma.app and DALL-E) to expand these suggestions into a fuller resource intended for use at and beyond university language centres. While some of the text in this handbook was AI generated, the ideas and frameworks are the authors’ own.We do not claim to have all the answers, but we hope these resources will be useful in starting conversations and supporting decision-making under real working conditions. We are particularly aware that the nature of language teaching work—often involving a high number of part-time collaborators—can make sustained shared practice and coordinated professional development difficult. Colleagues may have uneven access to training, different levels of confidence, and limited time to experiment, reflect, and redesign materials. We have tried to keep these constraints in view throughout, and we welcome suggestions for amendments to future editions.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.773 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.682 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 8.242 Zit.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
2019 · 6.898 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.
Autoren
- James Mackay
- Charis Xinari
- Claudia Model Moyseos
- Helle Dam Jensen
- Michael Ennis
- Dietmar Unterkofler
- Azevedo Brito Maria Jose
- Pedro Miguel Moreira
- Daniela Junker
- Franziska Klung
- Sabrina Forment
- Valérie Bouchardon
- Julie Valade
- Paula Wieczorek
- Nikolaos Monokrousos
- Ezinne-Sheridan Anyanwu
- Kyriaki-Persefoni Konstantinidou
- Tzanou Konstantina-Michaela
- Georgia Varouxi
Institutionen
- European University Cyprus(CY)
- Aarhus University(DK)
- Free University of Bozen-Bolzano(IT)
- Polytechnic Institute of Viana do Castelo(PT)
- University of Coimbra(PT)
- Technical University of Munich(DE)
- Technische Universität Ilmenau(DE)
- Université de Lille(FR)
- Université de Technologie de Compiègne(FR)
- University of Information Technology and Management in Rzeszow(PL)
- International Hellenic University(GR)