ISBN13: | 9781035326013 |
ISBN10: | 1035326019 |
Kötéstípus: | Keménykötés |
Terjedelem: | 214 oldal |
Méret: | 234x156 mm |
Súly: | 666 g |
Nyelv: | angol |
700 |
Operációs rendszerek és grafikus felhasználói felületek
Számítástechnika
Szervezetszociológia
Nevelési módszerek és gyógypedagógia
További könyvek a pedagógia területén
Operációs rendszerek és grafikus felhasználói felületek (karitatív célú kampány)
Számítástechnika (karitatív célú kampány)
Szervezetszociológia (karitatív célú kampány)
Nevelési módszerek és gyógypedagógia (karitatív célú kampány)
További könyvek a pedagógia területén (karitatív célú kampány)
Generative AI in Higher Education
GBP 85.00
Kattintson ide a feliratkozáshoz
Contrasting perspectives are explored, from educator viewpoints to student attitudes on AI usage, presenting a holistic illustration of the many, often polarizing responses to AI in the classroom. Chapters investigate what higher education could stand to gain from the use of generative AI, and which challenges are involved, looking at the broader implications, opportunities, and threats of usage of generative AI in higher education. This timely book further covers implications for teaching methods and insight into the impact of generative AI usage on student learning experiences as well as offering practical guidelines and warnings about uncritical use of generative AI in scientific research.
This thought-provoking book is beneficial to scholars and educators in higher education, particularly those concerned about the rapid development of AI technologies. Students of disciplines such as education theory, ethics, and development will additionally find it useful.
This insightful book provides a much-needed exploration into how the rapid expansion of generative AI over the last few years has impacted higher education. Addressing the good, the bad, and the ugly elements of this technological revolution, editors Kätlin Pulk and Riina Koris bring together an international collective of contributors to answer the question: how can we ensure that reliance on AI in higher education still enables positive, proactive teaching and learning?
?The chapters in Pulk and Koris? edited volume discuss the ?good?, ?bad?, and ?ugly? faces of generative AI in higher education and research. They address the paradoxes of using generative AI, namely that using it well requires background knowledge and skills, the absence of which is a main reason for using it in the first place and which are therefore at risk of not being further developed. Highly recommended.?
1 Introduction: setting the scene 1
Riina Koris and Kätlin Pulk
SETTING THE SCENE CONTINUED
2 What ChatGPT still can?t do (but we might do with it): Hubert Dreyfus and extended-mind cyborgs 16
Wayne Martin and Deidre Williams
3 Identifying discourses of generative AI in higher education 27
Chahna Gonsalves and Oguz A. Acar
THE GOOD
4 Considering the pedagogical benefits of generative artificial intelligence in higher education: applying constructivist learning theory 45
John V. Pavlik
5 Student learning in the age of AI: principles and practices for using AI in higher education 58
Christian Hendriksen
6 Generative AI as an enabler for educators: practical tips for generative AI usage in teaching 72
Katri Kerem
7 Generative AI for academic research 88
Michael Dowling and Yue Li
THE BAD
8 Generative AI as a disrupter of creativity 102
Abdullah H. Clark and Kathleen Denman
9 Assessment renaissance: authentic design in the era of generative AI 114
Peter Matheis and Jacob-John Jubin
10 Strategies and ethical challenges for equality in generative AI research: addressing access, bias and privacy 129
Margriet A. van Gestel
11 Ethical and moral pitfalls of generative AI in academic research 144
Ilia Protopapa and Bochra Idris
THE UGLY
12 Beyond the Friedman doctrine: contextuality, social knowledge, and professional craftsmanship in business education 164
Jukka V. Mäkinen, Jukka I. Mattila, Mika Tammilehto, and Raisa Varsta
13 Generative AI as a challenge to faculty development: ugly advice at the dawn of generative AI 178
Michelle D. Miller