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Full Product Details

Author:   Maurizio Gotti ,  David Hirsh
Publisher:   Peter Lang AG, Internationaler Verlag der Wissenschaften
Imprint:   Peter Lang AG, Internationaler Verlag der Wissenschaften
Edition:   New edition
Volume:   318
Weight:   0.374kg
ISBN:  

9783034361132

ISBN 10:   3034361130
Pages:   188
Publication Date:   08 October 2025
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Manufactured on demand  
We will order this item for you from a manufactured on demand supplier.

Table of Contents

List of Tables - List of Figures - 1. Introduction - 1.1. The GenAI Era - 1.2. GenAI in Higher Education - 1.3. Vocabulary Profiling - 1.4. Vocabulary Lists - 1.5. Anticipations of the Future - 2. GenAI in Higher Education - 2.1. Artificial Intelligence - 2.2. Generative Artificial Intelligence - 2.2.1. Machine Learning - 2.2.2. Natural Language Processing - 2.2.3 Large Language Models - 2.2.4. Datasets - 2.2.5. Algorithms - 2.3. GenAI Tools in Higher Education - 3. Vocabulary Profiling - 3.1. Lexical Nature of Academic Writing - 3.1.1. High Frequency Words - 3.1.2. Academic Words - 3.1.3. Technical Words - 3.1.4. Proper Nouns - 3.1.5. Low Frequency Words - 3.2. K1, K2 and AWL Word Lists - 3.3. BNC-COCA Word Lists - 4. The Current Study - 4.1. Selection of GenAI Tools - 4.2. Selection of Prompts for GenAI Text Generation - 4.3. Analysis of Texts - 5. Analysis 1: K1, K2 and AWL Words - 5.1. Arts Texts - 5.1.1. Non-GenAI - 5.1.2. GenAI Scite - 5.1.3. GenAI Jenni - 5.1.4. GenAI Yomu - 5.1.5. Comparison of Arts Texts - 5.2. Commerce Texts - 5.2.1. Non-GenAI -5.2.2. GenAI Scite - 5.2.3. GenAI Jenni - 5.2.4. GenAI Yomu - 5.2.5. Comparison of Commerce Texts - 5.3. Law Texts - 5.3.1. Non-GenAI - 5.3.2. Non-GenAI Scite - 5.3.3. Non-GenAI Jenni - 5.3.4. Non-GenAI Yomu - 5.3.5. Comparison of Law Texts - 5.4. Science Texts - 5.4.1. Non-GenAI - 5.4.2. Non-GenAI Scite - 5.4.3. Non-GenAI Jenni - 5.4.4. Non-GenAI Yomu - 5.4.5. Comparison of Science Texts - 6. Analysis 2: K1 to K10 Words - 6.1. Arts Texts - 6.2. Commerce Texts - 6.3. Law Texts - 6.4. Science Texts - 7. Analysis 3: 20 Most Frequently Occurring Content Words - 7.1. Arts Texts - 7.2. Commerce Texts -7.3. Law Texts - 7.4. Science Texts - 8. Analysis 4: Low Frequency Word Use - 8.1. Arts Texts - 8.2. Commerce Texts - 8.3. Law Texts - 8.4. Science Texts - 9. Conclusion - 9.1. Use of GSL K1, GSL K2 and AWL Words - 9.2. Use of BNC-COCA K1 to K10 Words - 9.3. 20 Most Frequently Occurring Content Words - 9.4. Use of Low Frequency Words - 9.5. Final Comments - References - Appendices - Index

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Author Information

David Hirsh is Associate Professor at the University of Sydney. His research explores vocabulary use, bilingual education and language revitalisation.

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