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Lexical Profile of AI-Generated Content in Higher Education

by David Hirsh (Author)
©2025 Monographs XII, 188 Pages
Series: Linguistic Insights, Volume 318

Summary

This book investigates vocabulary use in texts generated by Artificial Intelligence. The growing use of AI tools such as ChatGPT and DeepSeek in University settings indicates the importance of evaluating the products of such technology as potential reading material. In the study reported, twelve AI-generated samples of writing covering arts, commerce, law and science are analysed for vocabulary use, and compared with four research articles published in journals on the same topics. The vocabulary profiling covers high-frequency words, academic and technical vocabulary, most frequently used content words and low-frequency words. Potential future developments in AI are explored.

Table Of Contents

  • Cover
  • Title Page
  • Copyright Page
  • Contents
  • List of Figures
  • List of Tables
  • 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 GenAI Scite
  • 5.3.3 GenAI Jenni
  • 5.3.4 GenAI Yomu
  • 5.3.5 Comparison of Law Texts
  • 5.4 Science Texts
  • 5.4.1 Non-GenAI
  • 5.4.2 GenAI Scite
  • 5.4.3 GenAI Jenni
  • 5.4.4 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
  • Appendix 1. Arts Text GenAI Scite
  • Bourdieu, Social Capital and Online Interaction
  • Appendix 2. Arts Text GenAI Jenni
  • Bourdieu, Social Capital and Online Interaction
  • Appendix 3. Arts Text GenAI Yomu
  • Bourdieu, Social Capital and Online Interaction
  • Introduction to Bourdieu’s Social Capital
  • Online Interaction as a Form of Network
  • Impact on Social Structures
  • The Role of Social Media in Cultural Capital
  • Conclusion: Implications of Bourdieu’s Theory in the Digital Era
  • Appendix 4. Commerce Text GenAI Scite
  • Paid parental leave and children’s schooling outcomes
  • Appendix 5. Commerce Text GenAI Jenni
  • Paid parental leave and children’s schooling outcomes
  • Appendix 6. Commerce Text GenAI Yomu
  • Paid parental leave and children’s schooling outcomes
  • Introduction to Paid Parental Leave
  • Early Developmental Benefits of Paid Parental Leave
  • Long-term Effects on Educational Attainment
  • Challenges in Measuring the Impact
  • Conclusion: Embracing Comprehensive Parental Leave Policies
  • Appendix 7. Law Text GenAI Scite
  • Anti-Social Behaviour, Behavioural Expectations and an Urban Aesthetic
  • Appendix 8. Law Text GenAI Jenni
  • Anti-Social Behaviour, Behavioural Expectations and an Urban Aesthetic
  • Appendix 9. Law Text GenAI Yomu
  • Anti-Social Behaviour, Behavioural Expectations and an Urban Aesthetic
  • Introduction to Anti-Social Behavior and Urban Aesthetics
  • The Impact of Anti-Social Behavior on Urban Areas
  • The Role of Behavioral Norms in Shaping Urban Spaces
  • Conclusion: Balancing Aesthetics and Inclusivity in Urban Management
  • Appendix 10. Science Text GenAI Scite
  • Risk of yellow fever virus transmission in the Asia-Pacific region
  • Appendix 11. Science Text GenAI: Jenni
  • Risk of yellow fever virus transmission in the Asia-Pacific region
  • Appendix 12. Science Text GenAI Yomu
  • Risk of yellow fever virus transmission in the Asia-Pacific region
  • Introduction to Yellow Fever Virus
  • Transmission Prerequisites
  • Environmental and Anthropogenic Influences
  • Potential Control Measures
  • Conclusion
  • Appendix 13. Headwords of the GSL KI List
  • Appendix 14. Headwords of the GSL K2 List
  • Appendix 15. Headwords of the Academic Word List
  • Index

David Hirsh

Lexical Profile of AI-Generated
Content in Higher Education

Lausanne · Berlin · Bruxelles · Chennai · New York · Oxford

The Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbibliografie; detailed bibliographic data is available online at http://dnb.d-nb.de.

A CIP catalog record for this book has been applied for at the Library of Congress.

LCCN number: 2025032717

ISSN 1424-8689 ISBN 978-3-0343-6113-2 (Print)

ISBN 978-3-0343-6121-7 (E-PDF) ISBN 978-3-0343-6122-4 (E-PUB)

DOI 10.3726/b23152

Published by Peter Lang Group AG, Lausanne, Switzerland

Any utilization outside the strict limits of the copyright law, without the permission of the publisher, is forbidden and liable to prosecution.

This applies in particular to reproductions, translations, microfilming, and storage and processing in electronic retrieval systems.

Contents

List of Figures

List of Tables

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 GenAI Scite

5.3.3 GenAI Jenni

5.3.4 GenAI Yomu

5.3.5 Comparison of Law Texts

5.4 Science Texts

5.4.1 Non-GenAI

5.4.2 GenAI Scite

5.4.3 GenAI Jenni

Details

Pages
XII, 188
Publication Year
2025
ISBN (PDF)
9783034361217
ISBN (ePUB)
9783034361224
ISBN (Hardcover)
9783034361132
DOI
10.3726/b23152
Language
English
Publication date
2025 (October)
Keywords
Higher Education Vocabulary Generative AI AI
Published
Lausanne, Berlin, Bruxelles, Chennai, New York, Oxford, 2025. xii, 188 pp., 13 fig. b/w, 66 tables.
Product Safety
Peter Lang Group AG

Biographical notes

David Hirsh (Author)

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

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Title: Lexical Profile of AI-Generated Content in Higher Education