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Pierre-Alexandre Balland; Olesya Grabova; J. Scott Marcus; Robert Praas; Andrea Renda – European Union, 2025
This report examines the burgeoning generative artificial intelligence (GenAI) and foundation models landscape within the European Union, and analyses its impact, technological advancements, and regulatory implications. It details the GenAI value chain, identifying key players and investment trends, revealing a significant US dominance. The report…
Descriptors: Artificial Intelligence, Man Machine Systems, Natural Language Processing, Industry
Steven Watson; Jonathan Romic – European Educational Research Journal, 2025
This paper presents a novel contribution to the discourse surrounding Large Language Models (LLMs) like ChatGPT in relation to education and society by using systems theory. We argue that ChatGPT can be understood not just as an 'artificial' intelligence but that it is entangled in the evolution of society and therefore education. ChatGPT is a…
Descriptors: Artificial Intelligence, Man Machine Systems, Natural Language Processing, Role
Yusuf Oc; Chahna Gonsalves; La Toya Quamina – Journal of Marketing Education, 2025
The integration of generative artificial intelligence (AI) tools is a paradigm shift in enhanced learning methodologies and assessment techniques. This study explores the adoption of generative AI tools in higher education assessments by examining the perceptions of 353 students through a survey and 17 in-depth interviews. Anchored in the Unified…
Descriptors: Artificial Intelligence, Man Machine Systems, Natural Language Processing, Technology Uses in Education
Jiahui Luo – Assessment & Evaluation in Higher Education, 2024
This study offers a critical examination of university policies developed to address recent challenges presented by generative AI (GenAI) to higher education assessment. Drawing on Bacchi's 'What's the problem represented to be' (WPR) framework, we analysed the GenAI policies of 20 world-leading universities to explore what are considered problems…
Descriptors: Artificial Intelligence, Educational Policy, College Students, Student Evaluation
Maria Dimeli; Apostolos Kostas – Journal of Information Technology Education: Research, 2025
Aim/Purpose: The purpose of this systematic review is to identify and analyze the current findings of empirical research on the use of ChatGPT in school and higher education. Background: As AI reshapes education, the adoption of ChatGPT has the potential to revolutionize teaching and learning in school and higher educational settings. Meanwhile,…
Descriptors: Artificial Intelligence, Technology Uses in Education, Natural Language Processing, Barriers
Katarína Žáková; Diana Urbano; Ricardo Cruz-Correia; José Luis Guzmán; Jakub Matišák – Education and Information Technologies, 2025
Understanding how students interact with AI bots is a first step towards integrating them into instructional design. In this report, the results of a survey conducted in three European higher education institutions, and in the context of four different areas are presented. Among other things, they reveal for what purposes students use ChatGPT,…
Descriptors: Student Attitudes, Teacher Attitudes, Artificial Intelligence, Natural Language Processing
Rumeng Yan; Xin Zhao; Suvodeep Mazumdar – Education for Information, 2023
Chatbots have experienced significant growth over the past decade, with a proliferation of new applications across various domains. Previous studies also demonstrate the trend of new technologies, especially artificial intelligence, being adopted in libraries. The purpose of this study is to determine the current research priorities and findings…
Descriptors: Academic Libraries, Artificial Intelligence, Man Machine Systems, Natural Language Processing
Ilker Cingillioglu – Studies in Higher Education, 2024
This study provides an empirical approach to utilizing an Artificial Intelligence (AI)-based system for identifying students' university choice factors that impact their matriculation decision. We created an AI-based chatbot that gathered both qualitative and quantitative data from nearly 1200 participants worldwide. The entire human-AI…
Descriptors: Admission (School), Decision Making, Student Attitudes, College Choice