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Xuelin Liu; Hua Zhang; Yue Cheng – International Journal of Web-Based Learning and Teaching Technologies, 2024
In this article, a dialogue text feature extraction model based on big data and machine learning is constructed, which transforms the high-dimensional space of text features into the low-dimensional space that is easy to process, so that the best feature words can be selected to represent the document set. Tests show that in most cases, the…
Descriptors: Artificial Intelligence, Data, Text Structure, Classification
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Anna Koufakou – Education and Information Technologies, 2024
Student opinions for a course are important to educators and administrators, regardless of the type of the course or the institution. Reading and manually analyzing open-ended feedback becomes infeasible for massive volumes of comments at institution level or online forums. In this paper, we collected and pre-processed a large number of course…
Descriptors: Learning, Opinions, Student Attitudes, Natural Language Processing
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Rianne Conijn; Emily Dux Speltz; Evgeny Chukharev-Hudilainen – Reading and Writing: An Interdisciplinary Journal, 2024
Revision plays an important role in writing, and as revisions break down the linearity of the writing process, they are crucial in describing writing process dynamics. Keystroke logging and analysis have been used to identify revisions made during writing. Previous approaches include the manual annotation of revisions, building nonlinear…
Descriptors: Automation, Revision (Written Composition), Word Processing, Computers
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Mike Perkins; Jasper Roe; Binh H. Vu; Darius Postma; Don Hickerson; James McGaughran; Huy Q. Khuat – International Journal of Educational Technology in Higher Education, 2024
This study investigates the efficacy of six major Generative AI (GenAI) text detectors when confronted with machine-generated content modified to evade detection (n = 805). We compare these detectors to assess their reliability in identifying AI-generated text in educational settings, where they are increasingly used to address academic integrity…
Descriptors: Artificial Intelligence, Inclusion, Computer Software, Word Processing
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Abdullahi Yusuf; Nasrin Pervin; Marcos Román-González – International Journal of Educational Technology in Higher Education, 2024
In recent years, higher education (HE) globally has witnessed extensive adoption of technology, particularly in teaching and research. The emergence of generative Artificial Intelligence (GenAI) further accelerates this trend. However, the increasing sophistication of GenAI tools has raised concerns about their potential to automate teaching and…
Descriptors: Artificial Intelligence, Educational Trends, Futures (of Society), Higher Education
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Alec Thomson – Community College Enterprise, 2024
Artificial intelligence tools have presented many challenges and opportunities to transform teaching and learning on college campuses. These changes are significant enough to require colleges to take action to create a framework by which faculty and students can navigate the proper usage of these tools. Rather than working to create entirely new…
Descriptors: Artificial Intelligence, Information Technology, Position Papers, Educational Policy
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Kay M. Hammond; Patricia Lucas; Amira Hassouna; Stephen Brown – Journal of University Teaching and Learning Practice, 2023
Research on academic integrity used to focus more on student character and behaviour. Now this research includes wider viewing of this issue as a current teaching and learning challenge which requires pedagogical intervention. It is now the responsibility of staff and institutions to treat the creation of a learning environment supporting academic…
Descriptors: Criticism, Automation, Word Processing, Technology Uses in Education
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Huiyu Zhang; Linda Fang – Educational Media International, 2023
In Temasek Polytechnic, Singapore, several AI chatbots acting as digital teaching assistants were trialed between July 2021 and August 2022. As these were developed for different purposes, it is important to learn if these AI chatbots help achieve the desired learning outcomes. This paper focuses on lessons learnt from three chatbots designed for…
Descriptors: Foreign Countries, Artificial Intelligence, Asynchronous Communication, Teaching Assistants