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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
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
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
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
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
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
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
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
Linke, Dirk – Biochemistry and Molecular Biology Education, 2009
In this article, the author talks about the auto correction mode of word processors that leads to a number of problems and describes an example in biochemistry exams that shows how word processors can lead to mistakes in databases and in papers. The author contends that, where this system is applied, spell checking should not be left to a word…
Descriptors: Biochemistry, Word Processing, Spelling, Error Correction
Sternberg, Betty J.; Kaplan, Karen A.; Borck, Jennifer E. – Reading Research Quarterly, 2007
Adolescent literacy achievement across the United States is in crisis. More than eight million students in grades 4 to 12 are identified as struggling readers. These students, who perform below grade level in reading and writing, are at high risk for failure in all content subjects and ultimately for dropping out of school. Professionals in the…
Descriptors: Grade 4, Educational Technology, Delivery Systems, Word Processing

Yannakoudakis, E. J.; Fawthrop, D. – Information Processing and Management, 1983
This paper describes an intelligent spelling error correction system for use in a word processing environment. The system employs a dictionary of 93,769 words and, provided the intended word is in the dictionary, it identifies 80 percent to 90 percent of spelling and typing errors. Nine references are cited. (Author/EJS)
Descriptors: Algorithms, Artificial Intelligence, Computer Programs, Dictionaries

Rushinek, Avi; Rushinek, Sara – Office Systems Research Journal, 1984
Describes results of a system rating study in which users responded to WPS (word processing software) questions. Study objectives were data collection and evaluation of variables; statistical quantification of WPS's contribution (along with other variables) to user satisfaction; design of an expert system to evaluate WPS; and database update and…
Descriptors: Artificial Intelligence, Computer Software, Evaluation Methods, Information Retrieval

Pence, Penny; And Others – Research & Teaching in Developmental Education, 1990
Describes five categories of software that can be useful in the basic writing classroom: computer-assisted instruction, computer-controlled instruction, artificial intelligence, computer-based rhetorical invention, and word processing. Evaluates each type of software in terms of their ability to fulfill the goals of basic writing instruction to…
Descriptors: Artificial Intelligence, Basic Writing, Classroom Techniques, Computer Assisted Instruction
Kieras, David E. – 1992
The Computerized Comprehensibility System (CCS) provides an automated copy editing function, generating a mark-up of a draft of a technical document by simulating the simpler comprehension processes of a human reader, and then criticizing the text when these simple processes cannot successfully comprehend the material. A key CCS function is…
Descriptors: Artificial Intelligence, Computer Software Development, Computer System Design, Databases

Atnip, Gilbert W. – Teaching of Psychology, 1985
Described is a college course on the use of computers in psychology that included an introduction to computers, computing, word processing, data analysis, data acquisition, artificial intelligence, computer assisted instruction, simulation, and modeling. Students conducted independent research projects using the computer. (Author/RM)
Descriptors: Artificial Intelligence, Computer Assisted Instruction, Computers, Course Descriptions