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Jon-Philippe K. Hyatt; Elisa Jayne Bienenstock; Carla M. Firetto; Elizabeth R. Woods; Robert C. Comus – Advances in Physiology Education, 2025
Generative artificial intelligence (AI) large language models have become sufficiently accessible and user-friendly to assist students with course work, studying tactics, and written communication. AI-generated writing is almost indistinguishable from human-derived work. Instructors must rely on intuition/experience and, recently, assistance from…
Descriptors: Artificial Intelligence, Technology Uses in Education, STEM Education, Writing Skills
Tian Song; Hang Zhang; Yijia Xiao – IEEE Transactions on Learning Technologies, 2024
High-quality programming projects for education are critically required in teaching. However, it is hard to develop those projects efficiently and artificially constrained by the lecturers' experience and background. The recent popularity of large language models (LLMs) has led to a great number of applications in the field of education, but…
Descriptors: Artificial Intelligence, Education, Intellectual Disciplines, Undergraduate Students
William Orwig; Emma R. Edenbaum; Joshua D. Greene; Daniel L. Schacter – Journal of Creative Behavior, 2024
Recent developments in computerized scoring via semantic distance have provided automated assessments of verbal creativity. Here, we extend past work, applying computational linguistic approaches to characterize salient features of creative text. We hypothesize that, in addition to semantic diversity, the degree to which a story includes…
Descriptors: Computer Assisted Testing, Scoring, Creativity, Computational Linguistics
Celeste Combrinck – Discover Education, 2024
The current article used real data to demonstrate the analysis and synthesis of Mixed Methods Research (MMR) data with generative Artificial Intelligence (Gen AI). I explore how reliable and valid Gen AI data outputs are and how to improve their use. The current content is geared towards enhancing methodological application regardless of field or…
Descriptors: Foreign Countries, College Freshmen, Engineering Education, Artificial Intelligence
Bahar Memarian; Tenzin Doleck – Education and Information Technologies, 2024
There is a need to conceptualize a multidimensional taxonomy for learner-AI interaction. This conceptual/perspective article shares recent work on AI learner education and further presents new conceptions for a multidimensional taxonomy for learner-AI interaction. A review of the literature is conducted (N = 11). Open coding is used to summarize…
Descriptors: Journal Articles, Artificial Intelligence, Taxonomy, Man Machine Systems
Mustafa Yildiz; Hasan Kagan Keskin; Saadin Oyucu; Douglas K. Hartman; Murat Temur; Mücahit Aydogmus – Reading & Writing Quarterly, 2025
This study examined whether an artificial intelligence-based automatic speech recognition system can accurately assess students' reading fluency and reading level. Participants were 120 fourth-grade students attending public schools in Türkiye. Students read a grade-level text out loud while their voice was recorded. Two experts and the artificial…
Descriptors: Artificial Intelligence, Reading Fluency, Human Factors Engineering, Grade 4
Graham B. Slater – Review of Education, Pedagogy & Cultural Studies, 2024
Accelerating digitization, algorithmic computation, artificial intelligence, and machine learning, along with the increasing automation of work, communication, and everyday life, are central to critical studies of technology and political economy, as well as to public discourse concerning technology's role in creating futures. Ongoing…
Descriptors: Algorithms, Anxiety, Artificial Intelligence, Man Machine Systems
Jae Q. J. Liu; Kelvin T. K. Hui; Fadi Al Zoubi; Zing Z. X. Zhou; Dino Samartzis; Curtis C. H. Yu; Jeremy R. Chang; Arnold Y. L. Wong – International Journal for Educational Integrity, 2024
The application of artificial intelligence (AI) in academic writing has raised concerns regarding accuracy, ethics, and scientific rigour. Some AI content detectors may not accurately identify AI-generated texts, especially those that have undergone paraphrasing. Therefore, there is a pressing need for efficacious approaches or guidelines to…
Descriptors: Artificial Intelligence, Investigations, Identification, Human Factors Engineering
Kathleen J. Kennedy; Jill M. Castek – Contemporary Issues in Technology and Teacher Education (CITE Journal), 2025
This study examined how discourses surrounding Generative artificial intelligence (GenAI) tools in K-12 English language arts (ELA) classrooms construct relationships between teachers, students, and digital platforms. Drawing upon critical literacy theory and teaching as a sociocultural practice, researchers analyzed qualitative data from 10…
Descriptors: Artificial Intelligence, Technology Uses in Education, Teacher Empowerment, Elementary Secondary Education
Joshua Osondu; Emmanuel Jean Francois; Jesse Strycker – Journal of Global Education and Research, 2024
This paper offers a literature synthesis on the role of artificial intelligence (AI) as a strategic policy instrument in tackling the challenges of Teaching and Learning (TL) within the Ghanaian educational context. By examining the current state and prospects of AI in education (AIEd), specifically in Ghana, this study highlights how AI can…
Descriptors: Artificial Intelligence, Educational Policy, Foreign Countries, Barriers
New England Journal of Higher Education, 2018
New England Board of Higher Education's Commission on Higher Education & Employability has thought hard over the past year about the increasing role of artificial intelligence and robotics in the future of life and work. Many others are also waking up to this landscape, which not so long ago seemed like science fiction. Machines have changed…
Descriptors: Artificial Intelligence, Robotics, Decision Making, Moral Values
Heslep, Robert D. – Studies in Philosophy and Education, 2012
The computer engineers who refer to the education of computers do not have a definite idea of education and do not bother to justify the fuzzy ones to which they allude. Hence, they logically cannot specify the features a computer must have in order to be educable. This paper puts forth a non-standard, but not arbitrary, concept of education that…
Descriptors: Artificial Intelligence, Computers, Computer Software, Human Factors Engineering

Technology Teacher, 2004
Fuzzy logic programs for computers make them more human. Computers can then think through messy situations and make smart decisions. It makes computers able to control things the way people do. Fuzzy logic has been used to control subway trains, elevators, washing machines, microwave ovens, and cars. Pretty much all the human has to do is push one…
Descriptors: Robotics, Human Factors Engineering, Artificial Intelligence, Mathematical Concepts
Poirot, James L.; Norris, Cathleen A. – Computing Teacher, 1987
This first in a projected series of five articles discusses artificial intelligence and its impact on education. Highlights include the history of artificial intelligence and the impact of microcomputers; learning processes; human factors and interfaces; computer assisted instruction and intelligent tutoring systems; logic programing; and expert…
Descriptors: Artificial Intelligence, Computer Assisted Instruction, Educational Trends, Human Factors Engineering

Smith, Philip J.; And Others – Information Services and Use, 1987
Discusses the impact of research in artificial intelligence and human computer interaction on the design of bibliographic information retrieval systems, and presents design principles of a prototype system that uses semantically based searches and a knowledge base consisting of conceptual frames. (10 references) (CLB)
Descriptors: Artificial Intelligence, Cognitive Style, Human Factors Engineering, Man Machine Systems
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