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Daniel Wildcat – Tribal College Journal of American Indian Higher Education, 2024
The following is offered as only one Indigenous person's perspective--a Yuchi Muscogee tribal member's perspective. Several questions about AI emerge when we consider it through an (not the) Indigenous lens. Where is AI's heart and where is its spirit? Does this complex, fast source of intelligence have feelings or emotions? Does machine-generated…
Descriptors: Artificial Intelligence, Indigenous Populations, Cultural Context, Sustainability
Meredith King – Research Issues in Contemporary Education, 2024
This position paper introduces the idea of cognicy, the foundational ability to think and understand in a process that decouples cognitive processes from their tangible outcomes. Generative artificial intelligence (AI) can produce output often nearly indistinguishable from a human product, which presents a problem for educational assessment.…
Descriptors: Cognitive Processes, Artificial Intelligence, Metacognition, Individual Characteristics
Cui, Zhongmin – Educational Measurement: Issues and Practice, 2021
Commonly used machine learning applications seem to relate to big data. This article provides a gentle review of machine learning and shows why machine learning can be applied to small data too. An example of applying machine learning to screen irregularity reports is presented. In the example, the support vector machine and multinomial naïve…
Descriptors: Artificial Intelligence, Man Machine Systems, Data, Bayesian Statistics
Demuth, Katherine; Johnson, Mark – First Language, 2020
Exemplar-based learning requires: (1) a segmentation procedure for identifying the units of past experiences that a present experience can be compared to, and (2) a similarity function for comparing these past experiences to the present experience. This article argues that for a learner to learn a language these two mechanisms will require…
Descriptors: Comparative Analysis, Language Acquisition, Linguistic Theory, Grammar
McClelland, James L. – First Language, 2020
Humans are sensitive to the properties of individual items, and exemplar models are useful for capturing this sensitivity. I am a proponent of an extension of exemplar-based architectures that I briefly describe. However, exemplar models are very shallow architectures in which it is necessary to stipulate a set of primitive elements that make up…
Descriptors: Models, Language Processing, Artificial Intelligence, Language Usage
MacKenzie D. Sidwell; Landon W. Bonner; Kayla Bates-Brantley; Shengtian Wu – Intervention in School and Clinic, 2024
Oral reading fluency probes are essential for reading assessment, intervention, and progress monitoring. Due to the limited options for choosing oral reading fluency probes, it is important to utilize all available resources such as generative artificial intelligence (AI) like ChatGPT to create oral reading fluency probes. The purpose of this…
Descriptors: Artificial Intelligence, Natural Language Processing, Technology Uses in Education, Oral Reading
Goodman, Bradley; Linton, Frank; Gaimari, Robert – International Journal of Artificial Intelligence in Education, 2016
Our 1998 paper "Encouraging Student Reflection and Articulation using a Learning Companion" (Goodman et al. 1998) was a stepping stone in the progression of learning companions for intelligent tutoring systems (ITS). A simulated learning companion, acting as a peer in an intelligent tutoring environment ensures the availability of a…
Descriptors: Simulation, Cooperative Learning, Educational Benefits, Artificial Intelligence
Knabe, Melina L.; Vlach, Haley A. – First Language, 2020
Ambridge argues that there is widespread agreement among child language researchers that learners store linguistic abstractions. In this commentary the authors first argue that this assumption is incorrect; anti-representationalist/exemplar views are pervasive in theories of child language. Next, the authors outline what has been learned from this…
Descriptors: Child Language, Children, Language Acquisition, Models
Aldridge, David – Educational Theory, 2018
This article by David Aldridge concerns the promise of knowledge "insertion." The promise can be elucidated as follows: knowledge could be inserted by a less time-consuming (and possibly in many senses less expensive) technological process than traditional learning, whereby, for example, some relatively swift procedure of implanting or…
Descriptors: Technology Uses in Education, Brain, Epistemology, Learning Processes
Seeger, Matthew W. – Communication Education, 2018
The six essays published in this issue all seek to extend principles of communication and instruction. They share an instrumental view of communication as a way to address problems and deficiencies and simultaneously position instructional communication research squarely within larger organizational and professional communication contexts. In…
Descriptors: Interpersonal Communication, Communication Strategies, Research, Teaching Methods
Devedzic, Vladan – International Journal of Artificial Intelligence in Education, 2016
If you ask me "Will Semantic Web 'ever' happen, in general, and specifically in education?", the best answer I can give you is "I don't know," but I know that today we are still far away from the hopes that I had when I wrote my paper "Education and The Semantic Web" (Devedzic 2004) more than 10 years ago. Much of the…
Descriptors: Web 2.0 Technologies, Semantics, Web Based Instruction, Visual Aids
Ritzer, George; Jandric, Petar; Hayes, Sarah – Open Review of Educational Research, 2018
In the year that George Ritzer publishes the ninth edition of "The McDonaldization of Society," moving his famous theory firmly "Into the Digital Age," critical educator Petar Jandric and sociologist Sarah Hayes invited George to a dialogue on the digital transformation of McDonaldization and its critical application to Higher…
Descriptors: Higher Education, Social Systems, Global Approach, Consumer Economics
Schneider, W. Joel; Kaufman, Alan S. – International Journal of School & Educational Psychology, 2016
As documented in this special issue, all over the world hard choices must be made in education, government, business, and medicine. Intelligence tests, used intelligently and with appropriate ethical safeguards, are one tool of many that help make hard choices work out well, or at least better than the next-best alternative (Kaufman, Raiford,…
Descriptors: Intelligence Quotient, Artificial Intelligence, Children, Adolescents
Frude, Neil; Jandric, Petar – E-Learning and Digital Media, 2015
This conversation focuses on a book published in 1983 that examined "animism," the tendency to regard non-living entities as living and sentient. "The Intimate Machine" suggested that animism will be fully exploited by artificial intelligence (AI) and robotics, generating artefacts that will engage the user in…
Descriptors: Artificial Intelligence, Robotics, Books, Interpersonal Relationship
Nokelainen, Petri; Silander, Tomi – Frontline Learning Research, 2014
This commentary to the recent article by Musso et al. (2013) discusses issues related to model fitting, comparison of classification accuracy of generative and discriminative models, and two (or more) cultures of data modeling. We start by questioning the extremely high classification accuracy with an empirical data from a complex domain. There is…
Descriptors: Models, Classification, Accuracy, Regression (Statistics)