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Laura K. Allen; Arthur C. Grasser; Danielle S. McNamara – Grantee Submission, 2023
Assessments of natural language can provide vast information about individuals' thoughts and cognitive process, but they often rely on time-intensive human scoring, deterring researchers from collecting these sources of data. Natural language processing (NLP) gives researchers the opportunity to implement automated textual analyses across a…
Descriptors: Psychological Studies, Natural Language Processing, Automation, Research Methodology
Miguel Ángel Escotet – Prospects, 2024
Artificial Intelligence is a fast-evolving technology with enormous potential for education, higher education, and learning. AI can also negatively impact how societies and their citizens engage ethically with these generated, still-unexplored tools. These technological breakthroughs present both opportunity and potential peril. The problem of any…
Descriptors: Futures (of Society), Artificial Intelligence, Technology Uses in Education, Higher Education
Renu Balyan; Tracy Arner; Tong Li; Ellen Orcutt; Reese Butterfuss; Panayiota Kendeou; Danielle McNamara – Grantee Submission, 2022
Speech technology (automated speech recognition -- ASR and text-to-speech) offers great promise in the field of automated literacy and reading tutors for children. Students in third and fourth grades struggle with generating longer strings of text on a QWERTY keyboard because they still "hunt and peck" for AQ1 the letters and symbols…
Descriptors: Assistive Technology, Technology Integration, Intelligent Tutoring Systems, Automation
Cai, Zhiqiang; Hu, Xiangen; Graesser, Arthur C. – Grantee Submission, 2019
Conversational Intelligent Tutoring Systems (ITSs) are expensive to develop. While simple online courseware could be easily authored by teachers, the authoring of conversational ITSs usually involves a team of experts with different expertise, including domain experts, linguists, instruction designers, programmers, artists, computer scientists,…
Descriptors: Programming, Intelligent Tutoring Systems, Courseware, Educational Technology
Scandura, Joseph M. – Technology, Instruction, Cognition and Learning, 2018
This paper summarizes key stages in development of the Structural Learning Theory (SLT) and explains how and why it is now possible to model human tutors in a highly efficient manner. The paper focuses on evolution of the SLT, a deterministic theory of teaching and learning, on which AuthorIT authoring and TutorIT delivery systems have been built.…
Descriptors: Artificial Intelligence, Models, Tutors, Learning Theories
Murphy, Robert F. – RAND Corporation, 2019
Recent applications of artificial intelligence (AI) have been successful in performing complex tasks in health care, financial markets, manufacturing, and transportation logistics, but the influence of AI applications in the education sphere has been limited. However, that may be changing. In this paper, the author discusses several ways that AI…
Descriptors: Elementary Secondary Education, Artificial Intelligence, Teaching Methods, Educational Technology
Office of Educational Technology, US Department of Education, 2023
The U.S. Department of Education (Department) is committed to supporting the use of technology to improve teaching and learning and to support innovation throughout educational systems. This report addresses the clear need for sharing knowledge and developing policies for "Artificial Intelligence," a rapidly advancing class of…
Descriptors: Artificial Intelligence, Educational Technology, Technology Uses in Education, Educational Policy
Sottilare, Robert A. – Technology, Instruction, Cognition and Learning, 2018
This article is intended as a companion document to the more focused report provided by the author at the 2017 American Education Research Association (AERA) Conference as part of the Technology, Instruction, Cognition & Learning Special Interest Group's Symposium on Intelligent Tutoring Systems (ITSs). Both the AERA talk and this article…
Descriptors: Literature Reviews, Goal Orientation, Integrated Learning Systems, Instructional Design
Scott A. Crossley; Danielle S. McNamara – Grantee Submission, 2016
The purpose of this handbook is to provide actionable information to educators, administrators, and researchers about current, available research-based educational technologies that provide adaptive (personalized) instruction to students on literacy, including reading comprehension and writing. This handbook is comprised of chapters by leading…
Descriptors: Educational Technology, Literacy, Reading Comprehension, Writing Skills
Scandura, Joseph M. – Online Submission, 2010
According to Wikipedia "Automation is a step beyond mechanism." Whereas mechanization provided human operators with machinery to assist them with the muscular requirements of work, automation greatly reduces the need for human sensory and mental requirements as well. In this context, Artificial Intelligence (AI) was founded on the claim that a…
Descriptors: Learning Theories, Mathematics Skills, Automation, Computer Uses in Education
Nugent, Rebecca; Ayers, Elizabeth; Dean, Nema – International Working Group on Educational Data Mining, 2009
In educational research, a fundamental goal is identifying which skills students have mastered, which skills they have not, and which skills they are in the process of mastering. As the number of examinees, items, and skills increases, the estimation of even simple cognitive diagnosis models becomes difficult. We adopt a faster, simpler approach:…
Descriptors: Data Analysis, Students, Skills, Cluster Grouping
Rus, Vasile; Lintean, Mihai; Azevedo, Roger – International Working Group on Educational Data Mining, 2009
This paper presents several methods to automatically detecting students' mental models in MetaTutor, an intelligent tutoring system that teaches students self-regulatory processes during learning of complex science topics. In particular, we focus on detecting students' mental models based on student-generated paragraphs during prior knowledge…
Descriptors: Data Analysis, Prior Learning, Cognitive Structures, College Students
Peer reviewedMerrill, M. David – Instructional Science, 1998
Describes ID Expert, an intelligent computer-based multimedia interactive instructional development and delivery system. Topics include instructional transactions with built-in instructional design; knowledge bases and knowledge representation; automated instructional design; and similarities to GTE (Generic Tutoring Environment). (Author/LRW)
Descriptors: Automation, Instructional Design, Intelligent Tutoring Systems, Knowledge Representation
Ben-Naim, Dror; Bain, Michael; Marcus, Nadine – International Working Group on Educational Data Mining, 2009
It has been recognized that in order to drive Intelligent Tutoring Systems (ITSs) into mainstream use by the teaching community, it is essential to support teachers through the entire ITS process: Design, Development, Deployment, Reflection and Adaptation. Although research has been done on supporting teachers through design to deployment of ITSs,…
Descriptors: Foreign Countries, Intelligent Tutoring Systems, Computer System Design, Computer Managed Instruction
Peer reviewedElen, Jan – Instructional Science, 1998
Discusses GTE (Generic Tutoring Environment) and courseware engineering and argues that GTE's theoretical knowledge base focuses on teaching as a good model for any kind of instruction and thus reduces its generic nature. Two examples of weak automation for instructional design are described that have broader knowledge bases. (Author/LRW)
Descriptors: Automation, Computer Software Development, Courseware, Instructional Design

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