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Tsung-Ying Chen – Journal of Psycholinguistic Research, 2024
Artificial grammar learning (AGL) is an experimental paradigm frequently adopted to investigate the unconscious and conscious learning and application of linguistic knowledge. This paper will introduce ENIGMA (https://enigma-lang.org) as a free, flexible, and lightweight Web-based tool for running online AGL experiments. The application is…
Descriptors: Artificial Intelligence, Grammar, Computer Software, Handheld Devices
Aaron Haim; Eamon Worden; Neil T. Heffernan – Grantee Submission, 2024
Since GPT-4's release it has shown novel abilities in a variety of domains. This paper explores the use of LLM-generated explanations as on-demand assistance for problems within the ASSISTments platform. In particular, we are studying whether GPT-generated explanations are better than nothing on problems that have no supports and whether…
Descriptors: Artificial Intelligence, Learning Management Systems, Computer Software, Intelligent Tutoring Systems
Ayse Alkan – International Journal of Technology in Education and Science, 2024
Artificial intelligence (AI) based education represents a significant transformation in the field of education of our age. Artificial intelligence (AI) technology has great potential to enrich the learning experience of special needs students, provide support to teachers, and reduce inequalities in education. Artificial intelligence (AI)…
Descriptors: Artificial Intelligence, Educational Technology, Technology Uses in Education, Students with Disabilities
Andres Neyem; Luis A. Gonzalez; Marcelo Mendoza; Juan Pablo Sandoval Alcocer; Leonardo Centellas; Carlos Paredes – IEEE Transactions on Learning Technologies, 2024
Software assistants have significantly impacted software development for both practitioners and students, particularly in capstone projects. The effectiveness of these tools varies based on their knowledge sources; assistants with localized domain-specific knowledge may have limitations, while tools, such as ChatGPT, using broad datasets, might…
Descriptors: Computer Software, Artificial Intelligence, Intelligent Tutoring Systems, Capstone Experiences
Husni Almoubayyed; Stephen E. Fancsali; Steve Ritter – Grantee Submission, 2023
Adaptive educational software is likely to better support broader and more diverse sets of learners by considering more comprehensive views (or models) of such learners. For example, recent work proposed making inferences about "non-math" factors like reading comprehension while students used adaptive software for mathematics to better…
Descriptors: Reading Ability, Computer Software, Mathematics Education, Intelligent Tutoring Systems
S. Sageengrana; S. Selvakumar; S. Srinivasan – Interactive Learning Environments, 2024
Students are termed "multitaskers," and it is likely that they easily fall prey to other subjects or topics that most interest them. They occasionally took heed or gave close and thoughtful attention to the lectures they were on. In the current educational system, our young generations receive materials from their leftovers, and their…
Descriptors: Electronic Learning, Dropouts, Student Behavior, Student Interests
Vincent Aleven; Jori Blankestijn; LuEttaMae Lawrence; Tomohiro Nagashima; Niels Taatgen – Grantee Submission, 2022
Past research has yielded ample knowledge regarding the design of analytics-based tools for teachers and has found beneficial effects of several tools on teaching and learning. Yet there is relatively little knowledge regarding the design of tools that support teachers when a class of students uses AI-based tutoring software for self-paced…
Descriptors: Educational Technology, Artificial Intelligence, Problem Solving, Intelligent Tutoring Systems
Elif Polat; Yunus Emre Bastug; Sinan Hopcan; Simge Cepdibi Sibiç; Nazli Ceren Isikligil – Journal of Learning and Teaching in Digital Age, 2024
This study investigates the primary school teachers' perceptions towards the Intelligent Material System (IMS), which was developed to enhance the quality of inclusive environments and support the academic skills of students with Special Educational Needs (SEN). Within this scope, IMS training was conducted with 84 classroom teachers who worked at…
Descriptors: Teacher Attitudes, Elementary School Teachers, Inclusion, Special Education
Xizhe Wang; Yihua Zhong; Changqin Huang; Xiaodi Huang – IEEE Transactions on Learning Technologies, 2024
Reading comprehension is a widely adopted method for learning English, involving reading articles and answering related questions. However, the reading comprehension training typically focuses on the skill level required for a standardized learning stage, without considering the impact of individual differences in linguistic competence. This…
Descriptors: Reading Comprehension, Artificial Intelligence, Computer Software, Synchronous Communication
Westera, Wim; Prada, Rui; Mascarenhas, Samuel; Santos, Pedro A.; Dias, João; Guimarães, Manuel; Georgiadis, Konstantinos; Nyamsuren, Enkhbold; Bahreini, Kiavash; Yumak, Zerrin; Christyowidiasmoro, Chris; Dascalu, Mihai; Gutu-Robu, Gabriel; Ruseti, Stefan – Education and Information Technologies, 2020
This article provides a comprehensive overview of artificial intelligence (AI) for serious games. Reporting about the work of a European flagship project on serious game technologies, it presents a set of advanced game AI components that enable pedagogical affordances and that can be easily reused across a wide diversity of game engines and game…
Descriptors: Artificial Intelligence, Educational Games, Educational Technology, Computer Software
Chen-Chung Liu; Wan-Jun Chen; Fang-ying Lo; Chia-Hui Chang; Hung-Ming Lin – Journal of Educational Computing Research, 2024
Reading requires appropriate strategies to spark initial interest and sustain engagement. One promising strategy is the pedagogical approach of learning-by-teaching, transforming learners into active participants. Integrating this approach into digitalized and individualized reading contexts has the potential to foster the development of young…
Descriptors: Reading Interests, Active Learning, Intelligent Tutoring Systems, Artificial Intelligence
Yuan, Chia-Ching; Li, Cheng-Hsuan; Peng, Chin-Cheng – Interactive Learning Environments, 2023
Fighter jets are a critical national asset. Because of the high cost of their manufacture and that of their related equipment, both pilots and maintenance personnel must complete intensive training before coming into contact with a jet. Due to gradual military downsizing, one-on-one training is often impracticable, and the level of familiarization…
Descriptors: Artificial Intelligence, Man Machine Systems, Technology Uses in Education, Educational Technology
Lippert, Anne; Gatewood, Jessica; Cai, Zhiqiang; Graesser, Arthur C. – Grantee Submission, 2019
One out of six adults in the United States possesses low literacy skills. Many advocates believe that technology can pave the way for these adults to gain the skills that they desire. This article describes an adaptive intelligent tutoring system called AutoTutor that is designed to teach adults comprehension strategies across different levels of…
Descriptors: Intelligent Tutoring Systems, Educational Technology, Adult Literacy, Skill Development
Weber, Gerhard; Brusilovsky, Peter – International Journal of Artificial Intelligence in Education, 2016
This paper present provides a broader view on ELM-ART, one of the first Web-based Intelligent Educational systems that offered a creative combination of two different paradigms--Intelligent Tutoring and Adaptive Hypermedia technologies. The unique dual nature of ELM-ART contributed to its long life and research impact and was a result of…
Descriptors: Intelligent Tutoring Systems, Educational Technology, Technology Uses in Education, Hypermedia
Steven Moore; John Stamper; Norman Bier; Mary Jean Blink – Grantee Submission, 2020
In this paper we show how we can utilize human-guided machine learning techniques coupled with a learning science practitioner interface (DataShop) to identify potential improvements to existing educational technology. Specifically, we provide an interface for the classification of underlying Knowledge Components (KCs) to better model student…
Descriptors: Learning Analytics, Educational Improvement, Classification, Learning Processes