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Showing 1 to 15 of 58 results Save | Export
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Touretzky, David; Gardner-McCune, Christina; Seehorn, Deborah – International Journal of Artificial Intelligence in Education, 2023
This article provides an in-depth look at how K-12 students should be introduced to Machine Learning and the knowledge and skills they will develop as a result. We begin with an overview of the AI4K12 Initiative, which is developing national guidelines for teaching AI in K-12, and briefly discuss each of the "Five Big Ideas in AI" that…
Descriptors: Electronic Learning, Artificial Intelligence, Elementary School Students, Secondary School Students
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Bai, Xiaoyu; Stede, Manfred – International Journal of Artificial Intelligence in Education, 2023
Recent years have seen increased interests in applying the latest technological innovations, including artificial intelligence (AI) and machine learning (ML), to the field of education. One of the main areas of interest to researchers is the use of ML to assist teachers in assessing students' work on the one hand and to promote effective…
Descriptors: Artificial Intelligence, Intelligent Tutoring Systems, Natural Language Processing, Evaluation
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José-García, Adán; Sneyd, Alison; Melro, Ana; Ollagnier, Anaïs; Tarling, Georgina; Zhang, Haiyang; Stevenson, Mark; Everson, Richard; Arthur, Rudy – International Journal of Artificial Intelligence in Education, 2023
Artificial Intelligence in Education (AIED) has witnessed significant growth over the last twenty-five years, providing a wide range of technologies to support academic, institutional, and administrative services. More recently, AIED applications have been developed to prepare students for the workforce, providing career guidance services for…
Descriptors: Career Guidance, Student Evaluation, Artificial Intelligence, Networks
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Pelánek, Radek – International Journal of Artificial Intelligence in Education, 2022
Educational technology terminology is messy. The same meaning is often expressed using several terms. More confusingly, some terms are used with several meanings. This state is unfortunate, as it makes both research and development more difficult. Terminology is particularly important in the case of personalization techniques, where the nuances of…
Descriptors: Educational Technology, Semantics, Vocabulary, Misconceptions
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Bogina, Veronika; Hartman, Alan; Kuflik, Tsvi; Shulner-Tal, Avital – International Journal of Artificial Intelligence in Education, 2022
This paper discusses educating stakeholders of algorithmic systems (systems that apply Artificial Intelligence/Machine learning algorithms) in the areas of algorithmic fairness, accountability, transparency and ethics (FATE). We begin by establishing the need for such education and identifying the intended consumers of educational materials on the…
Descriptors: Educational Technology, Computer Software, Artificial Intelligence, Stakeholders
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Baker, Ryan S.; Hawn, Aaron – International Journal of Artificial Intelligence in Education, 2022
In this paper, we review algorithmic bias in education, discussing the causes of that bias and reviewing the empirical literature on the specific ways that algorithmic bias is known to have manifested in education. While other recent work has reviewed mathematical definitions of fairness and expanded algorithmic approaches to reducing bias, our…
Descriptors: Mathematics, Bias, Education, Race
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Yan, Hongxin; Lin, Fuhua; Kinshuk – International Journal of Artificial Intelligence in Education, 2021
Online education is growing because of its benefits and advantages that students enjoy. Educational technologies (e.g., learning analytics, student modelling, and intelligent tutoring systems) bring great potential to online education. Many online courses, particularly in self-paced online learning (SPOL), face some inherent barriers such as…
Descriptors: Learning Analytics, Independent Study, Online Courses, Electronic Learning
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Geden, Michael; Emerson, Andrew; Carpenter, Dan; Rowe, Jonathan; Azevedo, Roger; Lester, James – International Journal of Artificial Intelligence in Education, 2021
Game-based learning environments are designed to provide effective and engaging learning experiences for students. Predictive student models use trace data extracted from students' in-game learning behaviors to unobtrusively generate early assessments of student knowledge and skills, equipping game-based learning environments with the capacity to…
Descriptors: Game Based Learning, Middle School Students, Microbiology, Secondary School Science
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Zapata-Rivera, Diego – International Journal of Artificial Intelligence in Education, 2021
Research in the area of Open Student Models (OSMs) has shown that external representations of the student model can be used to facilitate educational processes such as student reflection, knowledge awareness, learning, collaboration, negotiation, and student model diagnosis. OSMs can be integrated into existing learning systems or become a…
Descriptors: Models, Student Characteristics, Access to Information, Educational Research
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Winne, Philip H. – International Journal of Artificial Intelligence in Education, 2021
Learner modeling systems so far formulated model learning in three main ways: a learner's "position" within a lattice of declarative and procedural knowledge about highly structured disciplines such as geometry or physics, a learner's path through curricular tasks compared to milestones, or profiles of a learner's achievements on a set…
Descriptors: Models, Student Characteristics, Access to Information, Learning Processes
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Lajoie, Susanne P. – International Journal of Artificial Intelligence in Education, 2021
I first met Jim Greer at the NATO Advanced Study Institute on Syntheses of Instructional Sciences and Computing Science for Effective Instructional Computing Systems in 1990 in Calgary, Canada. It was during this meeting that I came to realize that Jim was one of those rare individuals that could help "translate" computer science…
Descriptors: Models, Student Characteristics, Artificial Intelligence, Computer Uses in Education
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Bull, Susan – International Journal of Artificial Intelligence in Education, 2021
For the special issue of the International Journal of Artificial Intelligence in Education dedicated to the memory of Jim Greer, this paper highlights some of Jim's extensive and always-timely contributions to the field: from his early AI-focussed research on intelligent tutoring systems, through a variety of applications deployed to support…
Descriptors: Artificial Intelligence, Intelligent Tutoring Systems, Educational Research, College Students
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Arruarte, Josu; Larrañaga, Mikel; Arruarte, Ana; Elorriaga, Jon A. – International Journal of Artificial Intelligence in Education, 2021
In order to be effective, a learning process requires the use of valid and suitable educational resources. However, measuring the quality of an educational resource is not an easy task for a teacher. The data of the performance of the students can be used to measure how appropriate the didactic resources are. Besides this data, adequate metrics…
Descriptors: Educational Resources, Educational Quality, Learning Analytics, Tests
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Uto, Masaki; Miyazawa, Yoshimitsu; Kato, Yoshihiro; Nakajima, Koji; Kuwata, Hajime – International Journal of Artificial Intelligence in Education, 2020
Teaching writing strategies based on writing processes has attracted wide attention as a method for developing writing skills. The writing process can be generally defined as a sequence of subtasks, such as planning, formulation, and revision. Therefore, instructor feedback is often given based on sequence patterns of those subtasks. For such…
Descriptors: Markov Processes, Writing Processes, Writing Skills, Keyboarding (Data Entry)
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Wu, Wen; Chen, Li; Yang, Qingchang; Li, You – International Journal of Artificial Intelligence in Education, 2019
Communication tools have been popular in web-based learning systems because of their ability to promote the interaction and potentially alleviate the high dropout issue. In recent years, with the increased awareness among researchers about the individual difference of the students, more and more personalized learning supports have been developed.…
Descriptors: Personality, Inferences, Student Behavior, Computer Mediated Communication
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