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Download full textOffice 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
Peer reviewedZorislav Šojat; Gordana Gredicak Šojat – International Society for Technology, Education, and Science, 2023
The sudden, unexpected breakthrough in the intelligence shown by machines, as a wished for, but very disruptive element, will shape the future of our civilisation and Humans as individuals and collectives. The extreme drive towards commercialisation of newest developments already led to an extremely wide spread of Machine Intelligence Assistants…
Descriptors: Ethics, Artificial Intelligence, Technology Uses in Education, Morale
Phan, Vinhthuy; Wright, Laura; Decent, Bridgette – Journal of Educational Data Mining, 2022
The allocation of merit-based awards and need-based aid is important to both universities and students who wish to attend the universities. Current approaches tend to consider only institution-centric objectives (e.g. enrollment, revenue) and neglect student-centric objectives in their formulations of the problem. There is lack of consideration to…
Descriptors: Student Financial Aid, Access to Education, Merit Scholarships, Artificial Intelligence
Ye, Lu; Yuan, Yuqing – Journal of Baltic Science Education, 2022
Non-cognitive factors are considered critical aspects in shaping students' academic achievement. This study aims to analyze and explore the mechanisms of the influence of noncognitive factors on 15-year-old students' abilities in China and the United States. Based on the Programme for International Student Assessment (PISA) 2018 education dataset,…
Descriptors: Foreign Countries, Achievement Tests, International Assessment, Secondary School Students
Direct linkImmekus, Jason C.; Jeong, Tai-sun; Yoo, Jin Eun – Large-scale Assessments in Education, 2022
Large-scale international studies offer researchers a rich source of data to examine the relationship among variables. Machine learning embodies a range of flexible statistical procedures to identify key indicators of a response variable among a collection of hundreds or even thousands of potential predictor variables. Among these, penalized…
Descriptors: Foreign Countries, Secondary School Students, Artificial Intelligence, Educational Technology
Foreign Language Acquisition via Artificial Intelligence and Extended Reality: Design and Evaluation
Divekar, Rahul R.; Drozdal, Jaimie; Chabot, Samuel; Zhou, Yalun; Su, Hui; Chen, Yue; Zhu, Houming; Hendler, James A.; Braasch, Jonas – Computer Assisted Language Learning, 2022
Artificial Intelligence (AI) and Extended Reality (XR) have been employed in several foreign language education applications to increase the availability of experiential learning methods akin to international immersion programs. However, research in multi-modal spoken dialogue in L2 combined with immersive technologies and collaborative learning…
Descriptors: Second Language Learning, Language Acquisition, Artificial Intelligence, Computer Simulation
Uzun, Kutay – Journal of Educational Technology, 2020
Writing in L2 is both crucial and difficult for teachers and students since most of the assessment in higher education is in written form. The production of texts as well as providing feedback to them requires time and effort on both sides. For this reason, prediction of future L2 writing performance in advance may prove quite useful both for…
Descriptors: Writing Instruction, Writing Skills, Second Language Instruction, English (Second Language)
Schuler, Kathryn D.; Kodner, Jordan; Caplan, Spencer – First Language, 2020
In 'Against Stored Abstractions,' Ambridge uses neural and computational evidence to make his case against abstract representations. He argues that storing only exemplars is more parsimonious -- why bother with abstraction when exemplar models with on-the-fly calculation can do everything abstracting models can and more -- and implies that his…
Descriptors: Language Processing, Language Acquisition, Computational Linguistics, Linguistic Theory
Standen, Penelope J.; Brown, David J.; Taheri, Mohammad; Galvez Trigo, Maria J.; Boulton, Helen; Burton, Andrew; Hallewell, Madeline J.; Lathe, James G.; Shopland, Nicholas; Blanco Gonzalez, Maria A.; Kwiatkowska, Gosia M.; Milli, Elena; Cobello, Stefano; Mazzucato, Annaleda; Traversi, Marco; Hortal, Enrique – British Journal of Educational Technology, 2020
Artificial intelligence tools for education (AIEd) have been used to automate the provision of learning support to mainstream learners. One of the most innovative approaches in this field is the use of data and machine learning for the detection of a student's affective state, to move them out of negative states that inhibit learning, into…
Descriptors: Artificial Intelligence, Technology Uses in Education, Educational Technology, Identification
Marques, Lívia S.; Gresse von Wangenheim, Christiane; Hauck, Jean – Informatics in Education, 2020
Although Machine Learning (ML) is integrated today into various aspects of our lives, few understand the technology behind it. This presents new challenges to extend computing education early to ML concepts helping students to understand its potential and limits. Thus, in order to obtain an overview of the state of the art on teaching Machine…
Descriptors: Artificial Intelligence, Man Machine Systems, Elementary Secondary Education, Educational Technology
Fesharaki, Mehdi N.; Fetanat, AbdolHamid; Shooshtari, Davood Farajian – Cogent Education, 2020
How is the future of the Web, as one of the most influential inventions of the twentieth century? Today, there are great conceptual gaps between the Web 2.0 (Social Web), Web 3.0 (Semantic Web), and Web 4.0 (Pragmatic Web) generations. Every generation of Web is merely an independent conceptual branch of Web and the future Web needs to benefit…
Descriptors: Information Technology, Web 2.0 Technologies, Futures (of Society), Semantics
Salas-Rueda, Ricardo-Adán – E-Learning and Digital Media, 2020
This quantitative research aims to analyze the impact of the WampServer application in Blended learning during the educational process of computing through data science, machine learning, and neural networks. WampServer is a free application that allows the creation of websites considering the use of the database. This research proposes the use of…
Descriptors: Computer Software, Blended Learning, Educational Technology, Technology Uses in Education
Hansen, David M. – Strategic Enrollment Management Quarterly, 2020
In recent years we have developed a data analytics pipeline using artificial neural networks to predict prospective student matriculation for university admissions using very limited demographic data. Predictions are generated at the earliest stages of the admissions process and successfully inform recruiting and admissions staff about the…
Descriptors: Artificial Intelligence, Data Analysis, College Admission, Enrollment Management
Holowka, Peter – International Association for Development of the Information Society, 2020
COVID-19 presented a challenge to the traditional methods of teaching programming and robotics in a secondary school environment. When campuses were closed around the world in the spring of 2020, it was not possible for students to access the computer labs nor the robotics equipment that was traditionally used to facilitate the instruction of…
Descriptors: Robotics, COVID-19, Pandemics, Teaching Methods
Rybinski, Krzysztof; Kopciuszewska, Elzbieta – Assessment & Evaluation in Higher Education, 2021
This article presents the first-ever big data study of the student evaluation of teaching (SET) using artificial intelligence (AI). We train natural language processing (NLP) models on 1.6 million student evaluations from the US and the UK. We address two research questions: (1) are these models able to predict student ratings from the student…
Descriptors: Artificial Intelligence, Technology Uses in Education, Student Evaluation of Teacher Performance, Natural Language Processing
