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Toppo, Greg; Tracy, Jim – MIT Press, 2021
What will high school education look like in twenty years? High school students are educated today to take their places in a knowledge economy. But the knowledge economy, based on the assumption that information is a scarce and precious commodity, is giving way to an economy in which information is ubiquitous, digital, and machine-generated. In…
Descriptors: Robotics, High Schools, Educational Change, Artificial Intelligence
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Fateen, Menna; Mine, Tsunenori – International Educational Data Mining Society, 2021
Studying for entrance examinations can be a distressing period for numerous students. Consequently, many students decide to attend cram schools to assist them in preparing for these exams. For such schools and for all educational institutes, it is necessary to obtain the best tools to provide the highest quality of learning and guidance.…
Descriptors: Grade Prediction, Academic Achievement, Observation, Middle School Students
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Ozkan Ergene; Busra Caylan Ergene – Education and Information Technologies, 2025
One of the aims of the present study was to reveal and compare the performance of ChatGPT versions (GPT-4o, GPT-4, and GPT-3.5), MathGPT, and Gemini in solving 390 mathematical problems in interactive mathematics e-textbooks across various dimensions. The other aim was to identify the affordances and constraints of ChatGPT through the instrumental…
Descriptors: Artificial Intelligence, Computer Software, Synchronous Communication, Electronic Books
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Kurz, Terri; Jayasuriya, Suren; Swisher, Kimberlee; Mativo, John; Pidaparti, Ramana; Robinson, Dawn T. – Journal of Interactive Learning Research, 2022
Artificial intelligence is impacting society on a very large scale and should be included in K-12 educational content in some capacity to provide meaningful STEM experiences. Computer vision (a field of research that heavily leverages artificial intelligence) was emphasized in professional development for in-service teachers. The teachers received…
Descriptors: Attitude Change, Teacher Attitudes, Artificial Intelligence, Electronic Learning
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Asselman, Amal; Khaldi, Mohamed; Aammou, Souhaib – Interactive Learning Environments, 2023
Performance Factors Analysis (PFA) is considered one of the most important Knowledge Tracing (KT) approaches used for constructing adaptive educational hypermedia systems. It has shown a high prediction accuracy against many other KT approaches. While, the desire to estimate more accurately the student level leads researchers to enhance PFA by…
Descriptors: Algorithms, Artificial Intelligence, Factor Analysis, Student Behavior
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Hu, Xiaoyong; He, Wei; Chiu, Thomas K. F.; Zhao, Li – Education and Information Technologies, 2023
Recently, Artificial Intelligence (AI), seen as an engineering domain, has been introduced into school education, but its pedagogy remains unclear. In general, group learning has been applied as a primary form of instruction in hands-on engineering activities. This learning approach is more common in higher education. School students are less…
Descriptors: Middle School Students, Artificial Intelligence, Group Activities, Group Behavior
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John Sabatini; Arthur C. Graesser; John Hollander; Tenaha O'Reilly – British Journal of Educational Technology, 2023
We argue in this paper that there is currently no adequate theoretical framework or model that spans the twelve odd year trajectory from non-reader to proficient reader, nor addresses fine-grain skill acquisition, mastery and integration. The target construct itself, reading proficiency, as often operationalized as an endpoint of formal secondary…
Descriptors: Literacy Education, Scaffolding (Teaching Technique), Decision Making, Artificial Intelligence
Minji Jeon – ProQuest LLC, 2023
This study investigated middle school students' Artificial Intelligence (AI) literacy, focusing on cognitive and affective dimensions with regard to learning about AI. Fourteen middle school students participated in a five-day summer camp. They engaged in a project-based AI literacy program with hands-on activities, data collection and modeling,…
Descriptors: Middle School Students, Artificial Intelligence, Active Learning, Student Projects
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Devika Venugopalan; Ziwen Yan; Conrad Borchers; Jionghao Lin; Vincent Aleven – Grantee Submission, 2025
Caregivers (i.e., parents and members of a child's caring community) are underappreciated stakeholders in learning analytics. Although caregiver involvement can enhance student academic outcomes, many obstacles hinder involvement, most notably knowledge gaps with respect to modern school curricula. An emerging topic of interest in learning…
Descriptors: Homework, Computational Linguistics, Teaching Methods, Learning Analytics
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Rong Wu; Zhonggen Yu – British Journal of Educational Technology, 2024
Artificial intelligence (AI) chatbots are gaining increasing popularity in education. Due to their increasing popularity, many empirical studies have been devoted to exploring the effects of AI chatbots on students' learning outcomes. The proliferation of experimental studies has highlighted the need to summarize and synthesize the inconsistent…
Descriptors: Artificial Intelligence, Synchronous Communication, Outcomes of Education, Educational Improvement
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Shruti Priya; Shubhankar Bhadra; Sridhar Chimalakonda; Akhila Sri Manasa Venigalla – Interactive Learning Environments, 2024
Owing to the predominant role of Machine Learning(ML) across domains, it is being introduced at multiple levels of education, including K-12. Researchers have leveraged games, augmented reality and other ways to make learning ML concepts interesting. However, most of the existing games to teach ML concepts either focus on use-cases and…
Descriptors: Artificial Intelligence, Secondary School Students, Video Games, Visual Aids
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Munise Seçkin Kapucu; I?brahim Özcan; Hülya Özcan; Ahmet Aypay – International Journal of Technology in Education and Science, 2024
Our research aims to predict students' academic performance by considering the variables affecting academic performance in science courses using the deep learning method from machine learning algorithms and to determine the importance of independent variables affecting students' academic performance in science courses. 445 students from 5th, 6th,…
Descriptors: Secondary School Students, Science Achievement, Artificial Intelligence, Foreign Countries
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Ha Tien Nguyen; Conrad Borchers; Meng Xia; Vincent Aleven – Grantee Submission, 2024
Intelligent tutoring systems (ITS) can help students learn successfully, yet little work has explored the role of caregivers in shaping that success. Past interventions to support caregivers in supporting their child's homework have been largely disjunct from educational technology. The paper presents prototyping design research with nine middle…
Descriptors: Middle School Mathematics, Intelligent Tutoring Systems, Caregivers, Caregiver Attitudes
Matthew Christopher Myers – ProQuest LLC, 2024
This study uses an experimental comparative design to accomplish two primary goals related teachers' perceptions of automated writing evaluation (AWE) performance. First, it quantitatively and qualitatively examines teachers' perceptions of the accuracy and trustworthiness of differentially performing AWE models. Second, it synthesizes interview…
Descriptors: Language Arts, Teacher Attitudes, English Teachers, Automation
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Ramon Mayor Martins; Christiane Gresse Von Wangenheim; Marcelo Fernando Rauber; Jean Carlo Rossa Hauck; Melissa Figueiredo Silvestre – Informatics in Education, 2024
Knowledge about Machine Learning (ML) is becoming essential, yet it remains a restricted privilege that may not be available to students from a low socio-economic status background. Thus, in order to provide equal opportunities, we taught ML concepts and applications to 158 middle and high school students from a low socio-economic background in…
Descriptors: Middle School Students, High School Students, Low Income Students, Socioeconomic Status
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