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Lei Shi – Journal of Educational Computing Research, 2025
This study explores the integration of advanced AI technologies, including emotion detection and adaptive learning systems, to enhance second language acquisition among 274 English as a Foreign Language (EFL) learners. Utilizing a pretest-posttest randomized control trial, the research evaluates the effects of AI-enhanced interventions on…
Descriptors: Artificial Intelligence, Technology Uses in Education, Emotional Response, Intelligent Tutoring Systems
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Mingyu Feng; Natalie Brezack; Chunwei Huang; Yvonne Kao; Kelly Collins; Melissa Lee; Megan Schneider; Wynnie Chan – Journal of Computer Assisted Learning, 2025
Background: Computer-assisted educational technologies that integrate affective supports into math practices could be particularly beneficial for addressing troubling declines in middle grade students' math achievement, affect, and motivation. MathSpring is a web-based intelligent tutor that offers personalised content, remedial tutoring, and…
Descriptors: Intelligent Tutoring Systems, Technology Uses in Education, Mathematics Education, Mathematics Achievement
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Hyeongdon Moon; Richard Lee Davis; Seyed Parsa Neshaei; Pierre Dillenbourg – International Educational Data Mining Society, 2025
Knowledge tracing models have enabled a range of intelligent tutoring systems to provide feedback to students. However, existing methods for knowledge tracing in learning sciences are predominantly reliant on statistical data and instructor-defined knowledge components, making it challenging to integrate AI-generated educational content with…
Descriptors: Artificial Intelligence, Natural Language Processing, Automation, Information Management
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Kole Norberg; Husni Almoubayyed; Stephen Fancsali – International Educational Data Mining Society, 2025
Solving a math word problem (MWP) requires understanding the mathematical components of the problem and an ability to decode the text. For some students, lower reading comprehension skills may make engagement with the mathematical content more difficult. Readability formulas (e.g., Flesch Reading Ease) are frequently used to assess reading…
Descriptors: Mathematics Instruction, Word Problems (Mathematics), Problem Solving, Reading Skills
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Kenneth Holman; Matthew Marino; Trey Vasquez; Michelle Taub; Jessica Hunt; Yacine Tazi – Insights into Learning Disabilities, 2025
This systematic review examines the impact of artificial intelligence (AI) interventions on mathematics education, focusing on studies published after 2020. A total of 14 empirical studies were selected based on inclusion criteria that emphasized AI's role in enhancing student learning outcomes, engagement, and teaching practices. The review…
Descriptors: Artificial Intelligence, Technology Uses in Education, Mathematics Education, Mathematics Achievement
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Da Tien Nguyen – International Electronic Journal of Mathematics Education, 2025
This study conducted a bibliometric analysis to investigate the trends and patterns in the application of artificial intelligence (AI) in mathematics education during the period 2020-2025. By analyzing a large dataset of scholarly publications, the study aims to identify the key research areas, influential researchers, and emerging trends in this…
Descriptors: Foreign Countries, Literature Reviews, Bibliometrics, Artificial Intelligence
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Metta Marwiang; Mongkhol Prasertsang; Putcharee Junpeng – Journal of Education and Learning, 2025
This study examined the effectiveness of an intelligent tutoring system (ITS) driven by real-time feedback in enhancing students' mathematical learning outcomes, as defined by the Structure of Observed Learning Outcomes. Conducted within the domains of Measurement and Geometry, the study employed a randomized controlled trial involving 120…
Descriptors: Mathematics Achievement, Mathematics Education, Intelligent Tutoring Systems, Feedback (Response)
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Mark Guiberson; Isabella H. Mijares – Topics in Language Disorders, 2025
This article explores the potential of technology-enhanced dialogic reading (DR) approaches to enhance language skills in young children via examining evidence from systematic reviews and extant technology integration research plus a preliminary study of caregivers' perceptions of digital tutorials designed to promote DR. Systematic reviews…
Descriptors: Dialogs (Language), Reading Skills, Language Skills, Young Children
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Conrad Borchers; Ha Tien Nguyen; Paulo F. Carvalho; Kenneth R. Koedinger; Vincent Aleven – Grantee Submission, 2025
Parent and caregiver involvement in homework can be critical to student success, yet many families face barriers such as limited time and content knowledge. We examine two approaches to involving caregivers in homework that students complete with tutoring systems for middle school mathematics. The first, an intelligent caregiver support module,…
Descriptors: Parent Role, Caregiver Role, Homework, Middle School Students
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Sha, Lele; Rakovic, Mladen; Lin, Jionghao; Guan, Quanlong; Whitelock-Wainwright, Alexander; Gasevic, Dragan; Chen, Guanliang – IEEE Transactions on Learning Technologies, 2023
In online courses, discussion forums play a key role in enhancing student interaction with peers and instructors. Due to large enrolment sizes, instructors often struggle to respond to students in a timely manner. To address this problem, both traditional machine learning (ML) (e.g., Random Forest) and deep learning (DL) approaches have been…
Descriptors: Computer Mediated Communication, Discussion Groups, Artificial Intelligence, Intelligent Tutoring Systems
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Benmesbah, Ouissem; Lamia, Mahnane; Hafidi, Mohamed – Interactive Learning Environments, 2023
Adaptive learning has garnered researchers' interest. The main issue within this field is how to select appropriate learning objects (LOs) based on learners' requirements and context, and how to combine the selected LOs to form what is known as an adaptive learning path. Heuristic and metaheuristic approaches have achieved significant progress on…
Descriptors: Algorithms, Teaching Methods, Educational Innovation, Genetics
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Yousaf, Yousra; Shoaib, Muhammad; Hassan, Muhammad Awais; Habiba, Ume – Interactive Learning Environments, 2023
Learning trend has been shifted from a conventional way to a digital way in the form of E-learning, but it faces a high dropout ratio. Lack of engagement is one of the primary factors reported for this issue as the same type of course content is presented to learners despite their different background, knowledge and learning styles. Different…
Descriptors: Intelligent Tutoring Systems, Cognitive Style, Learner Engagement, Academic Achievement
Richey, J. Elizabeth; McEldoon, Katherine; Tan, Elaine – Pearson, 2023
Pearson's Learning Foundations describe the optimal conditions for learning and reflect the learner experience Pearson hopes their products will create. Pearson does this by incorporating the Learning Design Principles. Each of the Learning Design Principles goes into detail about a key principle, supporting product design and marketing by…
Descriptors: Theory Practice Relationship, Research and Development, Individualized Instruction, Intelligent Tutoring Systems
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Zotov, Vladimir; Kramkowski, Eric – International Journal of Artificial Intelligence in Education, 2023
Intelligent tutoring systems (ITSs) may augment military training systems and mitigate existing limitations in training personnel and resources. A study was conducted to investigate the effectiveness of an embedded rifle marksmanship ITS for Moving Targets (MT-ITS). MT-ITS has two main components: (1) a Smart Sight System that provides a…
Descriptors: Intelligent Tutoring Systems, Military Personnel, Military Training, Program Effectiveness
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Husni Almoubayyed; Stephen E. Fancsali; Steve Ritter – Grantee Submission, 2023
Recent research seeks to develop more comprehensive learner models for adaptive learning software. For example, models of reading comprehension built using data from students' use of adaptive instructional software for mathematics have recently been developed. These models aim to deliver experiences that consider factors related to learning beyond…
Descriptors: Middle School Students, Middle School Mathematics, Reading Comprehension, Intelligent Tutoring Systems
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