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Putnikovic, Marko; Jovanovic, Jelena – IEEE Transactions on Learning Technologies, 2023
Automatic grading of short answers is an important task in computer-assisted assessment (CAA). Recently, embeddings, as semantic-rich textual representations, have been increasingly used to represent short answers and predict the grade. Despite the recent trend of applying embeddings in automatic short answer grading (ASAG), there are no…
Descriptors: Automation, Computer Assisted Testing, Grading, Natural Language Processing
Yangna Hu; Cindy Sing Bik Ngai; Sihui Chen – Journal of Speech, Language, and Hearing Research, 2025
Purpose: This study examines existing automatic screening methods for developmental language disorder (DLD), a neurodevelopmental language deficit without known biomedical etiologies, focusing on languages, data sets, extracted features, performance metrics, and classification methods. Additionally, it summarizes the strengths and weaknesses of…
Descriptors: Developmental Disabilities, Language Impairments, Automation, Screening Tests
Yujie Han; Sumin Hong; Zhenyan Li; Cheolil Lim – TechTrends: Linking Research and Practice to Improve Learning, 2025
This scoping review investigates the roles of intelligent learning companion systems (LCS) within educational settings, as well as the presences artificial intelligence (AI) embodies within these roles, and their application in education. Employing the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for…
Descriptors: Artificial Intelligence, Definitions, Classification, Technology Uses in Education
Chelsea M. Parlett-Pelleriti; Elizabeth Stevens; Dennis Dixon; Erik J. Linstead – Review Journal of Autism and Developmental Disorders, 2023
Large amounts of autism spectrum disorder (ASD) data is created through hospitals, therapy centers, and mobile applications; however, much of this rich data does not have pre-existing classes or labels. Large amounts of data--both genetic and behavioral--that are collected as part of scientific studies or a part of treatment can provide a deeper,…
Descriptors: Artificial Intelligence, Autism Spectrum Disorders, Classification, Supervision
Sghir, Nabila; Adadi, Amina; Lahmer, Mohammed – Education and Information Technologies, 2023
The last few years have witnessed an upsurge in the number of studies using Machine and Deep learning models to predict vital academic outcomes based on different kinds and sources of student-related data, with the goal of improving the learning process from all perspectives. This has led to the emergence of predictive modelling as a core practice…
Descriptors: Prediction, Learning Analytics, Artificial Intelligence, Data Collection
Emmett Lombard – Research Management Review, 2024
This study analyzed how closely ChatGPT aligned with university IRB (institutional review board) decisions regarding which review category applies to research proposals. A literature review revealed that studies about IRBs mostly focus on ethical aspects of the process; this study offers additional insight into IRB administration. For this study,…
Descriptors: Artificial Intelligence, Technology Uses in Education, Universities, Institutional Evaluation
Ming Li; Ariunaa Enkhtur; Beverley Anne Yamamoto; Fei Cheng; Lilan Chen – Open Praxis, 2025
Generative Artificial Intelligence (GAI) models, such as ChatGPT, may inherit or amplify societal biases due to their training on extensive datasets. With the increasing usage of GAI by students, faculty, and staff in higher education institutions (HEIs), it is urgent to examine the ethical issues and potential biases associated with this…
Descriptors: Artificial Intelligence, Ethics, Technology Integration, Computer Software
Hemmler, Yvonne M.; Rasch, Julian; Ifenthaler, Dirk – TechTrends: Linking Research and Practice to Improve Learning, 2023
Educational recommender systems offer benefits for workplace learning by tailoring the selection of learning activities to the individual's learning goals. However, existing systems focus on the learner as the primary stakeholder of learning processes and do not consider the organization's perspective. We conducted a systematic review to develop a…
Descriptors: Workplace Learning, Educational Objectives, Educational Technology, Artificial Intelligence
Bahar Memarian; Tenzin Doleck – Education and Information Technologies, 2024
A key feature of embodied education is the participation of the learners' body and mind with the environment. Yet, little work has been done to review the state of embodied education with Artificial Intelligence (AI). The goal of this systematic review is to examine the state of human and AI's triad engagement in education, that is the mind, body,…
Descriptors: Artificial Intelligence, Cognitive Processes, Human Body, Technology Uses in Education
Souabi, Sonia; Retbi, Asmaâ; Idrissi, Mohammed Khalidi; Bennani, Samir – Electronic Journal of e-Learning, 2021
E-learning is renowned as one of the highly effective modalities of learning. Social learning, in turn, is considered to be of major importance as it promotes collaboration between learners. For properly managing learning resources, recommender systems have been implemented in e-learning to enhance learners' experience. Whilst recommender systems…
Descriptors: Artificial Intelligence, Information Systems, Electronic Learning, Social Development
Balyan, Renu; McCarthy, Kathryn S.; McNamara, Danielle S. – International Journal of Artificial Intelligence in Education, 2020
For decades, educators have relied on readability metrics that tend to oversimplify dimensions of text difficulty. This study examines the potential of applying advanced artificial intelligence methods to the educational problem of assessing text difficulty. The combination of hierarchical machine learning and natural language processing (NLP) is…
Descriptors: Natural Language Processing, Artificial Intelligence, Man Machine Systems, Classification
Mohamed, Mohamed Zulhilmi bin; Hidayat, Riyan; Suhaizi, Nurain Nabilah binti; Sabri, Norhafiza binti Mat; Mahmud, Muhamad Khairul Hakim bin; Baharuddin, Siti Nurshafikah binti – International Electronic Journal of Mathematics Education, 2022
The advancement of technology like artificial intelligence (AI) provides a chance to help teachers and students solve and improve teaching and learning performances. The goal of this review is to add to the conversation by offering a complete overview of AI in mathematics teaching and learning for students at all levels of education. A systematic…
Descriptors: Artificial Intelligence, Mathematics Instruction, Meta Analysis, Databases
Bakker, Nelleke – Paedagogica Historica: International Journal of the History of Education, 2021
This article explores the tensions between medical and pedagogical professionals involved with the classification and selection of pupils for the special day-schools for "feebleminded" children that were established from 1900 in the Netherlands to promote compulsory mass schooling's efficiency. These are set against the increasing…
Descriptors: Classification, Intelligence Tests, Foreign Countries, Learning Disabilities
Çetin, Hakan – International Journal of Education and Literacy Studies, 2022
This research aims to examine the researches based on augmented reality-based applications conducted for primary school students between 2015-2021 and to determine the positive and negative effects of this applications on students. The document analysis method was used in the research. In this context, articles containing AR-based applications…
Descriptors: Artificial Intelligence, Teaching Methods, Elementary School Students, Research Reports
Cascallar, Eduardo; Musso, Mariel; Kyndt, Eva; Dochy, Filip – Frontline Learning Research, 2014
Two articles, Edelsbrunner and, Schneider (2013), and Nokelainen and Silander (2014) comment on Musso, Kyndt, Cascallar, and Dochy (2013). Several relevant issues are raised and some important clarifications are made in response to both commentaries. Predictive systems based on artificial neural networks continue to be the focus of current…
Descriptors: Artificial Intelligence, Research Methodology, Prediction, Classification

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