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Peer reviewed
Direct linkIvana Stepanovic – Prospects, 2024
While AI-powered digital platforms are transforming the economy through forms of algorithmic governance that prioritize the automation of work and decision-making, it is critical to ensure that human education remains relevant in the age of machine learning. Unrestricted reliance on artificial intelligence could undermine the entire system of…
Descriptors: Higher Education, Artificial Intelligence, Humanism, Human Capital
Andrea Hicks; Wissam Kontar – Journal of Civil Engineering Education, 2024
Engineering programs must produce graduates who are able to consider multicriteria decisions including ethical implications during engineering practice. Teaching students in a meaningful manner to consider these multifaceted decisions was investigated through the usage of disasters and primary coverage of infrastructure disasters. Students…
Descriptors: Engineering Education, Emergency Programs, Natural Disasters, Ethics
Abdullahi Yusuf; Nasrin Pervin; Marcos Román-González; Norah Md Noor – Review of Education, 2024
Given the potential applications of generative AI (GenAI) in education and its rising interest in research, this systematic review mapped the thematic landscape of 407 publications indexed in the Web of Science, ScienceDirect and Scopus. Using EPPI Reviewer, publication type, educational level, disciplines, research areas and applications of GenAI…
Descriptors: Artificial Intelligence, Educational Technology, Technology Uses in Education, Elementary Secondary Education
Stefania Badiglio; Giuseppe Chiazzese; Vito Pipitone; Luciano Seta – International Journal on E-Learning, 2024
This study explores the interaction between artificial intelligence (AI) and human behavior in economic contexts, focusing on using generative AI, such as ChatGPT, to examine trust and reciprocity. Through an experiment conducted at LUMSA in Palermo, 61 economics students participated in three economic games: the Ultimatum Game, the Trust Game,…
Descriptors: Artificial Intelligence, Computer Software, Asynchronous Communication, Man Machine Systems
Download full textEyüp Yurt – International Society for Technology, Education, and Science, 2024
This study examines the use of artificial intelligence-based assessment systems in education and their impact on student achievement. AI technologies make the learning experience more compelling by monitoring and evaluating student performance through data analysis and machine learning, thereby individualizing the assessment process. This study…
Descriptors: Artificial Intelligence, Student Evaluation, Academic Achievement, Computer Uses in Education
Eyüp Yurt – International Society for Technology, Education, and Science, 2024
This study examines teachers' motivations for using artificial intelligence (AI) applications in education from the perspective of Expectancy-Value Theory. The Expectancy-Value Theory is a framework used to explain the key factors that influence individuals' motivation to perform a specific task. In this study, the two main components of the…
Descriptors: Teacher Attitudes, Teacher Motivation, Artificial Intelligence, Computer Software
Policy and Global Affairs, Contributor; Health and Medicine Division, Contributor; Division of Behavioral and Social Sciences and Education, Contributor; Science and Technology for Sustainability Program, Contributor; Board on Health Care Services, Contributor; Board on Science Education, Contributor; Franklin Carrero-Martínez, Rapporteur; Jennifer Saunders, Rapporteur; Emi Kameyama, Rapporteur – National Academies Press, 2024
The pandemic and overlapping global crises, including climate change, have increased attention to the importance of mental health and well-being as foundational for humans. Similarly, COVID-19 significantly exacerbated gaps in education, leaving children one to three years behind. Artificial intelligence (AI) has demonstrated potential to be…
Descriptors: Artificial Intelligence, Technology Uses in Education, Sustainability, Pandemics
Nancy Sulla – Eye on Education, 2024
If students haven't developed the brain-based skills to focus, catch and correct errors, identify cause-and-effect relationships, and more, they can't make sense of lessons. Executive function is the missing link to student achievement. But how can you develop this in the classroom? Bestselling author Nancy Sulla has the answers. She explains how…
Descriptors: Executive Function, Academic Achievement, Cognitive Processes, Daily Living Skills
Ying Zhang; Yan Zhang; Wei Xu; Zhifeng Wang; Jianwen Sun – International Educational Data Mining Society, 2024
Knowledge tracing (KT) aims to model a learner's knowledge mastery level through his historical exercise records to predict future learning performance. Using this technology, learners can get appropriate customized exercises based on their current knowledge states, and thus the great potential of personalized teaching services such as intelligent…
Descriptors: Artificial Intelligence, Intelligent Tutoring Systems, Knowledge Level, Music Education
Yuya Asano; Diane Litman; Quentin King-Shepard; Tristan Maidment; Tyree Langley; Teresa Davison – International Educational Data Mining Society, 2024
One of the keys to the success of collaborative learning is balanced participation by all learners, but this does not always happen naturally. Pedagogical robots have the potential to facilitate balance. However, it remains unclear what participation balance robots should aim at; various metrics have been proposed, but it is still an open question…
Descriptors: Cooperative Learning, Tutoring, Artificial Intelligence, Interpersonal Relationship
Oscar Clivio; Avi Feller; Chris Holmes – Grantee Submission, 2024
Reweighting a distribution to minimize a distance to a target distribution is a powerful and flexible strategy for estimating a wide range of causal effects, but can be challenging in practice because optimal weights typically depend on knowledge of the underlying data generating process. In this paper, we focus on design-based weights, which do…
Descriptors: Evaluation Methods, Causal Models, Error of Measurement, Guidelines
Wulff, Peter; Buschhüter, David; Westphal, Andrea; Mientus, Lukas; Nowak, Anna; Borowski, Andreas – Journal of Science Education and Technology, 2022
Science education researchers typically face a trade-off between more quantitatively oriented confirmatory testing of hypotheses, or more qualitatively oriented exploration of novel hypotheses. More recently, open-ended, constructed response items were used to combine both approaches and advance assessment of complex science-related skills and…
Descriptors: Evaluation Methods, Science Education, Educational Research, Artificial Intelligence
The Promises and Challenges of Artificial Intelligence for Teachers: A Systematic Review of Research
Celik, Ismail; Dindar, Muhterem; Muukkonen, Hanni; Järvelä, Sanna – TechTrends: Linking Research and Practice to Improve Learning, 2022
This study provides an overview of research on teachers' use of artificial intelligence (AI) applications and machine learning methods to analyze teachers' data. Our analysis showed that AI offers teachers several opportunities for improved planning (e.g., by defining students' needs and familiarizing teachers with such needs), implementation…
Descriptors: Artificial Intelligence, Educational Technology, Technology Uses in Education, Teacher Role
Darvishi, Ali; Khosravi, Hassan; Sadiq, Shazia; Gaševic, Dragan – British Journal of Educational Technology, 2022
Peer assessment has been recognised as a sustainable and scalable assessment method that promotes higher-order learning and provides students with fast and detailed feedback on their work. Despite these benefits, some common concerns and criticisms are associated with the use of peer assessments (eg, scarcity of high-quality feedback from peer…
Descriptors: Artificial Intelligence, Learning Analytics, Peer Evaluation, Student Evaluation
Zhao, Zhong; Zhu, Zhipeng; Zhang, Xiaobin; Tang, Haiming; Xing, Jiayi; Hu, Xinyao; Lu, Jianping; Qu, Xingda – Journal of Autism and Developmental Disorders, 2022
Our study investigated the feasibility of using head movement features to identify individuals with autism spectrum disorder (ASD). Children with ASD and typical development (TD) were required to answer ten yes--no questions, and they were encouraged to nod/shake head while doing so. The head rotation range (RR) and the amount of rotation per…
Descriptors: Autism, Pervasive Developmental Disorders, Motion, Human Body
