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Moresi, Marco; Gomez, Marcos J.; Benotti, Luciana – IEEE Transactions on Learning Technologies, 2021
Based on hundreds of thousands of hours of data about how students learn in massive open online courses, educational machine learning promises to help students who are learning to code. However, in most classrooms, students and assignments do not have enough historical data for feeding these data hungry algorithms. Previous work on predicting…
Descriptors: Prediction, Difficulty Level, Programming, Online Courses
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Maestrales, Sarah; Zhai, Xiaoming; Touitou, Israel; Baker, Quinton; Schneider, Barbara; Krajcik, Joseph – Journal of Science Education and Technology, 2021
In response to the call for promoting three-dimensional science learning (NRC, 2012), researchers argue for developing assessment items that go beyond rote memorization tasks to ones that require deeper understanding and the use of reasoning that can improve science literacy. Such assessment items are usually performance-based constructed…
Descriptors: Artificial Intelligence, Scoring, Evaluation Methods, Chemistry
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R., Akila Devi T.; Sathick, K. Javubar; Khan, A. Abdul Azeez; Raj, L. Arun – International Journal of Web-Based Learning and Teaching Technologies, 2021
Non-Factoid Question Answering (QA) is the next generation of textual QA systems, which gives passage level summaries for a natural language query, posted by the user. The main issue lies in the appropriateness of the generated summary. This paper proposes a framework for non-factoid QA system, which has three main components: (1) a deep neural…
Descriptors: Natural Language Processing, Artificial Intelligence, Classification, Responses
Li, Chenglu; Xing, Wanli; Leite, Walter – Grantee Submission, 2021
To support online learners at a large scale, extensive studies have adopted machine learning (ML) techniques to analyze students' artifacts and predict their learning outcomes automatically. However, limited attention has been paid to the fairness of prediction with ML in educational settings. This study intends to fill the gap by introducing a…
Descriptors: Learning Analytics, Prediction, Models, Electronic Learning
Scott Anthony Gigante – ProQuest LLC, 2021
In recent years, modern technologies have enabled the collection of exponentially larger quantities of data in the biomedical domain and elsewhere. In particular, the advent of single-cell genomics has allowed for the collection of datasets containing hundreds of thousands of cells measured in tens of thousands of dimensions. This rapid expansion…
Descriptors: Visualization, Data, Algorithms, Artificial Intelligence
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William Neil Littell; Brittany L. Peterson – Communication Teacher, 2025
Artificial intelligence (AI) is fundamentally reshaping classroom experiences. In this activity, students in a graduate-level project management class engaged in real-time conversations with AI- powered chatbots as though they were actual people. Students were required to perform a stakeholder analysis on the key stakeholders involved in their…
Descriptors: Artificial Intelligence, Synchronous Communication, Computer Software, Executive Function
Emmanuel Dumbuya – Online Submission, 2025
The exponential growth of online learning has catalyzed significant pedagogical innovations and transformed the educational landscape. This paper explores the emerging trends in online learning, including the shift towards blended learning, the rise of personalized learning, and the integration of technology-enhanced pedagogical practices. The…
Descriptors: Educational Trends, Electronic Learning, Educational Innovation, Teaching Methods
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Hao-Chiang Koong Lin; Chun-Hsiung Tseng; Nian-Shing Chen – Educational Technology & Society, 2025
In recent years, learning programming has been a challenge for both learners and educators. How to enhance student engagement and learning outcomes has been a significant concern for researchers. This study examines the effects of AI-based pedagogical agents on students' learning experiences in programming courses, focusing on web game development…
Descriptors: Programming, Learner Engagement, Self Efficacy, Artificial Intelligence
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Kristin Dutcher Mann – History Teacher, 2025
Historians sometimes view teaching and community engagement as peripheral to research. Self-reflection on the design of assignments, pedagogy techniques, and students' work aids teachers as they refine their teaching, and it can also inform research questions and methods. Teaching, research, and community engagement do not have to be separate…
Descriptors: Community Involvement, Authentic Learning, History Instruction, Teaching Methods
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Galip Bedir; Ibrahim Benek; Eda Yuca; Ismail Donmez – Journal of Education in Science, Environment and Health, 2025
Artificial Intelligence (AI) emerges as the development of computer systems and software that imitate human abilities and perform human-like tasks. Understanding what gifted students think about this system that includes deep cognitive abilities is considered important. Based on this premise, this study examines the perceptions of gifted students…
Descriptors: Gifted, Student Attitudes, Freehand Drawing, Artificial Intelligence
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Ran Liu; Wei Pang; Junming Chen; Vishalache A. P. Balakrishnan; Hai Leng Chin – Education and Information Technologies, 2025
In the context of globalization, adapting to modern educational needs and adopting innovative teaching methods have become increasingly crucial, particularly in the field of children's aesthetic education. This study explores the integration of scaffolding instruction and AI-driven diffusion models in children's aesthetic education, with a special…
Descriptors: Scaffolding (Teaching Technique), Artificial Intelligence, Aesthetic Education, Asian Culture
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Peidi Gu; Fang Xu; Lingwei Chen; Zijie Ma; Madian Zhang; Yi Zhang – Education and Information Technologies, 2025
Conversational skills, which are essential for effective social interactions and typically pose difficulties for individuals with autism spectrum disorder (ASD), include abilities such as initiating topics, engaging in back-and-forth dialog, and responding to conversational cues. Chatbots have been used in mental health fields, and the development…
Descriptors: Technology Uses in Education, Artificial Intelligence, Interpersonal Communication, Communication Skills
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Marcus Pietsch; Dana-Kristin Mah – Educational Technology Research and Development, 2025
Rapid developments in artificial intelligence (AI) require dynamic adaptation in education to integrate new technologies timely and sustainably. In particular, the rise of generative AI requires leadership to implement it in a meaningful way for teaching and learning. School leaders have a special role to play in driving digital transformation.…
Descriptors: Artificial Intelligence, Computer Software, Technology Integration, Teaching Methods
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Mengqian Wang; Wenge Guo – ECNU Review of Education, 2025
This review compares generative artificial intelligence with five representative educational technologies in history and concludes that AI technology can become a knowledge producer and thus can be utilized as educative AI to enhance teaching and learning outcomes. From a historical perspective, each technological breakthrough has affected…
Descriptors: Artificial Intelligence, Man Machine Systems, Natural Language Processing, History
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Enes Küçük; Fidaye Cincil; Yasemin Karal – Journal of Theoretical Educational Science, 2025
AI technology, which is becoming more widespread day by day, also affects education and training processes. The use of AI tools in educational environments provides many benefits to teachers and students. However, the use of AI in education also raises some ethical concerns. The aim of this study was to reveal the ethical issues arising from the…
Descriptors: Ethics, Teaching Methods, Learning Analytics, Internet
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