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Grant Cooper; Kok-Sing Tang – Journal of Science Education and Technology, 2024
The proliferation of generative artificial intelligence (GenAI) means we are witnessing transformative change in education. While GenAI offers exciting possibilities for personalised learning and innovative teaching methodologies, its potential for reinforcing biases and perpetuating stereotypes poses ethical and pedagogical concerns. This article…
Descriptors: Artificial Intelligence, Science Education, Visual Aids, Stereotypes
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Benjamin Gagl; Klara Gregorová – npj Science of Learning, 2024
Efficient reading is essential for societal participation, so reading proficiency is a central educational goal. Here, we use an individualized diagnostics and training framework to investigate processes in visual word recognition and evaluate its usefulness for detecting training responders. We (i) motivated a training procedure based on the…
Descriptors: Reading, Reading Rate, Language Acquisition, Artificial Intelligence
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José Luis Rodríguez Illera – Digital Education Review, 2024
The article reviews some of the relationships between AI and education, emphasizing the metaphors used, the difficulties in finding points of agreement, as well as aspects of the social criticism that is made of AI (e.g. considering that it can be a form of unwanted deviation). AI appears as one more case of technology that comes to improve…
Descriptors: Artificial Intelligence, Technology Uses in Education, Thinking Skills, Ethics
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Adronisha T. Frazier – Research Issues in Contemporary Education, 2024
This position paper explores the current state of artificial intelligence (AI) tools, educator support of and opposition to AI tools in teaching and learning, and the ethical and social implications of AI tools in higher education. As technology continuously develops in the educational community, educators must have a voice in how AI exists in the…
Descriptors: Artificial Intelligence, Higher Education, Technology Uses in Education, Inclusion
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Shin-Yu Kim; Inseong Jeon; Seong-Joo Kang – Journal of Chemical Education, 2024
Artificial intelligence (AI) and data science (DS) are receiving a lot of attention in various fields. In the educational field, the need for education utilizing AI and DS is also being emerged. In this context, we have created an AI/DS integrating program that generates a compound classification/regression model using characteristics of compounds…
Descriptors: Chemistry, Science Instruction, Laboratory Experiments, Artificial Intelligence
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Beatriz Carbajal-Carrera – Australian Review of Applied Linguistics, 2024
The growing implementation of Generative AI (GenAI) in education has implications on the representation of knowledge and identity across languages. In a context where content biases have been reported in AI-generated content, it becomes relevant to interrogate the ways in which AI technologies represent different linguistic identities. This…
Descriptors: Artificial Intelligence, Sociolinguistics, Language Usage, Bias
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Li Chen; Gen Li; Boxuan Ma; Cheng Tang; Masanori Yamada – International Association for Development of the Information Society, 2024
This paper proposes a three-step approach to develop knowledge graphs that integrate textbook-based target knowledge graph with student dialogue-based knowledge graphs. The study was conducted in seventh-grade STEM classes, following a collaborative problem solving process. First, the proposed approach generates a comprehensive target knowledge…
Descriptors: Concept Mapping, Graphs, Cooperative Learning, Problem Solving
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Ghosh, Krishnendu; Nangi, Sharmila Reddy; Kanchugantla, Yashasvi; Rayapati, Pavan Gopal; Bhowmick, Plaban Kumar; Goyal, Pawan – International Journal of Artificial Intelligence in Education, 2022
Video lectures are considered as one of the primary media to cater good-quality educational content to the learners. The video lectures illustrate the course-relevant concepts with necessary details. However, they sometimes fail to offer a basic understanding of off-topic concepts. Such off-topic concepts may spawn cognitive overload among the…
Descriptors: Video Technology, Concept Formation, Artificial Intelligence, Lecture Method
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MacLellan, Christopher J.; Koedinger, Kenneth R. – International Journal of Artificial Intelligence in Education, 2022
Intelligent tutoring systems are effective for improving students' learning outcomes (Pane et al. 2013; Koedinger and Anderson, "International Journal of Artificial Intelligence in Education," 8, 1-14, 1997; Bowen et al. "Journal of Policy Analysis and Management," 1, 94-111 2013). However, constructing tutoring systems that…
Descriptors: Intelligent Tutoring Systems, Artificial Intelligence, Models, Instructional Design
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Chen, Dyi-Cheng; You, Ci-Syong; Su, Ming-Shang – Interactive Learning Environments, 2022
This study identified the competency requirement for artificial intelligence in finite element analysis. The 10 Delphi group members included 5 field engineers in mechanical fields and 5 scholars from a technology institute. Next, 10 field experts were invited to participate. Using the Delphi technique and analytic hierarchy process,…
Descriptors: Engineering, Technical Occupations, Competence, Artificial Intelligence
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Hoq, Muntasir; Brusilovsky, Peter; Akram, Bita – International Educational Data Mining Society, 2023
Prediction of student performance in introductory programming courses can assist struggling students and improve their persistence. On the other hand, it is important for the prediction to be transparent for the instructor and students to effectively utilize the results of this prediction. Explainable Machine Learning models can effectively help…
Descriptors: Academic Achievement, Prediction, Models, Introductory Courses
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Shen, Guohua; Yang, Sien; Huang, Zhiqiu; Yu, Yaoshen; Li, Xin – Education and Information Technologies, 2023
Due to the growing demand for information technology skills, programming education has received increasing attention. Predicting students' programming performance helps teachers realize their teaching effect and students' learning status in time to provide support for students. However, few of the existing researches have taken the code that…
Descriptors: Prediction, Programming, Student Characteristics, Profiles
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Ouyang, Fan; Xu, Weiqi; Cukurova, Mutlu – International Journal of Computer-Supported Collaborative Learning, 2023
Collaborative problem solving (CPS) enables student groups to complete learning tasks, construct knowledge, and solve problems. Previous research has argued the importance of examining the complexity of CPS, including its multimodality, dynamics, and synergy from the complex adaptive systems perspective. However, there is limited empirical…
Descriptors: Artificial Intelligence, Learning Analytics, Cooperative Learning, Problem Solving
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Goldstein, Yoav; Legewie, Nicolas M.; Shiffer-Sebba, Doron – Sociological Methods & Research, 2023
Video data offer important insights into social processes because they enable direct observation of real-life social interaction. Though such data have become abundant and increasingly accessible, they pose challenges to scalability and measurement. Computer vision (CV), i.e., software-based automated analysis of visual material, can help address…
Descriptors: Artificial Intelligence, Data Analysis, Interpersonal Relationship, Social Science Research
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Jiang, Zhehan; Han, Yuting; Xu, Lingling; Shi, Dexin; Liu, Ren; Ouyang, Jinying; Cai, Fen – Educational and Psychological Measurement, 2023
The part of responses that is absent in the nonequivalent groups with anchor test (NEAT) design can be managed to a planned missing scenario. In the context of small sample sizes, we present a machine learning (ML)-based imputation technique called chaining random forests (CRF) to perform equating tasks within the NEAT design. Specifically, seven…
Descriptors: Test Items, Equated Scores, Sample Size, Artificial Intelligence
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