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Ariely, Moriah; Nazaretsky, Tanya; Alexandron, Giora – International Journal of Artificial Intelligence in Education, 2023
Machine learning algorithms that automatically score scientific explanations can be used to measure students' conceptual understanding, identify gaps in their reasoning, and provide them with timely and individualized feedback. This paper presents the results of a study that uses Hebrew NLP to automatically score student explanations in Biology…
Descriptors: Artificial Intelligence, Algorithms, Natural Language Processing, Hebrew
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Galafassi, Cristiano; Galafassi, Fabiane Flores Penteado; Vicari, Rosa Maria; Reategui, Eliseo Berni – International Journal of Artificial Intelligence in Education, 2023
This work presents the intelligent tutoring system, EvoLogic, developed to assist students in problems of natural production in propositional logic. EvoLogic has been modeled as a multiagent system composed of three autonomous agents: interface, pedagogical and specialist agents. It supports pedagogical strategies inspired by the theory of…
Descriptors: Intelligent Tutoring Systems, Logical Thinking, Models, Teaching Methods
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Schneider, Johannes; Richner, Robin; Riser, Micha – International Journal of Artificial Intelligence in Education, 2023
Autograding short textual answers has become much more feasible due to the rise of NLP and the increased availability of question-answer pairs brought about by a shift to online education. Autograding performance is still inferior to human grading. The statistical and black-box nature of state-of-the-art machine learning models makes them…
Descriptors: Grading, Natural Language Processing, Computer Assisted Testing, Ethics
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Gonz├ílez-Eras, Alexandra; Dos Santos, Ricardo; Aguilar, Jose – International Journal of Artificial Intelligence in Education, 2023
Professional profiles are unstructured documents where the knowledge and experience of the editor predominate, presenting inconsistencies and ambiguities in terms of the competencies they contain, making complicated the recognition of knowledge and skills necessary for the proposal of university study programs. Also, the identification of…
Descriptors: Technological Literacy, Competence, Profiles, Evaluation Methods
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Shao, Lucy; Levine, Richard A.; Guarcello, Maureen A.; Wilke, Morten C.; Stronach, Jeanne; Frazee, James P.; Fan, Juanjuan – International Journal of Artificial Intelligence in Education, 2023
Propensity score matching and weighting methods are applied to balance covariates and reduce selection bias in the analysis of observational study data, and ultimately estimate a treatment effect. We wish to evaluate the impact of a Supplemental Instruction (SI) program on student success in an Introductory Statistics course. In such student…
Descriptors: Statistical Bias, Probability, Scores, Weighted Scores
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Tang, Jingwan; Zhou, Xiaofei; Wan, Xiaoyu; Daley, Michael; Bai, Zhen – International Journal of Artificial Intelligence in Education, 2023
The advances of machine learning (ML) in scientific discovery (SD) reveal exciting opportunities to utilize it as a cross-cutting tool for inquiry-based learning in K-12 STEM classrooms. There are, however, limited efforts on providing teachers with sufficient knowledge and skills to integrate ML into teaching. Our study addresses this gap by…
Descriptors: STEM Education, Artificial Intelligence, Elementary School Teachers, Secondary School Teachers
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Bamdev, Pakhi; Grover, Manraj Singh; Singla, Yaman Kumar; Vafaee, Payman; Hama, Mika; Shah, Rajiv Ratn – International Journal of Artificial Intelligence in Education, 2023
English proficiency assessments have become a necessary metric for filtering and selecting prospective candidates for both academia and industry. With the rise in demand for such assessments, it has become increasingly necessary to have the automated human-interpretable results to prevent inconsistencies and ensure meaningful feedback to the…
Descriptors: Language Proficiency, Automation, Scoring, Speech Tests
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Williams, Randi; Ali, Safinah; Devasia, Nisha; DiPaola, Daniella; Hong, Jenna; Kaputsos, Stephen P.; Jordan, Brian; Breazeal, Cynthia – International Journal of Artificial Intelligence in Education, 2023
Artificial Intelligence (AI) is revolutionizing many industries and becoming increasingly ubiquitous in everyday life. To empower children growing up with AI to navigate society's evolving sociotechnical context, we developed three middle school AI literacy curricula: "Creative AI,| "Dancing with AI," and "How to Train Your…
Descriptors: Middle School Students, Artificial Intelligence, Ethics, Curriculum
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Bellas, Francisco; Guerreiro-Santalla, Sara; Naya, Martin; Duro, Richard J. – International Journal of Artificial Intelligence in Education, 2023
This paper presents a proposal of specific curriculum in Artificial Intelligence (AI) for high school students, which has been organized as a two-year subject. The curriculum was designed based on two premises. The first one is that, although the proposal is targeted to scientific programmes, the involved students and teachers do not have any…
Descriptors: Foreign Countries, Artificial Intelligence, Curriculum, High Schools
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Ottenbreit-Leftwich, Anne; Glazewski, Krista; Jeon, Minji; Jantaraweragul, Katie; Hmelo-Silver, Cindy E.; Scribner, Adam; Lee, Seung; Mott, Bradford; Lester, James – International Journal of Artificial Intelligence in Education, 2023
With accelerating advances in artificial intelligence, it is clear that introducing K-12 students to AI is essential for preparation to interact with and potentially develop AI technologies. To succeed as the workers, creators, and innovators of the future, we argue students should encounter core concepts of AI as early as elementary school.…
Descriptors: Elementary School Students, Grade 4, Grade 5, Artificial Intelligence
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Zhang, Helen; Lee, Irene; Ali, Safinah; DiPaola, Daniella; Cheng, Yihong; Breazeal, Cynthia – International Journal of Artificial Intelligence in Education, 2023
The rapid expansion of artificial intelligence (AI) necessitates promoting AI education at the K-12 level. However, educating young learners to become AI literate citizens poses several challenges. The components of AI literacy are ill-defined and it is unclear to what extent middle school students can engage in learning about AI as a…
Descriptors: Artificial Intelligence, Digital Literacy, Ethics, Middle School Students
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Touretzky, David; Gardner-McCune, Christina; Seehorn, Deborah – International Journal of Artificial Intelligence in Education, 2023
This article provides an in-depth look at how K-12 students should be introduced to Machine Learning and the knowledge and skills they will develop as a result. We begin with an overview of the AI4K12 Initiative, which is developing national guidelines for teaching AI in K-12, and briefly discuss each of the "Five Big Ideas in AI" that…
Descriptors: Electronic Learning, Artificial Intelligence, Elementary School Students, Secondary School Students
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Leitner, Maxyn; Greenwald, Eric; Wang, Ning; Montgomery, Ryan; Merchant, Chirag – International Journal of Artificial Intelligence in Education, 2023
Artificial Intelligence (AI) permeates every aspect of our daily lives and is no longer a subject reserved for a select few in higher education but is essential knowledge that our youth need for the future. Much is unknown about the level of AI knowledge that is age and developmentally appropriate for high school, let alone about how to teach AI…
Descriptors: Instructional Design, Game Based Learning, High School Students, Artificial Intelligence
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Myers, Matthew C.; Wilson, Joshua – International Journal of Artificial Intelligence in Education, 2023
This study evaluated the construct validity of six scoring traits of an automated writing evaluation (AWE) system called "MI Write." Persuasive essays (N = 100) written by students in grades 7 and 8 were randomized at the sentence-level using a script written with Python's NLTK module. Each persuasive essay was randomized 30 times (n =…
Descriptors: Construct Validity, Automation, Writing Evaluation, Algorithms
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Lu, Yu; Wang, Deliang; Chen, Penghe; Meng, Qinggang; Yu, Shengquan – International Journal of Artificial Intelligence in Education, 2023
As a prominent aspect of modeling learners in the education domain, knowledge tracing attempts to model learner's cognitive process, and it has been studied for nearly 30 years. Driven by the rapid advancements in deep learning techniques, deep neural networks have been recently adopted for knowledge tracing and have exhibited unique advantages…
Descriptors: Learning Processes, Artificial Intelligence, Intelligent Tutoring Systems, Data Analysis
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