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Peter Baldwin; Victoria Yaneva; Kai North; Le An Ha; Yiyun Zhou; Alex J. Mechaber; Brian E. Clauser – Journal of Educational Measurement, 2025
Recent developments in the use of large-language models have led to substantial improvements in the accuracy of content-based automated scoring of free-text responses. The reported accuracy levels suggest that automated systems could have widespread applicability in assessment. However, before they are used in operational testing, other aspects of…
Descriptors: Artificial Intelligence, Scoring, Computational Linguistics, Accuracy
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
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
Fabio Spano; Adam Kardos; Craig Dennis Howard – International Journal of Designs for Learning, 2025
This design case presents a gamified dictionary learning intervention for away-from-school learning. Intended to be mobile, the game features AI speech recognition for practicing English words and phrases. We also introduce a second feature--an integrated teacher dashboard that addresses issues in traditional homework--a lack of immediate feedback…
Descriptors: Gamification, Dictionaries, Artificial Intelligence, English (Second Language)
Calvert, Sandra L.; Putnam, Marisa M.; Aguiar, Naomi R.; Ryan, Rebecca M.; Wright, Charlotte A.; Liu, Yi Hui Angella; Barba, Evan – Child Development, 2020
Children's math learning (N = 217; M[subscript age] = 4.87 years; 63% European American, 96% college-educated families) from an intelligent character game was examined via social meaningfulness (parasocial relationships [PSRs]) and social contingency (parasocial interactions, e.g., math talk). In three studies (data collected in the DC area:…
Descriptors: Young Children, Mathematics Skills, Computer Games, Play
Hou, Xinying; Nguyen, Huy Anh; Richey, J. Elizabeth; Harpstead, Erik; Hammer, Jessica; McLaren, Bruce M. – International Journal of Artificial Intelligence in Education, 2022
Digital learning games are designed to foster both student learning and enjoyment. Given this goal, an interesting research topic is whether game mechanics that promote learning and those that promote enjoyment have different effects on students' experience and learning performance. We explored these questions in "Decimal Point," a…
Descriptors: Models, Learner Engagement, Computer Games, Educational Games
Seyedahmad Rahimi; Justice T. Walker; Lin Lin-Lipsmeyer; Jinnie Shin – Creativity Research Journal, 2024
Digital sandbox games such as "Minecraft" can be used to assess and support creativity. Doing so, however, requires an understanding of what is deemed creative in this game context. One approach is to understand how Minecrafters describe creativity in their communities, and how much those descriptions overlap with the established…
Descriptors: Creativity, Video Games, Computer Games, Evaluation Methods
Codebook Co-Development to Understand Fidelity and Initiate Artificial Intelligence in Serious Games
Ravyse, Werner Siegfried; Blugnaut, A. Seugnet; Botha-Ravyse, Chrisna R. – International Journal of Game-Based Learning, 2020
This study aimed to identify and rank the serious game fidelity themes that should be considered for retaining both the learning potential and predicted market growth of serious games. The authors also investigated existing links between fidelity and AI. The methodology unraveled serious game fidelity through the co-development of a theory- and…
Descriptors: Fidelity, Artificial Intelligence, Educational Games, Computer Games
Sinem Aslan; Lenitra M. Durham; Nese Alyuz; Rebecca Chierichetti; Pete A. Denman; Eda Okur; David I. Gonzalez Aguirre; Julio C. Zamora Esquivel; Hector A. Cordourier Maruri; Sangita Sharma; Giuseppe Raffa; Richard E. Mayer; Lama Nachman – British Journal of Educational Technology, 2024
Previous research showed that the parents acknowledged the technology's benefits for their young children's learning, however, they are still worried about the extended screen time, lack of physical activity and lack of social interactions. To address these concerns, we developed Kid Space to enable pedagogically appropriate technology use for…
Descriptors: Parents, Young Children, Artificial Intelligence, Interpersonal Communication
Kun Huang; Ching-Huei Chen – Journal of Computer Assisted Learning, 2025
Background: Digital game-based learning (DGBL) has shown promise in enhancing learning and motivation, with appropriate scaffolding playing a crucial role in facilitating student inquiries and knowledge acquisition through science games. While scaffolding is generally effective in promoting learning in DGBL, there is variability among different…
Descriptors: Video Technology, Educational Technology, Artificial Intelligence, Computer Mediated Communication
Ju, Song; Zhou, Guojing; Barnes, Tiffany; Chi, Min – International Educational Data Mining Society, 2020
Identifying critical decisions is one of the most challenging decision-making problems in real-world applications. In this work, we propose a novel Reinforcement Learning (RL) based Long-Short Term Rewards (LSTR) framework for critical decisions identification. RL is a machine learning area concerning with inducing effective decision-making…
Descriptors: Decision Making, Reinforcement, Artificial Intelligence, Man Machine Systems
Selcuk Acar; Denis Dumas; Peter Organisciak; Kelly Berthiaume – Grantee Submission, 2024
Creativity is highly valued in both education and the workforce, but assessing and developing creativity can be difficult without psychometrically robust and affordable tools. The open-ended nature of creativity assessments has made them difficult to score, expensive, often imprecise, and therefore impractical for school- or district-wide use. To…
Descriptors: Thinking Skills, Elementary School Students, Artificial Intelligence, Measurement Techniques
Silvervarg, Annika; Wolf, Rachel; Blair, Kristen Pilner; Haake, Magnus; Gulz, Agneta – Journal of Research on Technology in Education, 2021
Does a teachable agent influence the uptake or neglect of 'critical constructive feedback' and learning within a digital environment? 285 middle-school students engaged with a history learning game in a 2x2 study design. One dimension was inclusion of a teachable agent. Orthogonal was whether critical constructive feedback was presented…
Descriptors: Teaching Methods, Feedback (Response), Middle School Students, History Instruction
Danial Hooshyar; Nour El Mawas; Yeongwook Yang – Knowledge Management & E-Learning, 2024
The use of learner modelling approaches is critical for providing adaptive support in educational computer games, with predictive learner modelling being among the key approaches. While adaptive supports have been shown to improve the effectiveness of educational games, improperly customized support can have negative effects on learning outcomes.…
Descriptors: Artificial Intelligence, Course Content, Tests, Scores
Motejlek, Jiri; Alpay, Esat – IEEE Transactions on Learning Technologies, 2021
This article presents and analyzes existing taxonomies of virtual and augmented reality and demonstrates knowledge gaps and mixed terminology, which may cause confusion among educators, researchers, and developers. Several such occasions of confusion are presented. A methodology is then presented to construct a taxonomy of virtual reality and…
Descriptors: Taxonomy, Teaching Methods, Artificial Intelligence, Educational Objectives
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