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Chelsea Chandler; Rohit Raju; Jason G. Reitman; William R. Penuel; Monica Ko; Jeffrey B. Bush; Quentin Biddy; Sidney K. D’Mello – International Educational Data Mining Society, 2025
We investigated methods to enhance the generalizability of large language models (LLMs) designed to classify dimensions of collaborative discourse during small group work. Our research utilized five diverse datasets that spanned various grade levels, demographic groups, collaboration settings, and curriculum units. We explored different model…
Descriptors: Artificial Intelligence, Models, Natural Language Processing, Discourse Analysis
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Clayton Cohn; Surya Rayala; Caitlin Snyder; Joyce Horn Fonteles; Shruti Jain; Naveeduddin Mohammed; Umesh Timalsina; Sarah K. Burriss; Ashwin T. S.; Namrata Srivastava; Menton Deweese; Angela Eeds; Gautam Biswas – Grantee Submission, 2025
Collaborative dialogue offers rich insights into students' learning and critical thinking. This is essential for adapting pedagogical agents to students' learning and problem-solving skills in STEM+C settings. While large language models (LLMs) facilitate dynamic pedagogical interactions, potential hallucinations can undermine confidence, trust,…
Descriptors: STEM Education, Computer Science Education, Artificial Intelligence, Natural Language Processing
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Sarah Levine; Sarah W. Beck; Chris Mah; Lena Phalen; Jaylen PIttman – Journal of Adolescent & Adult Literacy, 2025
Educators and researchers are interested in ways that ChatGPT and other generative AI tools might move beyond the role of "cheatbot" and become part of the network of resources students use for writing. We studied how high school students used ChatGPT as a writing support while writing arguments about topics like school mascots. We…
Descriptors: Natural Language Processing, Artificial Intelligence, Technology Uses in Education, Writing (Composition)
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Alex Goslen; Yeo Jin Kim; Jonathan Rowe; James Lester – International Journal of Artificial Intelligence in Education, 2025
The development of large language models offers new possibilities for enhancing adaptive scaffolding of student learning in game-based learning environments. In this work, we present a novel framework for automatic plan generation that utilizes text-based representations of students' actions within a game-based learning environment, Crystal…
Descriptors: Artificial Intelligence, Natural Language Processing, Technology Uses in Education, Game Based Learning
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Abubakir Siedahmed; Jaclyn Ocumpaugh; Zelda Ferris; Dinesh Kodwani; Eamon Worden; Neil Heffernan – International Educational Data Mining Society, 2025
Recent advances in AI have opened the door for the automated scoring of open-ended math problems, which were previously much more difficult to assess at scale. However, we know that biases still remain in some of these algorithms. For example, recent research on the automated scoring of student essays has shown that certain varieties of English…
Descriptors: Artificial Intelligence, Automation, Scoring, Mathematics Tests
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Bihao Hu; Longwei Zheng; Jiayi Zhu; Lishan Ding; Yilei Wang; Xiaoqing Gu – IEEE Transactions on Learning Technologies, 2024
This study explores and analyzes the specific performance of large language models (LLMs) in instructional design, aiming to unveil their potential strengths and possible weaknesses. Recently, the influence of LLMs has gradually increased in multiple fields, yet exploratory research on their application in education remains relatively scarce. In…
Descriptors: Artificial Intelligence, Natural Language Processing, Instructional Design, Prompting
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Yucheng Chu; Peng He; Hang Li; Haoyu Han; Kaiqi Yang; Yu Xue; Tingting Li; Yasemin Copur-Gencturk; Joseph Krajcik; Jiliang Tang – International Educational Data Mining Society, 2025
Short answer assessment is a vital component of science education, allowing evaluation of students' complex three-dimensional understanding. Large language models (LLMs) that possess human-like ability in linguistic tasks are increasingly popular in assisting human graders to reduce their workload. However, LLMs' limitations in domain knowledge…
Descriptors: Artificial Intelligence, Science Education, Technology Uses in Education, Natural Language Processing
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Tolulope Famaye; Cinamon Sunrise Bailey; Ibrahim Adisa; Golnaz Arastoopour Irgens – International Journal of Technology in Education, 2024
The emergence of ChatGPT, an AI-powered language model, has sparked numerous debates and discussions. In educational research, scholars have raised significant questions regarding the potential, limitations, and ethical concerns around the use of this technology. While research on the application and implications of ChatGPT in academic settings…
Descriptors: Artificial Intelligence, High School Students, Technology Uses in Education, Student Attitudes
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Mathias Benedek; Roger E. Beaty – Journal of Creative Behavior, 2025
The PISA assessment 2022 of creative thinking was a moonshot effort that introduced significant advancements over existing creativity tests, including a broad range of domains (written, visual, social, and scientific), implementation in many languages, and sophisticated scoring methods. PISA 2022 demonstrated the general feasibility of assessing…
Descriptors: Creative Thinking, Creativity, Creativity Tests, Scoring
Kelsey Hammond; Chelsey Barber – Phi Delta Kappan, 2024
A mastery-based learning model has limited value in secondary English classrooms, particularly as it relates to writing instruction. Kelsey Hammond and Chelsey Barber argue against the focus on standardized benchmarks that are tied to mastery-based models in favor of an approach to writing that is explorative, personal, and imaginative. The rise…
Descriptors: Mastery Learning, Secondary Education, Writing Instruction, Artificial Intelligence
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Anthony G. Picciano – Online Learning, 2024
Artificial intelligence (AI) has been evolving since the mid-twentieth-century when luminaries such as Alan Turing, Herbert Simon, and Marvin Minsky began developing rudimentary AI applications. For decades, AI programs remained pretty much in the realm of computer science and experimental game playing. This changed radically in the 2020s when…
Descriptors: Teacher Education, Seminars, Technology Uses in Education, Artificial Intelligence
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Sami Baral; Eamon Worden; Wen-Chiang Lim; Zhuang Luo; Christopher Santorelli; Ashish Gurung; Neil Heffernan – Grantee Submission, 2024
The effectiveness of feedback in enhancing learning outcomes is well documented within Educational Data Mining (EDM). Various prior research have explored methodologies to enhance the effectiveness of feedback to students in various ways. Recent developments in Large Language Models (LLMs) have extended their utility in enhancing automated…
Descriptors: Automation, Scoring, Computer Assisted Testing, Natural Language Processing
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Tarek Ait Baha; Mohamed El Hajji; Youssef Es-Saady; Hammou Fadili – Education and Information Technologies, 2024
Artificial Intelligence (AI) technologies have increasingly become vital in our everyday lives. Education is one of the most visible domains in which these technologies are being used. Conversational Agents (CAs) are among the most prominent AI systems for assisting teaching and learning processes. Their integration into an e-learning system can…
Descriptors: Secondary School Students, Public Schools, Foreign Countries, Artificial Intelligence
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Owen Henkel; Zach Levoninan; Millie-Ellen Postle; Chenglu Li – International Educational Data Mining Society, 2024
For middle-school math students, interactive question-answering (QA) with tutors is an effective way to learn. The flexibility and emergent capabilities of generative large language models (LLMs) has led to a surge of interest in automating portions of the tutoring process--including interactive QA to support conceptual discussion of mathematical…
Descriptors: Middle School Mathematics, Questioning Techniques, Algebra, Geometry
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Alexandra S. Dylman; Marie-France Champoux-Larsson; Candice Frances – Educational Psychology, 2025
We report four experiments investigating the effect of prosody on listening comprehension in 11-13-year-old children. Across all experiments, participants listened to short object descriptions and answered content-based questions about said objects. In Experiments 1-3, the descriptions were read in an emotionally positive or neutral tone of voice.…
Descriptors: Intonation, Middle School Students, Foreign Countries, Listening Comprehension
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