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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
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
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
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
Wesley Morris; Scott Crossley; Langdon Holmes; Chaohua Ou; Mihai Dascalu; Danielle McNamara – International Journal of Artificial Intelligence in Education, 2025
As intelligent textbooks become more ubiquitous in classrooms and educational settings, the need to make them more interactive arises. An alternative is to ask students to generate knowledge in response to textbook content and provide feedback about the produced knowledge. This study develops Natural Language Processing models to automatically…
Descriptors: Formative Evaluation, Feedback (Response), Textbooks, Artificial Intelligence
Hunkoog Jho; Minsu Ha – Journal of Baltic Science Education, 2024
This study aimed at examining the performance of generative artificial intelligence to extract argumentation elements from text. Thus, the researchers developed a web-based framework to provide automated assessment and feedback relying on a large language model, ChatGPT. The results produced by ChatGPT were compared to human experts across…
Descriptors: Feedback (Response), Artificial Intelligence, Persuasive Discourse, Models
Suna-Seyma Uçar; Itziar Aldabe; Nora Aranberri; Ana Arruarte – International Journal of Artificial Intelligence in Education, 2024
Current student-centred, multilingual, active teaching methodologies require that teachers have continuous access to texts that are adequate in terms of topic and language competence. However, the task of finding appropriate materials is arduous and time consuming for teachers. To build on automatic readability assessment research that could help…
Descriptors: Artificial Intelligence, Technology Uses in Education, Automation, Readability
Paul Deane; Duanli Yan; Katherine Castellano; Yigal Attali; Michelle Lamar; Mo Zhang; Ian Blood; James V. Bruno; Chen Li; Wenju Cui; Chunyi Ruan; Colleen Appel; Kofi James; Rodolfo Long; Farah Qureshi – ETS Research Report Series, 2024
This paper presents a multidimensional model of variation in writing quality, register, and genre in student essays, trained and tested via confirmatory factor analysis of 1.37 million essay submissions to ETS' digital writing service, Criterion®. The model was also validated with several other corpora, which indicated that it provides a…
Descriptors: Writing (Composition), Essays, Models, Elementary School Students
C. H., Dhawaleswar Rao; Saha, Sujan Kumar – IEEE Transactions on Learning Technologies, 2023
Multiple-choice question (MCQ) plays a significant role in educational assessment. Automatic MCQ generation has been an active research area for years, and many systems have been developed for MCQ generation. Still, we could not find any system that generates accurate MCQs from school-level textbook contents that are useful in real examinations.…
Descriptors: Multiple Choice Tests, Computer Assisted Testing, Automation, Test Items
Fengkai Liu; Yishi Jiang; Chun Lai; Tan Jin – Language Learning & Technology, 2024
Differentiated instruction is much demanded yet quite challenging in face of the growing student diversity in today's K-12 classrooms. One major challenge is the provision of differentiated materials to students. Automated text simplification (ATS) tools fueled by natural language processing may serve as a useful assistant for teachers. However,…
Descriptors: Automation, Individualized Instruction, Natural Language Processing, Technology Uses in Education
Abhijit Suresh – ProQuest LLC, 2022
Over the past decade, robust literature focused on teacher "talk moves" that promote student argumentation has emerged, especially in mathematics education. Teachers and students can use talk moves to construct conversations in which students share their thinking, actively consider the ideas of others, and engage in sustained reasoning.…
Descriptors: Automation, Feedback (Response), Teacher Effectiveness, Discourse Modes
Say What? Automatic Modeling of Collaborative Problem Solving Skills from Student Speech in the Wild
Pugh, Samuel L.; Subburaj, Shree Krishna; Rao, Arjun Ramesh; Stewart, Angela E. B.; Andrews-Todd, Jessica; D'Mello, Sidney K. – International Educational Data Mining Society, 2021
We investigated the feasibility of using automatic speech recognition (ASR) and natural language processing (NLP) to classify collaborative problem solving (CPS) skills from recorded speech in noisy environments. We analyzed data from 44 dyads of middle and high school students who used videoconferencing to collaboratively solve physics and math…
Descriptors: Problem Solving, Cooperation, Middle School Students, High School Students
Wilson, Joshua; Roscoe, Rod D. – Journal of Educational Computing Research, 2020
The present study extended research on the effectiveness of automated writing evaluation (AWE) systems. Sixth graders were randomly assigned by classroom to an AWE condition that used "Project Essay Grade Writing" (n = 56) or a word-processing condition that used Google Docs (n = 58). Effectiveness was evaluated using multiple metrics:…
Descriptors: Automation, Writing Evaluation, Feedback (Response), Instructional Effectiveness
Hu, Xiao – Learning: Research and Practice, 2017
Despite the rapid development in the area of learning analytics (LA), there is comparatively little focused towards the secondary level of education. This ongoing work presents the latest developed function of Wikiglass, an LA tool designed for automatically recognising, aggregating, and visualising levels of thinking orders in student…
Descriptors: Secondary School Students, Data Analysis, Learning, Automation
Erkens, Melanie; Bodemer, Daniel; Hoppe, H. Ulrich – International Journal of Computer-Supported Collaborative Learning, 2016
Orchestrating collaborative learning in the classroom involves tasks such as forming learning groups with heterogeneous knowledge and making learners aware of the knowledge differences. However, gathering information on which the formation of appropriate groups and the creation of graphical knowledge representations can be based is very effortful…
Descriptors: Cooperative Learning, Information Retrieval, Data Collection, Data Analysis
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