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Lachlan McGinness; Peter Baumgartner – Physical Review Physics Education Research, 2025
This paper explores the potential of large language models to accurately extract and translate equations from typed student responses into a standard format. This is a useful task as standardized equations can be graded reliably using a computer algebra system or a satisfiability modulo theories solver. Therefore physics instructors interested in…
Descriptors: Artificial Intelligence, Computer Uses in Education, Physics, Grading
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Benjamin Goecke; Paul V. DiStefano; Wolfgang Aschauer; Kurt Haim; Roger Beaty; Boris Forthmann – Journal of Creative Behavior, 2024
Automated scoring is a current hot topic in creativity research. However, most research has focused on the English language and popular verbal creative thinking tasks, such as the alternate uses task. Therefore, in this study, we present a large language model approach for automated scoring of a scientific creative thinking task that assesses…
Descriptors: Creativity, Creative Thinking, Scoring, Automation
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Sandra McKeown; Zuhaib M. Mir – Research Synthesis Methods, 2024
Searching multiple resources to locate eligible studies for research syntheses can result in hundreds to thousands of duplicate references that should be removed before the screening process for efficiency. Research investigating the performance of automated methods for deduplicating references via reference managers and systematic review software…
Descriptors: Literature Reviews, Evaluation, Followup Studies, Automation
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Yi Xue – Education and Information Technologies, 2024
The new era of generative artificial intelligence has sparked the blossoming academic fireworks in the realm of education and information technologies. Driven by natural language processing (NLP), automated writing evaluation (AWE) tools become a ubiquitous practice in intelligent computer-assisted language learning (CALL) environments. Based on…
Descriptors: Literature Reviews, Meta Analysis, Bibliometrics, Artificial Intelligence
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Victoria Kishchak; Anna Ewert; Paulina Halczak; Pawel Kleka; Marcin Szczerbinski – Reading and Writing: An Interdisciplinary Journal, 2024
RAN (Rapid Automatized Naming) is known to be a robust predictor of reading development in different languages. Much less is known about RAN predictive power in bilingual contexts. This is the first meta-analysis of research with bilingual children, assessing the strength of the RAN-reading relationship both within and across languages. It also…
Descriptors: Automation, Naming, Meta Analysis, Bilingualism
Takashi Yamashita; Donnette Narine; Runcie C. W. Chidebe; Jenna W. Kramer; Rita Karam; Phyllis A. Cummins; Thomas J. Smith – Grantee Submission, 2024
Background and Objective: Advancing automation technologies are replacing certain occupations such as those involving simple food preparation more than occupations such as those in STEM fields (e.g., engineering, health care). Older workers generally face higher job automation risks in part due to their lower levels of digital skills. A better…
Descriptors: Digital Literacy, STEM Careers, Automation, Older Workers
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Mohammad Arif Ul Alam; Madhavi Pagare; Susan Davis; Geeta Verma; Ashis Biswas; Justin Barbern – International Educational Data Mining Society, 2024
Recognizing the Social Determinants of Mental Health (SDMHs) among students is essential, as lower backgrounds in these determinants elevate the risk of poor academic achievement, behavioral issues, and physical health problems, thereby affecting both physical and emotional well-being. Leveraging students' self-reported lived experiential essays…
Descriptors: Mental Health, At Risk Students, Prediction, Automation
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Kebede, Mihiretu M.; Le Cornet, Charlotte; Fortner, Renée Turzanski – Research Synthesis Methods, 2023
We aimed to evaluate the performance of supervised machine learning algorithms in predicting articles relevant for full-text review in a systematic review. Overall, 16,430 manually screened titles/abstracts, including 861 references identified relevant for full-text review were used for the analysis. Of these, 40% (n = 6573) were sub-divided for…
Descriptors: Automation, Literature Reviews, Artificial Intelligence, Algorithms
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Ben Babcock; Kim Brunnert – Journal of Applied Testing Technology, 2023
Automatic Item Generation (AIG) is an extremely useful tool to construct many high-quality exam items more efficiently than traditional item writing methods. A large pool of items, however, presents challenges like identifying a particular item to meet a specific need. For example, when making a fixed form exam, best practices forbid item stems…
Descriptors: Test Items, Automation, Algorithms, Artificial Intelligence
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Ishaya Gambo; Faith-Jane Abegunde; Omobola Gambo; Roseline Oluwaseun Ogundokun; Akinbowale Natheniel Babatunde; Cheng-Chi Lee – Education and Information Technologies, 2025
The current educational system relies heavily on manual grading, posing challenges such as delayed feedback and grading inaccuracies. Automated grading tools (AGTs) offer solutions but come with limitations. To address this, "GRAD-AI" is introduced, an advanced AGT that combines automation with teacher involvement for precise grading,…
Descriptors: Automation, Grading, Artificial Intelligence, Computer Assisted Testing
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Hanshu Zhang; Ran Zhou; Cheng-You Cheng; Sheng-Hsu Huang; Ming-Hui Cheng; Cheng-Ta Yang – Cognitive Research: Principles and Implications, 2025
Although it is commonly believed that automation aids human decision-making, conflicting evidence raises questions about whether individuals would gain greater advantages from automation in difficult tasks. Our study examines the combined influence of task difficulty and automation reliability on aided decision-making. We assessed decision…
Descriptors: Task Analysis, Difficulty Level, Decision Making, Automation
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Simon Kitto; H. L. Michelle Chiang; Olivia Ng; Jennifer Cleland – Advances in Health Sciences Education, 2025
There is a long-standing lack of learner satisfaction with quality and quantity of feedback in health professions education (HPE) and training. To address this, university and training programmes are increasingly using technological advancements and data analytic tools to provide feedback. One such educational technology is the Learning Analytic…
Descriptors: Feedback (Response), Learning Analytics, Educational Technology, Allied Health Occupations Education
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Danielle Lottridge; Davis Dimalen; Gerald Weber – ACM Transactions on Computing Education, 2025
Automated assessment is well-established within computer science courses but largely absent from human--computer interaction courses. Automating the assessment of human--computer interaction (HCI) is challenging because the coursework tends not to be computational but rather highly creative, such as designing and implementing interactive…
Descriptors: Computer Science Education, Computer Assisted Testing, Automation, Man Machine Systems
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Erik Voss – Language Testing, 2025
An increasing number of language testing companies are developing and deploying deep learning-based automated essay scoring systems (AES) to replace traditional approaches that rely on handcrafted feature extraction. However, there is hesitation to accept neural network approaches to automated essay scoring because the features are automatically…
Descriptors: Artificial Intelligence, Automation, Scoring, English (Second Language)
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Harpreet Auby; Namrata Shivagunde; Vijeta Deshpande; Anna Rumshisky; Milo D. Koretsky – Journal of Engineering Education, 2025
Background: Analyzing student short-answer written justifications to conceptually challenging questions has proven helpful to understand student thinking and improve conceptual understanding. However, qualitative analyses are limited by the burden of analyzing large amounts of text. Purpose: We apply dense and sparse Large Language Models (LLMs)…
Descriptors: Student Evaluation, Thinking Skills, Test Format, Cognitive Processes
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