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Showing 1 to 15 of 84 results Save | Export
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Julian F. Lohmann; Fynn Junge; Jens Möller; Johanna Fleckenstein; Ruth Trüb; Stefan Keller; Thorben Jansen; Andrea Horbach – International Journal of Artificial Intelligence in Education, 2025
Recent investigations in automated essay scoring research imply that hybrid models, which combine feature engineering and the powerful tools of deep neural networks (DNNs), reach state-of-the-art performance. However, most of these findings are from holistic scoring tasks. In the present study, we use a total of four prompts from two different…
Descriptors: Artificial Intelligence, Natural Language Processing, Essays, Writing Evaluation
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Ngoc My Bui; Jessie S. Barrot – Education and Information Technologies, 2025
With the generative artificial intelligence (AI) tool's remarkable capabilities in understanding and generating meaningful content, intriguing questions have been raised about its potential as an automated essay scoring (AES) system. One such tool is ChatGPT, which is capable of scoring any written work based on predefined criteria. However,…
Descriptors: Artificial Intelligence, Natural Language Processing, Technology Uses in Education, Automation
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Qinghao Guan; Yangxi Han – Innovations in Education and Teaching International, 2025
As generative AI (GenAI) continues to permeate academia, distinguishing between student-authored essays and those by Large Language Models (LLMs) becomes crucial for maintaining academic integrity. This study conducted a survey on the ethical awareness of using generative AI tools among a group of STEM students (n=156). Also, we empirically…
Descriptors: Foreign Countries, College Students, Artificial Intelligence, Intelligent Tutoring Systems
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Kirkwood Adams; Maria G. Baker – Thresholds in Education, 2025
In response to (1) studies finding that essay feedback generated by ChatGPT might be useful for student writers and (2) studies observing ChatGPT's tendency to adhere to narrow genre definitions when producing writing, our study seeks to examine whether ChatGPT can provide useful feedback in a first-year writing learning environment that targets a…
Descriptors: Freshman Composition, Artificial Intelligence, Man Machine Systems, Natural Language Processing
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Aneet Dharmavaram Narendranath; Jeffrey S. Allen – Journal of STEM Education: Innovations and Research, 2023
Investigations on the utility of reflective essays in engineering education are firmly grounded in the theory of metacognition. Besides being a tool to measure metacognition, insight acquired from assessing reflective essays can provide context for future instruction and assessment. Some challenges that hinder the critical assessment of reflective…
Descriptors: Engineering, Essays, Reflection, Information Retrieval
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Haerim Hwang – Written Communication, 2025
The use of subordination enables language users to achieve syntactic efficiency by allowing them to connect ideas in temporal/logical relation. Although the importance of subordination has been recognized in previous research on second language (L2) writing, it has been typically assessed with global indices that measure overall ratio of…
Descriptors: Second Language Learning, Writing (Composition), Form Classes (Languages), Syntax
Stefan Ruseti; Ionut Paraschiv; Mihai Dascalu; Danielle S. McNamara – Grantee Submission, 2024
Automated Essay Scoring (AES) is a well-studied problem in Natural Language Processing applied in education. Solutions vary from handcrafted linguistic features to large Transformer-based models, implying a significant effort in feature extraction and model implementation. We introduce a novel Automated Machine Learning (AutoML) pipeline…
Descriptors: Computer Assisted Testing, Scoring, Automation, Essays
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Stefan Ruseti; Ionut Paraschiv; Mihai Dascalu; Danielle S. McNamara – International Journal of Artificial Intelligence in Education, 2024
Automated Essay Scoring (AES) is a well-studied problem in Natural Language Processing applied in education. Solutions vary from handcrafted linguistic features to large Transformer-based models, implying a significant effort in feature extraction and model implementation. We introduce a novel Automated Machine Learning (AutoML) pipeline…
Descriptors: Computer Assisted Testing, Scoring, Automation, Essays
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Adam B. Lockwood; Joshua Castleberry – Contemporary School Psychology, 2025
Technological Advances in Artificial Intelligence (AI) have Brought forth the Potential for Models to Assist in Academic Writing. However, Concerns Regarding the Accuracy, Reliability, and Impact of AI in Academic Writing have been Raised. This Study Examined the Capabilities of GPT-4, a state-of-the-art AI Language Model, in Writing an American…
Descriptors: Artificial Intelligence, Natural Language Processing, Technology Uses in Education, Writing (Composition)
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Maira Klyshbekova; Pamela Abbott – Electronic Journal of e-Learning, 2024
There is a current debate about the extent to which ChatGPT, a natural language AI chatbot, can disrupt processes in higher education settings. The chatbot is capable of not only answering queries in a human-like way within seconds but can also provide long tracts of texts which can be in the form of essays, emails, and coding. In this study, in…
Descriptors: Artificial Intelligence, Higher Education, Technology Uses in Education, Evaluation Methods
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Dadi Ramesh; Suresh Kumar Sanampudi – European Journal of Education, 2024
Automatic essay scoring (AES) is an essential educational application in natural language processing. This automated process will alleviate the burden by increasing the reliability and consistency of the assessment. With the advances in text embedding libraries and neural network models, AES systems achieved good results in terms of accuracy.…
Descriptors: Scoring, Essays, Writing Evaluation, Memory
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Mickie De Wet; Margarita Oja Da Silva; René Bohnsack – Innovations in Education and Teaching International, 2025
This study explores the use of large language models (LLMs) to generate feedback on essay-type assignments in Higher Education. Drawing on a seminal feedback framework, it examines the pedagogical and psychological effectiveness of LLM-generated feedback across three cohorts of MBA, MSc, and undergraduate students. Methods included linguistic…
Descriptors: Higher Education, College Students, Artificial Intelligence, Writing Evaluation
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Tal Waltzer; Celeste Pilegard; Gail D. Heyman – International Journal for Educational Integrity, 2024
The release of ChatGPT in 2022 has generated extensive speculation about how Artificial Intelligence (AI) will impact the capacity of institutions for higher learning to achieve their central missions of promoting learning and certifying knowledge. Our main questions were whether people could identify AI-generated text and whether factors such as…
Descriptors: Artificial Intelligence, Man Machine Systems, Natural Language Processing, College Students
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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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Rakovic, Mladen; Iqbal, Sehrish; Li, Tongguang; Fan, Yizhou; Singh, Shaveen; Surendrannair, Surya; Kilgour, Jonathan; Graaf, Joep; Lim, Lyn; Molenaar, Inge; Bannert, Maria; Moore, Johanna; Gaševic, Dragan – Journal of Computer Assisted Learning, 2023
Background: Assignments that involve writing based on several texts are challenging to many learners. Formative feedback supporting learners in these tasks should be informed by the characteristics of evolving written product and by the characteristics of learning processes learners enacted while developing the product. However, formative feedback…
Descriptors: Artificial Intelligence, Essays, High Achievement, Writing Achievement
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