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Showing all 12 results Save | Export
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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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Abdulla-All Mijan; Md Rabiul Hasan; Mehedi Hasan – International Journal of Technology in Education and Science, 2025
This study investigated the utilization of artificial intelligence (AI) platforms in Bangladeshi higher education institutions, with an emphasis on determining which AI platforms are most widely used and assessing the variables that affect AI adoption. The particular criteria influencing platform preferences and the comparative analysis of various…
Descriptors: Artificial Intelligence, Technology Integration, Technology Uses in Education, Foreign Countries
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Anya S. Evmenova; Kelley Regan; Reagan Mergen; Roba Hrisseh – TechTrends: Linking Research and Practice to Improve Learning, 2024
Generative AI has the potential to support teachers with writing instruction and feedback. The purpose of this study was to explore and compare feedback and data-based instructional suggestions from teachers and those generated by different AI tools. Essays from students with and without disabilities who struggled with writing and needed a…
Descriptors: Writing Instruction, Feedback (Response), Writing Difficulties, Artificial Intelligence
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Alexandra Farazouli; Teresa Cerratto-Pargman; Klara Bolander-Laksov; Cormac McGrath – Assessment & Evaluation in Higher Education, 2024
AI chatbots have recently fuelled debate regarding education practices in higher education institutions worldwide. Focusing on Generative AI and ChatGPT in particular, our study examines how AI chatbots impact university teachers' assessment practices, exploring teachers' perceptions about how ChatGPT performs in response to home examination…
Descriptors: Artificial Intelligence, Natural Language Processing, Student Evaluation, Educational Change
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Jaeho Jeon; Seongyong Lee – Education and Information Technologies, 2024
Research has demonstrated the promising potential of chatbots in education. Moreover, technological advancements, such as ChatGPT, prompted us to reexamine distinctions between pedagogical roles that humans and chatbots assume. In this context, a systematic review of 11 experimental studies on human-chatbot comparisons in language education was…
Descriptors: Artificial Intelligence, Natural Language Processing, Technology Uses in Education, Computer Assisted Instruction
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Younglong Kim; Katherine A. Curry; Ashlyn M. Fiegener – Journal of School Administration Research and Development, 2024
Educational leaders are faced with multi-faceted dilemmas that place decision-making at the heart of their day-to-day work. For support, they often turn to collaborative networks of experienced educators, such as Project ECHO, for solutions to address challenges they encounter while working in the field. The availability of generative AI…
Descriptors: Artificial Intelligence, Natural Language Processing, Barriers, Educational Practices
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Qi Lu; Yuan Yao; Longhai Xiao; Mingzhu Yuan; Jue Wang; Xinhua Zhu – Assessment & Evaluation in Higher Education, 2024
The integration of ChatGPT as a supplementary tool for writing instruction has gained traction. However, uncertainties persist regarding how ChatGPT complements teacher assessment and the overall effectiveness of this combined approach. To address this, we conducted a mixed-methods investigation involving 46 undergraduate students from a research…
Descriptors: Artificial Intelligence, Educational Technology, Natural Language Processing, Student Evaluation
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Alexander Stanoyevitch – Discover Education, 2024
Online education, while not a new phenomenon, underwent a monumental shift during the COVID-19 pandemic, pushing educators and students alike into the uncharted waters of full-time digital learning. With this shift came renewed concerns about the integrity of online assessments. Amidst a landscape rapidly being reshaped by online exam/homework…
Descriptors: Computer Assisted Testing, Student Evaluation, Artificial Intelligence, Electronic Learning
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Ted M. Clark; Ellie Anderson; Nicole M. Dickson-Karn; Comelia Soltanirad; Nicolas Tafini – Journal of Chemical Education, 2023
Student performance on open-response calculations involving acid and base solutions before and after instruction in general chemistry and analytical chemistry courses was compared with the output from the artificial intelligence chatbot ChatGPT. Applying a theoretical model of expertise for problem solving that includes problem conceptualization,…
Descriptors: Academic Achievement, College Students, College Science, Chemistry
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Bilal Khallel Younis – Online Learning, 2024
This study aims to investigate students' self-regulation skills, confidence to learn online, and perception of satisfaction and usefulness of online classes in three learning environments that integrates ChatGPT. In this study, a quasi-experiential design was used to compare three online learning environments that integrate ChatGPT (independent,…
Descriptors: Self Management, Self Esteem, Electronic Learning, Student Attitudes
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Bruno, James V.; Cahill, Aoife; Gyawali, Binod – ETS Research Report Series, 2016
We present an annotation scheme for classifying differences in the outputs of syntactic constituency parsers when a gold standard is unavailable or undesired, as in the case of texts written by nonnative speakers of English. We discuss its automated implementation and the results of a case study that uses the scheme to choose a parser best suited…
Descriptors: Documentation, Classification, Differences, Syntax
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Allen, Laura K.; Crossley, Scott A.; McNamara, Danielle S. – Grantee Submission, 2015
We investigated linguistic factors that relate to misalignment between students' and teachers' ratings of essay quality. Students (n = 126) wrote essays and rated the quality of their work. Teachers then provided their own ratings of the essays. Results revealed that students who were less accurate in their self-assessments produced essays that…
Descriptors: Essays, Scores, Natural Language Processing, Interrater Reliability