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Anson, Chris M. – Composition Studies, 2022
Student plagiarism has challenged educators for decades, with heightened paranoia following the advent of the Internet in the 1980's and ready access to easily copied text. But plagiarism will look like child's play next to new developments in AI-based natural-language processing (NLP) systems that increasingly appear to "write" as…
Descriptors: Plagiarism, Artificial Intelligence, Natural Language Processing, Writing Assignments
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Andrew Williams – International Journal of Educational Technology in Higher Education, 2024
The value of generative AI tools in higher education has received considerable attention. Although there are many proponents of its value as a learning tool, many are concerned with the issues regarding academic integrity and its use by students to compose written assessments. This study evaluates and compares the output of three commonly used…
Descriptors: Content Area Writing, Artificial Intelligence, Writing Assignments, Biomedicine
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Nguyen, Huy; Xiong, Wenting; Litman, Diane – International Journal of Artificial Intelligence in Education, 2017
A peer-review system that automatically evaluates and provides formative feedback on free-text feedback comments of students was iteratively designed and evaluated in college and high-school classrooms. Classroom assignments required students to write paper drafts and submit them to a peer-review system. When student peers later submitted feedback…
Descriptors: Computer Uses in Education, Computer Mediated Communication, Feedback (Response), Peer Evaluation
Allen, Laura K.; Snow, Erica L.; McNamara, Danielle S. – Grantee Submission, 2016
A commonly held belief among educators, researchers, and students is that high-quality texts are easier to read than low-quality texts, as they contain more engaging narrative and story-like elements. Interestingly, these assumptions have typically failed to be supported by the literature on writing. Previous research suggests that higher quality…
Descriptors: Role, Writing (Composition), Natural Language Processing, Hypothesis Testing
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Allen, Laura K.; Snow, Erica L.; McNamara, Danielle S. – Journal of Educational Psychology, 2016
A commonly held belief among educators, researchers, and students is that high-quality texts are easier to read than low-quality texts, as they contain more engaging narrative and story-like elements. Interestingly, these assumptions have typically failed to be supported by the literature on writing. Previous research suggests that higher quality…
Descriptors: Role, Writing (Composition), Natural Language Processing, Hypothesis Testing
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Crossley, Scott A.; Varner, Laura K.; Roscoe, Rod D.; McNamara, Danielle S. – Grantee Submission, 2013
We present an evaluation of the Writing Pal (W-Pal) intelligent tutoring system (ITS) and the W-Pal automated writing evaluation (AWE) system through the use of computational indices related to text cohesion. Sixty-four students participated in this study. Each student was assigned to either the W-Pal ITS condition or the W-Pal AWE condition. The…
Descriptors: Intelligent Tutoring Systems, Automation, Writing Evaluation, Writing Assignments