NotesFAQContact Us
Collection
Advanced
Search Tips
Audience
Laws, Policies, & Programs
What Works Clearinghouse Rating
Showing 1 to 15 of 26 results Save | Export
Peer reviewed Peer reviewed
Direct linkDirect link
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
Peer reviewed Peer reviewed
Direct linkDirect link
Mary Rice; Nicholas DePascal; Joaquín T. Argüello de Jesús; Helen McFeely; Amy Traylor; Lehman Heaviland – Professional Development in Education, 2025
With the introduction of artificial intelligence (AI), particularly Generative AI (GenAI) to school settings, teachers are likely to be drawn into professional learning scenarios where they will be expected to learn how to use programs and applications for remediation and tutoring of children. Previous research highlights how professional learning…
Descriptors: Artificial Intelligence, Man Machine Systems, Natural Language Processing, Technology Uses in Education
Peer reviewed Peer reviewed
Direct linkDirect link
Kai Guo; Deliang Wang – Education and Information Technologies, 2024
ChatGPT, the newest pre-trained large language model, has recently attracted unprecedented worldwide attention. Its exceptional performance in understanding human language and completing a variety of tasks in a conversational way has led to heated discussions about its implications for and use in education. This exploratory study represents one of…
Descriptors: Feedback (Response), English (Second Language), Artificial Intelligence, Natural Language Processing
Hong Jiao, Editor; Robert W. Lissitz, Editor – IAP - Information Age Publishing, Inc., 2024
With the exponential increase of digital assessment, different types of data in addition to item responses become available in the measurement process. One of the salient features in digital assessment is that process data can be easily collected. This non-conventional structured or unstructured data source may bring new perspectives to better…
Descriptors: Artificial Intelligence, Natural Language Processing, Psychometrics, Computer Assisted Testing
Peer reviewed Peer reviewed
Direct linkDirect link
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
Peer reviewed Peer reviewed
Direct linkDirect link
Waad Alsaweed; Saad Aljebreen – International Journal of Computer-Assisted Language Learning and Teaching, 2024
Artificial intelligence revolution becomes a trend in most aspects of life. ChatGPT, an AI chatbot, has impacted various domains, including education and language learning. Enhancing writing abilities of ESL learners requires frequent writing practice and feedback, which ChatGPT can easily provide. However, ChatGPT's accuracy in identifying and…
Descriptors: Error Correction, Writing Instruction, Grammar, Morphemes
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Qian Du; Tamara Tate – CATESOL Journal, 2024
ChatGPT has been at the center of media coverage since its public release at the end of 2022. Given ChatGPT's capacity for generating human-like text on a wide range of subjects, it is not surprising that educators, especially those who teach writing, have raised concerns regarding the implications of generative AI tools on issues of plagiarism…
Descriptors: Artificial Intelligence, Natural Language Processing, Technology Uses in Education, Plagiarism
Peer reviewed Peer reviewed
Direct linkDirect link
Daisuke Akiba; Rebecca Garte – Journal of Interactive Learning Research, 2024
The emergence of AI-powered Large Language Models (LLMs), such as ChatGPT and Google Gemini, presents both opportunities and challenges for higher education, particularly regarding academic integrity in writing instruction. This exploratory study examines a novel pedagogical approach that integrates LLMs as required feedback tools in a…
Descriptors: Artificial Intelligence, Technology Uses in Education, Writing Instruction, Integrity
Peer reviewed Peer reviewed
Direct linkDirect link
Davies, Patricia Marybelle; Passonneau, Rebecca Jane; Muresan, Smaranda; Gao, Yanjun – IEEE Transactions on Education, 2022
Contribution: Demonstrates how to use experiential learning (EL) to improve argumentative writing. Presents the design and development of a natural language processing (NLP) application for aiding instructors in providing feedback on student essays. Discusses how EL combined with automated support provides an analytical approach to improving…
Descriptors: Experiential Learning, Writing Instruction, Persuasive Discourse, Writing (Composition)
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Xiaoling Bai; Nur Rasyidah Mohd Nordin – Eurasian Journal of Applied Linguistics, 2025
A perfect writing skill has been deemed instrumental to achieving competence in EFL, yet it is considered one of the most impressive learning domains. This study investigates the impact of human-AI collaborative feedback on the writing proficiency of EFL students. It examines key teaching domains, including the teaching environment, teacher…
Descriptors: Artificial Intelligence, Feedback (Response), Evaluators, Writing Skills
Peer reviewed Peer reviewed
Direct linkDirect link
Ranalli, Jim; Yamashita, Taichi – Language Learning & Technology, 2022
To the extent automated written corrective feedback (AWCF) tools such as Grammarly are based on sophisticated error-correction technologies, such as machine-learning techniques, they have the potential to find and correct more common L2 error types than simpler spelling and grammar checkers such as the one included in Microsoft Word (technically…
Descriptors: Error Correction, Feedback (Response), Computer Software, Second Language Learning
Peer reviewed Peer reviewed
Direct linkDirect link
Stephanie Link; Robert Redmon; Yaser Shamsi; Martin Hagan – CALICO Journal, 2024
Artificial intelligence (AI) for supporting second language (L2) writing processes and practices has garnered increasing interest in recent years, establishing AI-mediated L2 writing as a new norm for many multilingual classrooms. As such, the emergence of AI-mediated technologies has challenged L2 writing instructors and their philosophies…
Descriptors: English for Academic Purposes, Teaching Methods, Second Language Learning, Second Language Instruction
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Miranty, Delsa; Widiati, Utami – Pegem Journal of Education and Instruction, 2021
Automated Writing Evaluation (AWE) has been considered a potential pedagogical technique that exploits technology to assist the students' writing. However, little attention has been devoted to examining students' perceptions of Grammarly use in higher education context. This paper aims to obtain information regarding the writing process and the…
Descriptors: Foreign Countries, Technology Uses in Education, Writing (Composition), Student Attitudes
Peer reviewed Peer reviewed
Direct linkDirect link
Sari, Elif; Han, Turgay – Reading Matrix: An International Online Journal, 2021
Providing both effective feedback applications and reliable assessment practices are two central issues in ESL/EFL writing instruction contexts. Giving individual feedback is very difficult in crowded classes as it requires a great amount of time and effort for instructors. Moreover, instructors likely employ inconsistent assessment procedures,…
Descriptors: Automation, Writing Evaluation, Artificial Intelligence, Natural Language Processing
Peer reviewed Peer reviewed
Direct linkDirect link
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
Previous Page | Next Page »
Pages: 1  |  2