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Suping Yi; Wayan Sintawati; Yibing Zhang – Journal of Computer Assisted Learning, 2025
Background: Natural language processing (NLP) and machine learning technologies offer significant advantages, such as facilitating the delivery of reflective feedback in collaborative learning environments while minimising technical constraints for educators related to time and location. Recently, scholars' interest in reflective feedback has…
Descriptors: Reflection, Feedback (Response), Cooperative Learning, Natural Language Processing
Dorottya Demszky; Jing Liu; Heather C. Hill; Shyamoli Sanghi; Ariel Chung – Annenberg Institute for School Reform at Brown University, 2023
While recent studies have demonstrated the potential of automated feedback to enhance teacher instruction in virtual settings, its efficacy in traditional classrooms remains unexplored. In collaboration with TeachFX, we conducted a pre-registered randomized controlled trial involving 523 Utah mathematics and science teachers to assess the impact…
Descriptors: Elementary Secondary Education, Mathematics Teachers, Science Teachers, Automation
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
Phung, Tung; Cambronero, José; Gulwani, Sumit; Kohn, Tobias; Majumdarm, Rupak; Singla, Adish; Soares, Gustavo – International Educational Data Mining Society, 2023
Large language models (LLMs), such as Codex, hold great promise in enhancing programming education by automatically generating feedback for students. We investigate using LLMs to generate feedback for fixing syntax errors in Python programs, a key scenario in introductory programming. More concretely, given a student's buggy program, our goal is…
Descriptors: Computational Linguistics, Feedback (Response), Programming, Computer Science Education
Demszky, Dorottya; Liu, Jing; Hill, Heather C.; Jurafsky, Dan; Piech, Chris – Annenberg Institute for School Reform at Brown University, 2021
Providing consistent, individualized feedback to teachers is essential for improving instruction but can be prohibitively resource intensive in most educational contexts. We develop an automated tool based on natural language processing to give teachers feedback on their uptake of student contributions, a high-leverage teaching practice that…
Descriptors: Automation, Feedback (Response), Online Courses, Teaching Methods
Tzu-Yu Tai; Howard Hao-Jan Chen – Computer Assisted Language Learning, 2024
English speaking is considered the most difficult and anxiety-provoking language skill for EFL learners due to lack of access to authentic language use, fear of making mistakes, and peers' negative comments. With automatic speech recognition and natural language processing, intelligent personal assistants (IPAs) have potential in foreign language…
Descriptors: English (Second Language), Speech Communication, English Language Learners, Anxiety
Silvia García-Méndez; Francisco de Arriba-Pérez; Francisco J. González-Castaño – International Association for Development of the Information Society, 2023
Mobile learning or mLearning has become an essential tool in many fields in this digital era, among the ones educational training deserves special attention, that is, applied to both basic and higher education towards active, flexible, effective high-quality and continuous learning. However, despite the advances in Natural Language Processing…
Descriptors: Higher Education, Artificial Intelligence, Computer Software, Usability
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
Shabnam Behzad – ProQuest LLC, 2024
Second language learners constitute a significant and expanding portion of the global population and there is a growing demand for tools that facilitate language learning and instruction across various levels and in different countries. The development of large language models (LLMs) has brought about a significant impact on the domains of natural…
Descriptors: Artificial Intelligence, Computer Software, Computational Linguistics, Second Language Learning
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
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
Liu, Chengyuan; Cui, Jialin; Shang, Ruixuan; Xiao, Yunkai; Jia, Qinjin; Gehringer, Edward – International Educational Data Mining Society, 2022
An online peer-assessment system typically allows students to give textual feedback to their peers, with the goal of helping the peers improve their work. The amount of help that students receive is highly dependent on the quality of the reviews. Previous studies have investigated using machine learning to detect characteristics of reviews (e.g.,…
Descriptors: Peer Evaluation, Feedback (Response), Computer Mediated Communication, Teaching Methods
Dorottya Demszky; Heather C. Hill; Eric S. Taylor; Ashlee Kupor; Deepak Varuvel Dennison; Chris Piech – Annenberg Institute for School Reform at Brown University, 2025
The role of teacher agency in professional learning has been the subject of several qualitative studies but has not yet been tested in an experimental setting. To provide causal evidence of the impact of teacher agency on the effectiveness of professional learning, we conducted a preregistered randomized controlled trial in an online computer…
Descriptors: Professional Autonomy, Faculty Development, Attribution Theory, Online Courses
Nicula, Bogdan; Dascalu, Mihai; Newton, Natalie; Orcutt, Ellen; McNamara, Danielle S. – Grantee Submission, 2021
The ability to automatically assess the quality of paraphrases can be very useful for facilitating literacy skills and providing timely feedback to learners. Our aim is twofold: a) to automatically evaluate the quality of paraphrases across four dimensions: lexical similarity, syntactic similarity, semantic similarity and paraphrase quality, and…
Descriptors: Phrase Structure, Networks, Semantics, Feedback (Response)
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