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Sang-Gu Kang – Journal of Pan-Pacific Association of Applied Linguistics, 2023
Generative AIs such as Google Bard are known to be equipped with techniques and grammatical principles of human language based on a large corpus of text and code that allow them to generate natural-sounding language, and also identify and correct grammatical errors in human-written texts. Still, they are not perfect language generators, and this…
Descriptors: Artificial Intelligence, Natural Language Processing, Error Correction, Writing (Composition)
Schneider, Johannes; Richner, Robin; Riser, Micha – International Journal of Artificial Intelligence in Education, 2023
Autograding short textual answers has become much more feasible due to the rise of NLP and the increased availability of question-answer pairs brought about by a shift to online education. Autograding performance is still inferior to human grading. The statistical and black-box nature of state-of-the-art machine learning models makes them…
Descriptors: Grading, Natural Language Processing, Computer Assisted Testing, Ethics
Wai Tong Chor; Kam Meng Goh; Li Li Lim; Kin Yun Lum; Tsung Heng Chiew – Education and Information Technologies, 2024
The programme outcomes are broad statements of knowledge, skills, and competencies that the students should be able to demonstrate upon graduation from a programme, while the Educational Taxonomy classifies learning objectives into different domains. The precise mapping of a course outcomes to the programme outcome and the educational taxonomy…
Descriptors: Artificial Intelligence, Engineering Education, Taxonomy, Educational Objectives
Adnane Ez-zizi; Dagmar Divjak; Petar Milin – Language Learning, 2024
Since its first adoption as a computational model for language learning, evidence has accumulated that Rescorla-Wagner error-correction learning (Rescorla & Wagner, 1972) captures several aspects of language processing. Whereas previous studies have provided general support for the Rescorla-Wagner rule by using it to explain the behavior of…
Descriptors: Error Correction, Second Language Learning, Second Language Instruction, Gender Differences
Ahmed Magooda; Diane Litman; Ahmed Ashraf; Muhsin Menekse – Grantee Submission, 2022
Having students write reflections has been shown to help teachers improve their instruction and students improve their learning outcomes. With the aid of Natural Language Processing (NLP), real-time educational applications that can assess and provide feedback on reflection quality can be deployed. In this work, we first evaluate various NLP…
Descriptors: Undergraduate Students, Writing Assignments, Reflection, 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
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
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
Huang, Ping-Yu; Tsao, Nai-Lung – Computer Assisted Language Learning, 2021
In this article, we describe an online English collocation explorer developed to help English L2 learners produce correct and appropriate collocations. Our tool, which is able to visually represent relevant correct/incorrect collocations on a single webpage, was designed based on the notions of collocation clusters and intercollocability proposed…
Descriptors: Second Language Learning, Second Language Instruction, English (Second Language), Error Correction
Mei-Rong Alice Chen – Educational Technology & Society, 2024
The increase in popularity of Generative Artificial Intelligence Chatbots, or GACs, has created a potentially fruitful opportunity to enhance teaching English as a Foreign Language (EFL). This study investigated the possibility of using GACs to give EFL students metalinguistic guidance (MG) in linguistics courses. Language competency gaps, a lack…
Descriptors: Metacognition, Transformative Learning, English (Second Language), Artificial Intelligence
Vittorini, Pierpaolo; Menini, Stefano; Tonelli, Sara – International Journal of Artificial Intelligence in Education, 2021
Massive open online courses (MOOCs) provide hundreds of students with teaching materials, assessment tools, and collaborative instruments. The assessment activity, in particular, is demanding in terms of both time and effort; thus, the use of artificial intelligence can be useful to address and reduce the time and effort required. This paper…
Descriptors: Artificial Intelligence, Formative Evaluation, Summative Evaluation, Data
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
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
Pareja-Lora, Antonio – Research-publishing.net, 2016
For the new approaches to language e-learning (e.g. language blended learning, language autonomous learning or mobile-assisted language learning) to succeed, some automatic functions for error correction (for instance, in exercises) will have to be included in the long run in the corresponding environments and/or applications. A possible way to…
Descriptors: Electronic Learning, Automation, Error Correction, Natural Language Processing
Velez, Martin – ProQuest LLC, 2019
Software is an integral part of our lives. It controls the cars we drive every day, the ships we send into space, and even our toasters. It is everywhere and we can easily download more. Software solves many real-world problems and satisfies many needs. Thus, unsurprisingly, there is a rising demand for software engineers to maintain existing…
Descriptors: Computer Science Education, Programming, Introductory Courses, Computer Software