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Yucheng Chu; Hang Li; Kaiqi Yang; Harry Shomer; Yasemin Copur-Gencturk; Leonora Kaldaras; Kevin Haudek; Joseph Krajcik; Namsoo Shin; Hui Liu; Jiliang Tang – International Educational Data Mining Society, 2025
Open-text responses provide researchers and educators with rich, nuanced insights that multiple-choice questions cannot capture. When reliably assessed, such responses have the potential to enhance teaching and learning. However, scaling and consistently capturing these nuances remain significant challenges, limiting the widespread use of…
Descriptors: Grading, Automation, Artificial Intelligence, Natural Language Processing
Jionghao Lin; Zifei Han; Danielle R. Thomas; Ashish Gurung; Shivang Gupta; Vincent Aleven; Kenneth R. Koedinger – International Journal of Artificial Intelligence in Education, 2025
One-on-one tutoring is widely acknowledged as an effective instructional method, conditioned on qualified tutors. However, the high demand for qualified tutors remains a challenge, often necessitating the training of novice tutors (i.e., trainees) to ensure effective tutoring. Research suggests that providing timely explanatory feedback can…
Descriptors: Artificial Intelligence, Technology Uses in Education, Tutor Training, Trainees
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)
Rob Hirschel; Kayoko Horai – Technology in Language Teaching & Learning, 2025
With the advent of generative AI that uses large language models such as ChatGPT, it is now relatively easy to provide automated written corrective feedback in a student's native language. This paper reports on an exploratory study using a ChatGPT-powered plugin recently developed for the Moodle learning management system. The classroom…
Descriptors: Artificial Intelligence, English (Second Language), Error Correction, Writing (Composition)
Olaperi Okuboyejo; Sigrid Ewert; Ian Sanders – ACM Transactions on Computing Education, 2025
Regular expressions (REs) are often taught to undergraduate computer science majors in the Formal Languages and Automata (FLA) course; they are widely used to implement different software functionalities such as search mechanisms and data validation in diverse fields. Despite their importance, the difficulty of REs has been asserted many times in…
Descriptors: Automation, Feedback (Response), Error Patterns, Error Correction
Christopher Mah; Mei Tan; Lena Phalen; Alexa Sparks; Dorottya Demszky – Annenberg Institute for School Reform at Brown University, 2025
Effective writing feedback is a powerful tool for enhancing student learning, encouraging revision, and increasing motivation and agency. Yet, teachers face many challenges that prevent them from consistently providing effective writing feedback. Recent advances in generative artificial intelligence (AI) have led educators and researchers to…
Descriptors: Artificial Intelligence, Technology Uses in Education, Natural Language Processing, Writing Evaluation
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
Philip Slobodsky; Mariana Durcheva – International Journal of Mathematical Education in Science and Technology, 2025
AI-based bots (ChatGPT) are capable of solving mathematics problems, and students often use them for homework preparation, self-learning, etc. This raises a number of didactical and technical questions: How can students submit assignments containing complex mathematical expressions using only a keyboard? How should mathematics errors in ChatGPT's…
Descriptors: Artificial Intelligence, Man Machine Systems, Natural Language Processing, Mathematics Instruction
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
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
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