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Hanadi Aldreabi; Nisreen Kareem Salama Dahdoul; Mohammad Alhur; Nidal Alzboun; Najeh Rajeh Alsalhi – Electronic Journal of e-Learning, 2025
The examination of the impact of Generative AI (GenAI) on higher education, especially from the viewpoint of students, is gaining significance. Although prior research has underscored GenAI's potential advantages in higher education, there exists a discernible research gap concerning the determinants that affect its adoption. In the present study,…
Descriptors: Student Behavior, Artificial Intelligence, Natural Language Processing, Technology Uses in Education
Ursula Holzmann; Sulekha Anand; Alexander Y. Payumo – Advances in Physiology Education, 2025
Generative large language models (LLMs) like ChatGPT can quickly produce informative essays on various topics. However, the information generated cannot be fully trusted, as artificial intelligence (AI) can make factual mistakes. This poses challenges for using such tools in college classrooms. To address this, an adaptable assignment called the…
Descriptors: Artificial Intelligence, Technology Uses in Education, Natural Language Processing, Thinking Skills
Ted M. Clark; Ellie Anderson; Nicole M. Dickson-Karn; Comelia Soltanirad; Nicolas Tafini – Journal of Chemical Education, 2023
Student performance on open-response calculations involving acid and base solutions before and after instruction in general chemistry and analytical chemistry courses was compared with the output from the artificial intelligence chatbot ChatGPT. Applying a theoretical model of expertise for problem solving that includes problem conceptualization,…
Descriptors: Academic Achievement, College Students, College Science, Chemistry
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
Qiao Wang; Ralph L. Rose; Ayaka Sugawara; Naho Orita – Vocabulary Learning and Instruction, 2025
VocQGen is an automated tool designed to generate multiple-choice cloze (MCC) questions for vocabulary assessment in second language learning contexts. It leverages several natural language processing (NLP) tools and OpenAI's GPT-4 model to produce MCC items quickly from user-specified word lists. To evaluate its effectiveness, we used the first…
Descriptors: Vocabulary Skills, Artificial Intelligence, Computer Software, Multiple Choice Tests
Rybinski, Krzysztof; Kopciuszewska, Elzbieta – Assessment & Evaluation in Higher Education, 2021
This article presents the first-ever big data study of the student evaluation of teaching (SET) using artificial intelligence (AI). We train natural language processing (NLP) models on 1.6 million student evaluations from the US and the UK. We address two research questions: (1) are these models able to predict student ratings from the student…
Descriptors: Artificial Intelligence, Technology Uses in Education, Student Evaluation of Teacher Performance, Natural Language Processing
Bosch, Nigel; Crues, R. Wes; Shaik, Najmuddin; Paquette, Luc – Grantee Submission, 2020
Online courses often include discussion forums, which provide a rich source of data to better understand and improve students' learning experiences. However, forum messages frequently contain private information that prevents researchers from analyzing these data. We present a method for discovering and redacting private information including…
Descriptors: Privacy, Discussion Groups, Asynchronous Communication, Methods
Balyan, Renu; McCarthy, Kathryn S.; McNamara, Danielle S. – Grantee Submission, 2017
This study examined how machine learning and natural language processing (NLP) techniques can be leveraged to assess the interpretive behavior that is required for successful literary text comprehension. We compared the accuracy of seven different machine learning classification algorithms in predicting human ratings of student essays about…
Descriptors: Artificial Intelligence, Natural Language Processing, Reading Comprehension, Literature
Balyan, Renu; McCarthy, Kathryn S.; McNamara, Danielle S. – International Educational Data Mining Society, 2017
This study examined how machine learning and natural language processing (NLP) techniques can be leveraged to assess the interpretive behavior that is required for successful literary text comprehension. We compared the accuracy of seven different machine learning classification algorithms in predicting human ratings of student essays about…
Descriptors: Artificial Intelligence, Natural Language Processing, Reading Comprehension, Literature
Heiner, Cecily; Zachary, Joseph L. – International Working Group on Educational Data Mining, 2009
Students in introductory programming classes often articulate their questions and information needs incompletely. Consequently, the automatic classification of student questions to provide automated tutorial responses is a challenging problem. This paper analyzes 411 questions from an introductory Java programming course by reducing the natural…
Descriptors: Classification, Questioning Techniques, Introductory Courses, Computer Science Education
Boyer, Kristy Elizabeth, Ed.; Yudelson, Michael, Ed. – International Educational Data Mining Society, 2018
The 11th International Conference on Educational Data Mining (EDM 2018) is held under the auspices of the International Educational Data Mining Society at the Templeton Landing in Buffalo, New York. This year's EDM conference was highly competitive, with 145 long and short paper submissions. Of these, 23 were accepted as full papers and 37…
Descriptors: Data Collection, Data Analysis, Computer Science Education, Program Proposals