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Sebastian Hobert; Florian Berens – Educational Technology Research and Development, 2024
Individualized learning support is an essential part of formal educational learning processes. However, in typical large-scale educational settings, resource constraints result in limited interaction among students, teaching assistants, and lecturers. Due to this, learning success in those settings may suffer. Inspired by current technological…
Descriptors: Individualized Instruction, Intelligent Tutoring Systems, Learning Processes, Teaching Methods
Thanh Pham; Binh Nguyen; Son Ha; Thanh Nguyen Ngoc – Australasian Journal of Educational Technology, 2023
This research explored the potential of artificial intelligence (AI)-assisted learning using ChatGPT in an engineering course at a university in South-east Asia. The study investigated the benefits and challenges that students may encounter when utilising ChatGPT-3.5 as a learning tool. This research developed an AI-assisted learning flow that…
Descriptors: Artificial Intelligence, Engineering Education, Universities, Foreign Countries
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
Carme Grimalt-Álvaro; Mireia Usart – Journal of Computing in Higher Education, 2024
Sentiment Analysis (SA), a technique based on applying artificial intelligence to analyze textual data in natural language, can help to characterize interactions between students and teachers and improve learning through timely, personalized feedback, but its use in education is still scarce. This systematic literature review explores how SA has…
Descriptors: Formative Evaluation, Higher Education, Artificial Intelligence, Natural Language Processing
Fahad Saleem Al-Hafdi; Sameer Mosa AlNajdi – Education and Information Technologies, 2024
During the last few years, the popularity of chatbots has risen and grown exponentially with the increase in demand for smartphones and messaging applications. Chatbots can be utilized in education by providing information about educational content, communication, and assistance, enhancing classroom participation, and facilitating collaborative…
Descriptors: Artificial Intelligence, Technology Uses in Education, Instructional Effectiveness, Learning Processes
Imre Bende – Acta Didactica Napocensia, 2024
The continuous development of artificial intelligence-based tools makes their emergence inevitable in education as well as other fields of life. This article presents findings of a mixed method study aimed at investigating the current perceptions and potential applications of AI in Hungarian educational settings. Through interviews with high…
Descriptors: Readiness, Artificial Intelligence, Technology Uses in Education, Foreign Countries
Baskara, Risang; Mukarto – Indonesian Journal of English Language Teaching and Applied Linguistics, 2023
Recent developments in natural language processing have led to the creation of large language models, such as ChatGPT, which could generate human-like text. In this paper, we explore the potential implications of ChatGPT for language learning in higher education. We first provide an overview of ChatGPT and discuss its capabilities and limitations.…
Descriptors: Artificial Intelligence, Second Language Learning, Second Language Instruction, Teaching Methods
Xu, Jia; Wei, Tingting; Lv, Pin – International Educational Data Mining Society, 2022
In an Intelligent Tutoring System (ITS), problem (or question) difficulty is one of the most critical parameters, directly impacting problem design, test paper organization, result analysis, and even the fairness guarantee. However, it is very difficult to evaluate the problem difficulty by organized pre-tests or by expertise, because these…
Descriptors: Prediction, Programming, Natural Language Processing, Databases
Danielle S. McNamara; Tracy Arner; Elizabeth Reilley; Paul Alvarado; Chani Clark; Thomas Fikes; Annie Hale; Betheny Weigele – Grantee Submission, 2022
Accounting for complex interactions between contextual variables and learners' individual differences in aptitudes and background requires building the means to connect and access learner data at large scales, across time, and in multiple contexts. This paper describes the ASU Learning@Scale (L@S) project to develop a digital learning network…
Descriptors: Electronic Learning, Educational Technology, Networks, Learning Analytics
Fabian Kieser; Peter Wulff; Jochen Kuhn; Stefan Küchemann – Physical Review Physics Education Research, 2023
Generative AI technologies such as large language models show novel potential to enhance educational research. For example, generative large language models were shown to be capable of solving quantitative reasoning tasks in physics and concept tests such as the Force Concept Inventory (FCI). Given the importance of such concept inventories for…
Descriptors: Physics, Science Instruction, Artificial Intelligence, Computer Software
Araz Zirar – Review of Education, 2023
Recent developments in language models, such as ChatGPT, have sparked debate. These tools can help, for example, dyslexic people, to write formal emails from a prompt and can be used by students to generate assessed work. Proponents argue that language models enhance the student experience and academic achievement. Those concerned argue that…
Descriptors: Artificial Intelligence, Technology Uses in Education, Natural Language Processing, Models
Švábenský, Valdemar; Baker, Ryan S.; Zambrano, Andrés; Zou, Yishan; Slater, Stefan – International Educational Data Mining Society, 2023
Students who take an online course, such as a MOOC, use the course's discussion forum to ask questions or reach out to instructors when encountering an issue. However, reading and responding to students' questions is difficult to scale because of the time needed to consider each message. As a result, critical issues may be left unresolved, and…
Descriptors: Generalization, Computer Mediated Communication, MOOCs, State Universities
Vanichvasin, Patchara – International Education Studies, 2022
There are many ways to learn how to be entrepreneurs and one of the powerful ways is to learn from successful entrepreneurs. However, it is difficult to reach and interview those entrepreneurs about their best practices in doing business in real lives. Chatbot technology can come into play in mimicking conversation of successful entrepreneurs and…
Descriptors: Entrepreneurship, Teaching Methods, Graduate Students, Best Practices
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
Charlotte N. Gunawardena; Yan Chen; Nick Flor; Damien Sánchez – Online Learning, 2023
Gunawardena et al.'s (1997) Interaction Analysis Model (IAM) is one of the most frequently employed frameworks to guide the qualitative analysis of social construction of knowledge online. However, qualitative analysis is time consuming, and precludes immediate feedback to revise online courses while being delivered. To expedite analysis with a…
Descriptors: Models, Learning Processes, Knowledge Level, Online Courses
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