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Showing 106 to 120 of 1,436 results Save | Export
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Elisabeth Bauer; Michael Sailer; Frank Niklas; Samuel Greiff; Sven Sarbu-Rothsching; Jan M. Zottmann; Jan Kiesewetter; Matthias Stadler; Martin R. Fischer; Tina Seidel; Detlef Urhahne; Maximilian Sailer; Frank Fischer – Journal of Computer Assisted Learning, 2025
Background: Artificial intelligence, particularly natural language processing (NLP), enables automating the formative assessment of written task solutions to provide adaptive feedback automatically. A laboratory study found that, compared with static feedback (an expert solution), adaptive feedback automated through artificial neural networks…
Descriptors: Artificial Intelligence, Feedback (Response), Computer Simulation, Natural Language Processing
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Gulson, Kalervo N.; Witzenberger, Kevin – Journal of Education Policy, 2022
Artificial Intelligence has the potential to be an important part of education governance. It is already being built into everything from business intelligence platforms to real-time online testing. In this paper, we aim to understand how AI becomes, and forms, a legitimate part of authority in contemporary education governance in what we call the…
Descriptors: Artificial Intelligence, Governance, Educational Technology, Automation
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Woongbin Park; Hyuksoo Kwon – International Journal of Technology and Design Education, 2024
The purpose of this study is multifold: First, to develop an educational program using artificial intelligence (AI) in middle school free semester system of South Korea. Second, to verify the program's effectiveness, the study clarified the definition of AI and AI education and considered their meaning in technology education. This study used…
Descriptors: Foreign Countries, Middle Schools, Artificial Intelligence, Program Effectiveness
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John Y. H. Bai; Olaf Zawacki-Richter; Wolfgang Muskens – Turkish Online Journal of Distance Education, 2024
Artificial intelligence in education (AIEd) is a fast-growing field of research. In previous work, we described efforts to explore the possible futures of AIEd by identifying key variables and their future prospects. This paper re-examines our discussions on the governance of data and the role of students and teachers by considering the…
Descriptors: Artificial Intelligence, Technology Uses in Education, Natural Language Processing, Governance
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Gerardo Ibarra-Vazquez; María Soledad Ramí­rez-Montoya; Hugo Terashima – Education and Information Technologies, 2024
This article aims to study machine learning models to determine their performance in classifying students by gender based on their perception of complex thinking competency. Data were collected from a convenience sample of 605 students from a private university in Mexico with the eComplexity instrument. In this study, we consider the following…
Descriptors: Foreign Countries, College Students, Private Colleges, Gender Bias
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Ghazala Bilquise; Samar Ibrahim; Sa'Ed M. Salhieh – Education and Information Technologies, 2024
The study explores factors affecting university students' behavioural intentions in adopting an academic advising chatbot. The study focuses on functional, socio-emotional, and relational factors affecting students' acceptance of an AI-driven academic advising chatbot. The research is based on a conceptual model derived from several constructs of…
Descriptors: Academic Advising, College Students, Intention, Artificial Intelligence
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T. Revell; W. Yeadon; G. Cahilly-Bretzin; I. Clarke; G. Manning; J. Jones; C. Mulley; R. J. Pascual; N. Bradley; D. Thomas; F. Leneghan – International Journal for Educational Integrity, 2024
Generative AI has prompted educators to reevaluate traditional teaching and assessment methods. This study examines AI's ability to write essays analysing Old English poetry; human markers assessed and attempted to distinguish them from authentic analyses of poetry by first-year undergraduate students in English at the University of Oxford. Using…
Descriptors: Artificial Intelligence, Authors, Integrity, Essays
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Jimmy Tobin; Phillip Nelson; Bob MacDonald; Rus Heywood; Richard Cave; Katie Seaver; Antoine Desjardins; Pan-Pan Jiang; Jordan R. Green – Journal of Speech, Language, and Hearing Research, 2024
Purpose: This study examines the effectiveness of automatic speech recognition (ASR) for individuals with speech disorders, addressing the gap in performance between read and conversational ASR. We analyze the factors influencing this disparity and the effect of speech mode--specific training on ASR accuracy. Method: Recordings of read and…
Descriptors: Foreign Countries, Speech Impairments, Computational Linguistics, Artificial Intelligence
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Semih Sait Yilmaz; Ayse Collins; Seyid Amjad Ali – European Journal of Education, 2024
In response to the COVID-19 pandemic, an abrupt wave of digitisation and online migration swept the higher education institutions around the globe. In the aftermath of this digital transformation which endures as the legacy of the pandemic, what lacks in knowledge is how effective the anti-COVID measures were in maintaining quality education.…
Descriptors: Foreign Countries, Artificial Intelligence, Higher Education, COVID-19
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Derwin Suhartono; Muhammad Rizki Nur Majiid; Renaldy Fredyan – Education and Information Technologies, 2024
Exam evaluations are essential to assessing students' knowledge and progress in a subject or course. To meet learning objectives and assess student performance, questions must be themed. Automatic Question Generation (AQG) is our novel approach to this problem. A comprehensive process for autonomously generating Bahasa Indonesia text questions is…
Descriptors: Foreign Countries, Computational Linguistics, Computer Software, Questioning Techniques
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Sijia Chen; Jan-Louis Kruger – Interpreter and Translator Trainer, 2024
Following a preliminary study that examined the potential effectiveness of a computer-assisted consecutive interpreting (CACI) mode, this paper presents a further trial of the CACI workflow. The workflow involves respeaking using speech recognition (SR) in phase I and production assisted by the SR text and its machine translation (MT) output in…
Descriptors: Computer Assisted Instruction, Artificial Intelligence, Translation, Speech Communication
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Mengjiao Yin; Hengshan Cao; Zuhong Yu; Xianyu Pan – International Journal of Web-Based Learning and Teaching Technologies, 2024
This study presents the Academic Investment Model (AIM) as a novel approach to predicting student academic performance by incorporating learning styles as a predictive feature. Utilizing data from 138 Marketing students across China, the research employs a combination of machine learning clustering methods and manual feature engineering through a…
Descriptors: Predictor Variables, Artificial Intelligence, Performance, Cluster Grouping
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Mohammad Jamshed; Iftikhar Alam; Sultan Al Sultan; Sameena Banu – Journal of Education and e-Learning Research, 2024
The study investigates EFL (English as a Foreign Language) learners' opinions, attitudes and the challenges of incorporating AI-powered teaching and learning. It also examines how their ideas and attitudes are affected by demographic variables. 258 students were selected using a random sampling method from a population comprising students studying…
Descriptors: Foreign Countries, English (Second Language), Second Language Instruction, Student Attitudes
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Picharpa Thangthong; Jidapa Phiromsombut; Pariwat Imsa-ard – THAITESOL Journal, 2024
Recently, AI writing assistance tools have emerged as pivotal aids in addressing L2 learners' writing challenges. With this emergence, this qualitative study delves into the perceptions of Thai EFL learners regarding the utilization of AI writing assistance tools. A total of ten Thai non-English-major undergraduates were interviewed to explore…
Descriptors: Artificial Intelligence, Foreign Countries, English (Second Language), Writing (Composition)
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Aniekan Essien; Oyegoke Teslim Bukoye; Xianghan O'Dea; Marios Kremantzis – Studies in Higher Education, 2024
This study investigates the influence of generative artificial intelligence (GAI), specifically AI text generators (ChatGPT), on critical thinking skills in UK postgraduate business school students. Using Bloom's taxonomy as theoretical underpinning, we adopt a mixed-method research employing a sample of 107 participants to investigate both the…
Descriptors: Foreign Countries, Graduate Students, Business Education, Artificial Intelligence
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