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Debora Weber-Wulff; Alla Anohina-Naumeca; Sonja Bjelobaba; Tomáš Foltýnek; Jean Guerrero-Dib; Olumide Popoola; Petr Šigut; Lorna Waddington – International Journal for Educational Integrity, 2023
Recent advances in generative pre-trained transformer large language models have emphasised the potential risks of unfair use of artificial intelligence (AI) generated content in an academic environment and intensified efforts in searching for solutions to detect such content. The paper examines the general functionality of detection tools for…
Descriptors: Artificial Intelligence, Identification, Man Machine Systems, Accuracy
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Gary D. Fisk – Teaching of Psychology, 2025
Introduction: Recent innovations in generative artificial intelligence (AI) technologies have led to an educational environment in which human authorship cannot be assumed, thereby posing a significant challenge to upholding academic integrity. Statement of the problem: Both humans and AI detection technologies have difficulty distinguishing…
Descriptors: Technology Uses in Education, Writing (Composition), Plagiarism, Identification
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Micir, Ian; Swygert, Kimberly; D'Angelo, Jean – Journal of Applied Testing Technology, 2022
The interpretations of test scores in secure, high-stakes environments are dependent on several assumptions, one of which is that examinee responses to items are independent and no enemy items are included on the same forms. This paper documents the development and implementation of a C#-based application that uses Natural Language Processing…
Descriptors: Artificial Intelligence, Man Machine Systems, Accuracy, Efficiency
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Urrutia, Felipe; Araya, Roberto – Journal of Educational Computing Research, 2024
Written answers to open-ended questions can have a higher long-term effect on learning than multiple-choice questions. However, it is critical that teachers immediately review the answers, and ask to redo those that are incoherent. This can be a difficult task and can be time-consuming for teachers. A possible solution is to automate the detection…
Descriptors: Elementary School Students, Grade 4, Elementary School Mathematics, Mathematics Tests
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Wan, Qian; Crossley, Scott; Banawan, Michelle; Balyan, Renu; Tian, Yu; McNamara, Danielle; Allen, Laura – International Educational Data Mining Society, 2021
The current study explores the ability to predict argumentative claims in structurally-annotated student essays to gain insights into the role of argumentation structure in the quality of persuasive writing. Our annotation scheme specified six types of argumentative components based on the well-established Toulmin's model of argumentation. We…
Descriptors: Essays, Persuasive Discourse, Automation, Identification
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Sanosi, Abdulaziz; Abdalla, Mohamed – Australian Journal of Applied Linguistics, 2021
This study aimed to examine the potentials of the NLP approach in detecting discourse markers (DMs), namely okay, in transcribed spoken data. One hundred thirty-eight concordance lines were presented to human referees to judge the functions of okay in them as a DM or Non-DM. After that, the researchers used a Python script written according to the…
Descriptors: Natural Language Processing, Computational Linguistics, Programming Languages, Accuracy
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Jae Q. J. Liu; Kelvin T. K. Hui; Fadi Al Zoubi; Zing Z. X. Zhou; Dino Samartzis; Curtis C. H. Yu; Jeremy R. Chang; Arnold Y. L. Wong – International Journal for Educational Integrity, 2024
The application of artificial intelligence (AI) in academic writing has raised concerns regarding accuracy, ethics, and scientific rigour. Some AI content detectors may not accurately identify AI-generated texts, especially those that have undergone paraphrasing. Therefore, there is a pressing need for efficacious approaches or guidelines to…
Descriptors: Artificial Intelligence, Investigations, Identification, Human Factors Engineering
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Liu, Chengyuan; Cui, Jialin; Shang, Ruixuan; Xiao, Yunkai; Jia, Qinjin; Gehringer, Edward – International Educational Data Mining Society, 2022
An online peer-assessment system typically allows students to give textual feedback to their peers, with the goal of helping the peers improve their work. The amount of help that students receive is highly dependent on the quality of the reviews. Previous studies have investigated using machine learning to detect characteristics of reviews (e.g.,…
Descriptors: Peer Evaluation, Feedback (Response), Computer Mediated Communication, Teaching Methods
Tu, Yuancheng – ProQuest LLC, 2012
The fundamental problem faced by automatic text understanding in Natural Language Processing (NLP) is to identify semantically related pieces of text and integrate them together to compute the meaning of the whole text. However, the principle of compositionality runs into trouble very quickly when real language is examined with its frequent…
Descriptors: English, Verbs, Computational Linguistics, Natural Language Processing