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Kangkang Li; Chengyang Qian; Xianmin Yang – Education and Information Technologies, 2025
In learnersourcing, automatic evaluation of student-generated content (SGC) is significant as it streamlines the evaluation process, provides timely feedback, and enhances the objectivity of grading, ultimately supporting more effective and efficient learning outcomes. However, the methods of aggregating students' evaluations of SGC face the…
Descriptors: Student Developed Materials, Educational Quality, Automation, Artificial Intelligence
Reese Butterfuss; Harold Doran – Educational Measurement: Issues and Practice, 2025
Large language models are increasingly used in educational and psychological measurement activities. Their rapidly evolving sophistication and ability to detect language semantics make them viable tools to supplement subject matter experts and their reviews of large amounts of text statements, such as educational content standards. This paper…
Descriptors: Alignment (Education), Academic Standards, Content Analysis, Concept Mapping
Rui Wang; Haili Ling; Jie Chen; Huijuan Fu – International Journal of Distance Education Technologies, 2025
This study adopted the Latent Dirichlet Allocation (LDA) to extract learners' needs based on 70,145 reviews from online course designed for software design and development in China and then applied Quality Function Deployment (QFD) to map learners' differentiated needs into quality attributes. Taking national first-class courses as the…
Descriptors: Educational Improvement, Student Needs, Computer Science Education, Foreign Countries
Jessica M. Lammert; Angela C. Roberts; Ken McRae; Laura J. Batterink; Blake E. Butler – Journal of Speech, Language, and Hearing Research, 2025
Purpose: Recent advances in artificial intelligence provide opportunities to capture and represent complex features of human language in a more automated manner, offering potential means of improving the efficiency of language assessment. This review article presents computerized approaches for the analysis of narrative language and identification…
Descriptors: Identification, Natural Language Processing, Artificial Intelligence, Barriers
Caroline Larson; Hannah R. Thomas; Jason Crutcher; Michael C. Stevens; Inge-Marie Eigsti – Review Journal of Autism and Developmental Disorders, 2025
Autism Spectrum Disorder (ASD) is a heterogeneous condition associated with differences in functional neural connectivity relative to neurotypical (NT) peers. Language-based functional connectivity represents an ideal context in which to characterize connectivity because language is heterogeneous and linked to core features in ASD, and NT language…
Descriptors: Autism Spectrum Disorders, Brain, Brain Hemisphere Functions, Language Processing
Maira Klyshbekova; Pamela Abbott – Electronic Journal of e-Learning, 2024
There is a current debate about the extent to which ChatGPT, a natural language AI chatbot, can disrupt processes in higher education settings. The chatbot is capable of not only answering queries in a human-like way within seconds but can also provide long tracts of texts which can be in the form of essays, emails, and coding. In this study, in…
Descriptors: Artificial Intelligence, Higher Education, Technology Uses in Education, Evaluation Methods
Soomaiya Hamid; Narmeen Zakaria Bawany – Interactive Learning Environments, 2024
E-learning is the process of sharing knowledge out of the traditional classrooms through different online tools using internet. The availability and use of these tools are not easy for every student. Many institutions gather e-learning feedback to know the problems of students to improve their systems. In e-learning systems, typically a high…
Descriptors: Feedback (Response), Electronic Learning, Automation, Classification
Mohammadreza Farrokhnia; Seyyed Kazem Banihashem; Omid Noroozi; Arjen Wals – Innovations in Education and Teaching International, 2024
ChatGPT is an AI tool that has sparked debates about its potential implications for education. We used the SWOT analysis framework to outline ChatGPT's strengths and weaknesses and to discuss its opportunities for and threats to education. The strengths include using a sophisticated natural language model to generate plausible answers,…
Descriptors: Artificial Intelligence, Synchronous Communication, Computer Software, Technology Uses in Education
Ibrahim Adeshola; Adeola Praise Adepoju – Interactive Learning Environments, 2024
The launch of OpenAI ChatGPT's language-generation model has raised alarms within many sectors, especially the academic sector. Several academicians have urged universities to develop new forms of assessment after the launch of ChatGPT, which solves academic questions in less than a few minutes. Academic cheating is not a new phenomenon, and the…
Descriptors: Opportunities, Barriers, Artificial Intelligence, Natural Language Processing
Cerstin Mahlow; Malgorzata Anna Ulasik; Don Tuggener – Reading and Writing: An Interdisciplinary Journal, 2024
Producing written texts is a non-linear process: in contrast to speech, writers are free to change already written text at any place at any point in time. Linguistic considerations are likely to play an important role, but so far, no linguistic models of the writing process exist. We present an approach for the analysis of writing processes with a…
Descriptors: Writing Processes, Methods, Sentences, Evaluation Methods
Mohammad Hmoud; Hadeel Swaity; Eman Anjass; Eva María Aguaded-Ramírez – Electronic Journal of e-Learning, 2024
This research aimed to develop and validate a rubric to assess Artificial Intelligence (AI) chatbots' effectiveness in accomplishing tasks, particularly within educational contexts. Given the rapidly growing integration of AI in various sectors, including education, a systematic and robust tool for evaluating AI chatbot performance is essential.…
Descriptors: Artificial Intelligence, Man Machine Systems, Natural Language Processing, Test Construction
Matthew Landers – Higher Education for the Future, 2025
This article presents a brief overview of the state-of-the-art in large language models (LLMs) like ChatGPT and discusses the difficulties that these technologies create for educators with regard to assessment. Making use of the 'arms race' metaphor, this article argues that there are no simple solutions to the 'AI problem'. Rather, this author…
Descriptors: Ethics, Cheating, Plagiarism, Artificial Intelligence
Debby R. E. Cotton; Peter A. Cotton; J. Reuben Shipway – Innovations in Education and Teaching International, 2024
The use of artificial intelligence in academia is a hot topic in the education field. ChatGPT is an AI tool that offers a range of benefits, including increased student engagement, collaboration, and accessibility. However, is also raises concerns regarding academic honesty and plagiarism. This paper examines the opportunities and challenges of…
Descriptors: Integrity, Cheating, Artificial Intelligence, Man Machine Systems
Huiying Cai; Xun Yan – Language Testing, 2024
Rater comments tend to be qualitatively analyzed to indicate raters' application of rating scales. This study applied natural language processing (NLP) techniques to quantify meaningful, behavioral information from a corpus of rater comments and triangulated that information with a many-facet Rasch measurement (MFRM) analysis of rater scores. The…
Descriptors: Natural Language Processing, Item Response Theory, Rating Scales, Writing Evaluation
Spyridoula Varlokosta; Katerina Fragkopoulou; Dimitra Arfani; Christina Manouilidou – International Journal of Language & Communication Disorders, 2024
Background: The detection and description of language impairments in neurodegenerative diseases like Alzheimer's Disease (AD) play an important role in research, clinical diagnosis and intervention. Various methodological protocols have been implemented for the assessment of morphosyntactic abilities in AD; narrative discourse elicitation tasks…
Descriptors: Morphology (Languages), Syntax, Alzheimers Disease, Speech Evaluation