Publication Date
| In 2026 | 0 |
| Since 2025 | 317 |
| Since 2022 (last 5 years) | 949 |
| Since 2017 (last 10 years) | 1256 |
| Since 2007 (last 20 years) | 1620 |
Descriptor
Source
Author
Publication Type
Education Level
Audience
| Teachers | 14 |
| Researchers | 11 |
| Administrators | 4 |
| Policymakers | 4 |
| Practitioners | 3 |
| Students | 3 |
| Counselors | 1 |
| Parents | 1 |
| Support Staff | 1 |
Location
| China | 47 |
| Australia | 30 |
| Germany | 27 |
| United Kingdom | 23 |
| Turkey | 21 |
| Canada | 20 |
| Spain | 20 |
| Taiwan | 19 |
| United States | 19 |
| Hong Kong | 15 |
| Pennsylvania | 14 |
| More ▼ | |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Wei Li; Jia-Wei Ji; Judy C. R. Tseng; Cheng-Ye Liu; Ji-Yi Huang; Hai-Ying Liu; Mo Zhou – Educational Technology & Society, 2025
Education is an important way to achieve global Sustainable Development Goals (SDGs), while classroom engagement and collective efficacy are key factors that influence SDG learning outcomes. However, students' in-depth thinking could be limited when they apply to search engines such as Google to support their learning of SDG-related topics. Large…
Descriptors: Learner Engagement, Self Efficacy, Critical Thinking, Artificial Intelligence
Rui Guan; Mladen Rakovic; Guanliang Chen; Dragan Gaševic – Education and Information Technologies, 2025
Engagement in self-regulated learning (SRL) may improve academic achievements and support development of lifelong learning skills. Despite its educational potential, many students find SRL challenging. Educational chatbots have a potential to scaffold or externally regulate SRL processes by interacting with students in an adaptive way. However, to…
Descriptors: Literature Reviews, Artificial Intelligence, Technology Uses in Education, Educational Technology
Maria Dimeli; Apostolos Kostas – Journal of Information Technology Education: Research, 2025
Aim/Purpose: The purpose of this systematic review is to identify and analyze the current findings of empirical research on the use of ChatGPT in school and higher education. Background: As AI reshapes education, the adoption of ChatGPT has the potential to revolutionize teaching and learning in school and higher educational settings. Meanwhile,…
Descriptors: Artificial Intelligence, Technology Uses in Education, Natural Language Processing, Barriers
Jinhee Kim; Seongryeong Yu; Rita Detrick; Na Li – Education and Information Technologies, 2025
The rapid development of generative artificial intelligence (GenAI), including large language models (LLM), has merged to support students in their academic writing process. Keeping pace with the technical and educational landscape requires careful consideration of the opportunities and challenges that GenAI-assisted systems create within…
Descriptors: Student Attitudes, Artificial Intelligence, Technology Uses in Education, Natural Language Processing
Usani Joseph Ofem; Valentine Joseph Owan; Mary Arikpo Iyam; Maryrose Ify Udeh; Pauline Mbua Anake; Sylvia Victor Ovat – Education and Information Technologies, 2025
While previous studies have explored students' use of different AI tools for academic purposes, studies that have specifically investigated students' use of ChatGPT for dishonest academic purposes in Nigeria are lacking. The consequence of this contextual and knowledge gap is a lack of specific understanding regarding students' engagement with…
Descriptors: Student Attitudes, Usability, Artificial Intelligence, Technology Uses in Education
Chia-Ju Lin; Wei-Sheng Wang; Hsin-Yu Lee; Yueh-Min Huang; Ting-Ting Wu – Journal of Educational Computing Research, 2025
This study uses a quasi-experimental design to explore the role of natural language processing (NLP) and speech recognition technologies in supporting teacher interventions during collaborative STEM activities. The Speech Recognition Keywords Analysis System (SRKAS) was developed to extract keywords from student discussions, enabling real-time…
Descriptors: Natural Language Processing, Computational Linguistics, Technology Uses in Education, STEM Education
Neil E. J. A. Bowen; Richard Watson Todd – Teaching English with Technology, 2025
An increasing number of studies have investigated how ChatGPT can aid in written assessment and feedback provision. However, many studies overlook its conversational design and underlying architecture, raising concerns about the reliability and validity of their analytical outputs. Therefore, applying first principles thinking to prompt use, and…
Descriptors: Artificial Intelligence, Man Machine Systems, Natural Language Processing, Cues
Manaf Al-Okaily – Education and Information Technologies, 2025
ChatGPT a state-of-the-art language model created by Open Artificial Intelligence (AI), can revolutionize education by improving student engagement and making learning more personalized. Consequently, the study's purpose is to evaluate the antecedent factors that the influence usage and continuance usage of a recently introduced AI-based tool…
Descriptors: Artificial Intelligence, Technology Uses in Education, Educational Resources, Educational Technology
Dinuka B. Herath; Egena Ode; Gayanga B. Herath – British Educational Research Journal, 2025
This study provides a comparative assessment of the capabilities of leading artificial intelligence (AI) tools and human participants in a business management education context. Specifically, we (a) assess how well current language models perform in providing answers to standardised essay-type assessments in a business and management education…
Descriptors: Artificial Intelligence, Technology Uses in Education, Man Machine Systems, Educational Benefits
Saman Ebadi; Hassan Nejadghanbar; Ahmed Rawdhan Salman; Hassan Khosravi – Journal of Academic Ethics, 2025
This study investigates the perspectives of 12 journal reviewers from diverse academic disciplines on using large language models (LLMs) in the peer review process. We identified key themes regarding integrating LLMs through qualitative data analysis of verbatim responses to an open-ended questionnaire. Reviewers noted that LLMs can automate tasks…
Descriptors: Artificial Intelligence, Peer Evaluation, Periodicals, Journal Articles
Diana Šimic; Barbara Šlibar; Jelena Gusic Mundar; Sabina Rako – Technology, Knowledge and Learning, 2025
Researchers and practitioners from different disciplines (e.g., educational science, computer science, statistics) continuously enter the rapidly developing research field of learning analytics (LA) and bring along different perspectives and experiences in research design and methodology. Scientific communities share common problems, concepts,…
Descriptors: Learning Analytics, Higher Education, Science Education, Publications
Andrew Runge; Sarah Goodwin; Yigal Attali; Mya Poe; Phoebe Mulcaire; Kai-Ling Lo; Geoffrey T. LaFlair – Language Testing, 2025
A longstanding criticism of traditional high-stakes writing assessments is their use of static prompts in which test takers compose a single text in response to a prompt. These static prompts do not allow measurement of the writing process. This paper describes the development and validation of an innovative interactive writing task. After the…
Descriptors: Material Development, Writing Evaluation, Writing Assignments, Writing Skills
Baldwin, Peter; Yaneva, Victoria; Mee, Janet; Clauser, Brian E.; Ha, Le An – Journal of Educational Measurement, 2021
In this article, it is shown how item text can be represented by (a) 113 features quantifying the text's linguistic characteristics, (b) 16 measures of the extent to which an information-retrieval-based automatic question-answering system finds an item challenging, and (c) through dense word representations (word embeddings). Using a random…
Descriptors: Natural Language Processing, Prediction, Item Response Theory, Reaction Time
Roy, Abhik; Rambo-Hernandez, Karen E. – American Journal of Evaluation, 2021
Evaluators often find themselves in situations where resources to conduct thorough evaluations are limited. In this paper, we present a familiar instance where there is an overwhelming amount of open text to be analyzed under the constraints of time and personnel. In instances when timely feedback is important, the data are plentiful, and answers…
Descriptors: Artificial Intelligence, Engineering Education, Natural Language Processing, College Students
Kochmar, Ekaterina; Vu, Dung Do; Belfer, Robert; Gupta, Varun; Serban, Iulian Vlad; Pineau, Joelle – International Journal of Artificial Intelligence in Education, 2022
Intelligent tutoring systems (ITS) have been shown to be highly effective at promoting learning as compared to other computer-based instructional approaches. However, many ITS rely heavily on expert design and hand-crafted rules. This makes them difficult to build and transfer across domains and limits their potential efficacy. In this paper, we…
Descriptors: Intelligent Tutoring Systems, Automation, Feedback (Response), Dialogs (Language)

Peer reviewed
Direct link
