Publication Date
| In 2026 | 8 |
| Since 2025 | 2587 |
| Since 2022 (last 5 years) | 6143 |
| Since 2017 (last 10 years) | 7087 |
| Since 2007 (last 20 years) | 7544 |
Descriptor
Source
Author
| Danielle S. McNamara | 26 |
| Gwo-Jen Hwang | 19 |
| Mihai Dascalu | 17 |
| McNamara, Danielle S. | 14 |
| Hwang, Gwo-Jen | 13 |
| Aleven, Vincent | 12 |
| Jiahong Su | 12 |
| Wanli Xing | 12 |
| Xiaoming Zhai | 12 |
| Chenglu Li | 11 |
| Di Zou | 11 |
| More ▼ | |
Publication Type
Education Level
Audience
| Researchers | 246 |
| Teachers | 213 |
| Practitioners | 183 |
| Policymakers | 92 |
| Administrators | 75 |
| Students | 56 |
| Media Staff | 10 |
| Counselors | 5 |
| Parents | 4 |
| Support Staff | 4 |
| Community | 3 |
| More ▼ | |
Location
| China | 393 |
| Turkey | 202 |
| Australia | 134 |
| United States | 124 |
| Taiwan | 120 |
| United Kingdom | 120 |
| India | 110 |
| South Korea | 102 |
| Indonesia | 97 |
| Canada | 95 |
| Germany | 93 |
| More ▼ | |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
| Meets WWC Standards without Reservations | 1 |
| Meets WWC Standards with or without Reservations | 1 |
Peer reviewed
Download full textMohamed Ally; Sanjaya Mishra – Canadian Journal of Learning and Technology, 2024
This paper highlights the importance of artificial intelligence (AI) policies in higher education institutions and presents a step-by-step process for adopting institutional policies. Emphasizing the inevitable implications on AI in teaching and learning, this paper also discusses key policy areas for consideration by the stakeholders and lists…
Descriptors: Artificial Intelligence, Educational Policy, Higher Education, Technology Uses in Education
Mohammad Arif Ul Alam; Madhavi Pagare; Susan Davis; Geeta Verma; Ashis Biswas; Justin Barbern – International Educational Data Mining Society, 2024
Recognizing the Social Determinants of Mental Health (SDMHs) among students is essential, as lower backgrounds in these determinants elevate the risk of poor academic achievement, behavioral issues, and physical health problems, thereby affecting both physical and emotional well-being. Leveraging students' self-reported lived experiential essays…
Descriptors: Mental Health, At Risk Students, Prediction, Automation
Priti Oli; Rabin Banjade; Jeevan Chapagain; Vasile Rus – Grantee Submission, 2024
Assessing students' answers and in particular natural language answers is a crucial challenge in the field of education. Advances in transformer-based models such as Large Language Models (LLMs), have led to significant progress in various natural language tasks. Nevertheless, amidst the growing trend of evaluating LLMs across diverse tasks,…
Descriptors: Student Evaluation, Computer Assisted Testing, Artificial Intelligence, Comprehension
Direct linkMonsalve-Pulido, Julian; Aguilar, Jose; Montoya, Edwin – Education and Information Technologies, 2023
The adaptation of traditional systems to service-oriented architectures is very frequent, due to the increase in technologies for this type of architecture. This has led to the construction of frameworks or methodologies for adapting computational projects to service-oriented architecture (SOA) technology. In this work, a framework for adaptation…
Descriptors: Artificial Intelligence, Information Technology, Design, Governance
Kebede, Mihiretu M.; Le Cornet, Charlotte; Fortner, Renée Turzanski – Research Synthesis Methods, 2023
We aimed to evaluate the performance of supervised machine learning algorithms in predicting articles relevant for full-text review in a systematic review. Overall, 16,430 manually screened titles/abstracts, including 861 references identified relevant for full-text review were used for the analysis. Of these, 40% (n = 6573) were sub-divided for…
Descriptors: Automation, Literature Reviews, Artificial Intelligence, Algorithms
Kataoka, Yuki; Taito, Shunsuke; Yamamoto, Norio; So, Ryuhei; Tsutsumi, Yusuke; Anan, Keisuke; Banno, Masahiro; Tsujimoto, Yasushi; Wada, Yoshitaka; Sagami, Shintaro; Tsujimoto, Hiraku; Nihashi, Takashi; Takeuchi, Motoki; Terasawa, Teruhiko; Iguchi, Masahiro; Kumasawa, Junji; Ichikawa, Takumi; Furukawa, Ryuki; Yamabe, Jun; Furukawa, Toshi A. – Research Synthesis Methods, 2023
There are currently no abstract classifiers, which can be used for new diagnostic test accuracy (DTA) systematic reviews to select primary DTA study abstracts from database searches. Our goal was to develop machine-learning-based abstract classifiers for new DTA systematic reviews through an open competition. We prepared a dataset of abstracts…
Descriptors: Competition, Classification, Diagnostic Tests, Accuracy
Elkhatat, Ahmed M.; Elsaid, Khaled; Almeer, Saeed – International Journal for Educational Integrity, 2023
The proliferation of artificial intelligence (AI)-generated content, particularly from models like ChatGPT, presents potential challenges to academic integrity and raises concerns about plagiarism. This study investigates the capabilities of various AI content detection tools in discerning human and AI-authored content. Fifteen paragraphs each…
Descriptors: Artificial Intelligence, Integrity, Plagiarism, Educational Technology
Baena-Rojas, Jose Jaime; Castillo-Martínez, Isolda Margarita; Méndez-Garduño, Juana Isabel; Suárez-Brito, Paloma; López-Caudana, Edgar Omar – Journal of Social Studies Education Research, 2023
Various technological devices, especially information communications technologies (ICTs), have become increasingly remarkable in higher education to help develop students' skills and qualifications. Considering this trend, supported by several academic theories, this paper proposes a breakthrough guidebook for universities and other scholastic…
Descriptors: Information Technology, Artificial Intelligence, Robotics, Higher Education
Zhao, Li; Zheng, Yi; Zhao, Junbang; Li, Guoqiang; Compton, Brian J.; Zhang, Rui; Fang, Fang; Heyman, Gail D.; Lee, Kang – Child Development, 2023
Academic cheating is common, but little is known about its early emergence. It was examined among Chinese second to sixth graders (N = 2094; 53% boys, collected between 2018 and 2019) using a machine learning approach. Overall, 25.74% reported having cheated, which was predicted by the best machine learning algorithm (Random Forest) at a mean…
Descriptors: Cheating, Elementary School Students, Artificial Intelligence, Foreign Countries
Prokofieva, Maria – Education and Information Technologies, 2023
External audit is undergoing rapid changes where more and more routine tasks are automated with analytics and artificial intelligence (AI) instruments. The paper addresses a research problem of mapping data analytics to audit tasks and develops a framework aligning audit phases and AI and using data analytics in teaching audit with AI. The paper…
Descriptors: Data Analysis, Financial Audits, Artificial Intelligence, Curriculum Development
Li, Aini; Roberts, Gareth – Cognitive Science, 2023
We investigated the emergence of sociolinguistic indexicality using an artificial-language-learning paradigm. Sociolinguistic indexicality involves the association of linguistic variants with nonlinguistic social or contextual features. Any linguistic variant can acquire "constellations" of such indexical meanings, though they also…
Descriptors: Artificial Intelligence, Sociolinguistics, Context Effect, Stereotypes
Karrenbauer, Christin; Brauner, Tim; König, Claudia M.; Breitner, Michael H. – Educational Technology Research and Development, 2023
The growing number of students in higher education institutions, along with students' diverse educational backgrounds, is driving demand for more individual study support. Furthermore, online lectures increased due to the COVID-19 pandemic and are expected to continue, further accelerating the need for self-regulated learning. Individual digital…
Descriptors: Design, Development, Evaluation, Higher Education
Chen, Jennifer J.; Perez, ChareMone' – Childhood Education, 2023
Assessment holds the key to unlocking for the teacher a child's past (what he already knows), present (what he is learning), and future (what he still needs to learn) to inform teaching. Despite the benefits of assessment for informing teaching practice and enhancing student learning, it remains one of the most challenging and time-consuming tasks…
Descriptors: Evaluation Methods, Individualized Instruction, Artificial Intelligence, Computer Assisted Testing
Hu, Yuanyuan; Donald, Claire; Giacaman, Nasser – International Journal of Artificial Intelligence in Education, 2023
This paper investigates using multi-label deep learning approach to extending the understanding of cognitive presence in MOOC discussions. Previous studies demonstrate the challenges of subjectivity in manual categorisation methods. Training automatic single-label classifiers may preserve this subjectivity. Using a triangulation approach, we…
Descriptors: Classification, MOOCs, Artificial Intelligence, Intelligent Tutoring Systems
Bai, Xiaoyu; Stede, Manfred – International Journal of Artificial Intelligence in Education, 2023
Recent years have seen increased interests in applying the latest technological innovations, including artificial intelligence (AI) and machine learning (ML), to the field of education. One of the main areas of interest to researchers is the use of ML to assist teachers in assessing students' work on the one hand and to promote effective…
Descriptors: Artificial Intelligence, Intelligent Tutoring Systems, Natural Language Processing, Evaluation
