NotesFAQContact Us
Collection
Advanced
Search Tips
Audience
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Showing all 14 results Save | Export
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Bekir Yildirim; Ahmet Tayfur Akcan – Journal of Education in Science, Environment and Health, 2024
This study aimed to propose a Professional Development Model (PDM) for chemistry teachers to enhance their professional development in Artificial Intelligence (AI). The research group consisted of 17 chemistry teachers. The study was designed using a particular case study suitable for qualitative research methods. Document review, teacher…
Descriptors: Artificial Intelligence, Faculty Development, Science Teachers, Chemistry
Peer reviewed Peer reviewed
Direct linkDirect link
Phillips, Tanner M.; Saleh, Asmalina; Ozogul, Gamze – International Journal of Artificial Intelligence in Education, 2023
Encouraging teachers to reflect on their instructional practices and course design has been shown to be an effective means of improving instruction and student learning. However, the process of encouraging reflection is difficult; reflection requires quality data, thoughtful analysis, and contextualized interpretation. Because of this, research on…
Descriptors: Reflection, Artificial Intelligence, Natural Language Processing, Data Collection
Peer reviewed Peer reviewed
Direct linkDirect link
Chung, Cheng-Yu; Hsiao, I-Han; Lin, Yi-Ling – Journal of Research on Technology in Education, 2023
Creating practice questions for programming learning is not an easy job. It requires the instructor to diligently organize heterogeneous learning resources. Although educational technologies have been adopted across levels of programming learning, programming question generation (PQG) is still predominantly performed by instructors without…
Descriptors: Artificial Intelligence, Programming, Questioning Techniques, Heterogeneous Grouping
Peer reviewed Peer reviewed
Direct linkDirect link
Dolawattha, Dhammika Manjula; Premadasa, H. K. Salinda; Jayaweera, Prasad M. – International Journal of Information and Learning Technology, 2022
Purpose: The purpose of this study is to evaluate the sustainability of the proposed mobile learning framework for higher education. Most sustainability evaluation studies use quantitative and qualitative methods with statistical approaches. Sometimes, in previous studies, machine learning models were utilized conventionally.…
Descriptors: Sustainability, Higher Education, Artificial Intelligence, Electronic Learning
Matthew Christopher Myers – ProQuest LLC, 2024
This study uses an experimental comparative design to accomplish two primary goals related teachers' perceptions of automated writing evaluation (AWE) performance. First, it quantitatively and qualitatively examines teachers' perceptions of the accuracy and trustworthiness of differentially performing AWE models. Second, it synthesizes interview…
Descriptors: Language Arts, Teacher Attitudes, English Teachers, Automation
Peer reviewed Peer reviewed
Direct linkDirect link
Manar Hazaimeh; Abdullah M. Al-Ansi – International Journal of Information and Learning Technology, 2024
Purpose: Artificial intelligence (AI) is constantly evolving and is poised to significantly transform the world, affecting nearly every sector and aspect of society. As AI continues to evolve, it is expected to create a more dynamic, efficient and personalized education system, supporting lifelong learning and adapting to the needs and pace of…
Descriptors: Artificial Intelligence, Adoption (Ideas), Teacher Attitudes, Student Attitudes
Peer reviewed Peer reviewed
Direct linkDirect link
Shoaib, Muhammad; Ullah, Hazir – International Journal of Educational Management, 2021
Purpose: This paper attempts to explore possible contributing factors of females' outperformance and males' underperformance in the higher education in Pakistan from teachers' perspective. The central question of the study is what are the key factors that affect female and male students' educational performance at the university level? Using…
Descriptors: Teacher Attitudes, Gender Differences, Achievement Gap, College Students
Peer reviewed Peer reviewed
Direct linkDirect link
Wyness, Gill; Macmillan, Lindsey; Anders, Jake; Dilnot, Catherine – Education Economics, 2023
Students in the UK apply to university with teacher-predicted examination grades, rather than actual results. These predictions have been shown to be inaccurate, and to favour certain groups, leading to concerns about teacher bias. We ask whether it is possible to improve on the accuracy of teachers' predictions by predicting pupil achievement…
Descriptors: Foreign Countries, Prediction, Grades (Scholastic), Expectation
Peer reviewed Peer reviewed
Direct linkDirect link
K. Keerthi Jain; J. N. V. Raghuram – Education and Information Technologies, 2024
This research delves into the multifaceted landscape of various factors that influence the adoption of Generation-Artificial Intelligence (Gen-AI) in Higher Education. By employing a comprehensive framework that includes perceived risk, perceived ease of use, usefulness, Technological Pedagogical Content Knowledge (TPACK), and trust, the study…
Descriptors: Prediction, Artificial Intelligence, Technological Literacy, Pedagogical Content Knowledge
Peer reviewed Peer reviewed
Direct linkDirect link
Belda-Medina, Jose; Kokošková, Vendula – International Journal of Educational Technology in Higher Education, 2023
Recent advances in Artificial Intelligence (AI) have paved the way for the integration of text-based and voice-enabled chatbots as adaptive virtual tutors in education. Despite the increasing use of AI-powered chatbots in language learning, there is a lack of studies exploring the attitudes and perceptions of teachers and students towards these…
Descriptors: Technology Integration, Technology Uses in Education, Artificial Intelligence, Man Machine Systems
Peer reviewed Peer reviewed
Direct linkDirect link
Srour, F. Jordan; Karkoulian, Silva – International Journal of Social Research Methodology, 2022
The literature provides multiple measures of diversity along a single demographic dimension, but when it comes to studying the interaction of multiple diversity types (e.g. age, gender, and race), the field of useable measures diminishes. We present the use of decision trees as a machine learning technique to automatically identify the…
Descriptors: Diversity, Decision Making, Artificial Intelligence, Correlation
Peer reviewed Peer reviewed
Direct linkDirect link
Vishal Soodan; Avinash Rana; Anurag Jain; Deeksha Sharma – Journal of Information Technology Education: Innovations in Practice, 2024
Aim/Purpose: This mixed-methods study aims to examine factors influencing academicians' intentions to continue using AI-based chatbots by integrating the Task-Technology Fit (TTF) model and social network characteristics. Background: AI-powered chatbots are gaining popularity across industries, including academia. However, empirical research on…
Descriptors: Artificial Intelligence, Social Networks, College Faculty, Computer Software
Peer reviewed Peer reviewed
Direct linkDirect link
Meletiadou, Eleni, Ed. – IGI Global, 2023
Recent evolutions, such as pervasive networking and other enabling technologies, have been increasingly changing human life, knowledge acquisition, and the way works are performed and students learn. In this societal change, educational institutions must maintain their leading role. They have therefore embraced digitally enhanced learning to…
Descriptors: Educational Change, Educational Technology, Technology Uses in Education, Student Needs
Peer reviewed Peer reviewed
Johnstone, A. H. – Journal of Chemical Education, 1997
Suggests that the development of good chemistry teaching and the pursuit of research have essentially the same structure. Similarities include the need for a clear focus, efficiency in time and effort, and a direction that is more often right than wrong. (DDR)
Descriptors: Artificial Intelligence, Chemistry, Cognitive Psychology, Educational Researchers