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Lahoud, Christine; Moussa, Sherin; Obeid, Charbel; El Khoury, Hicham; Champin, Pierre-Antoine – Education and Information Technologies, 2023
Academic advising is inhibited at most of the high schools to help students identify appropriate academic pathways. The choice of a career domain is significantly influenced by the complexity of life and the volatility of the labor market. Thus, high school students feel confused during the shift period from high school to university, especially…
Descriptors: Academic Advising, Artificial Intelligence, Majors (Students), Career Guidance
William Joseph Fassbender – English Teaching: Practice and Critique, 2024
Purpose: This study builds on previous theoretical work that considered artificial intelligence (AI) and its potential for creating "teacher-centaurs" whose labor could be accelerated through the use of generative AI (Fassbender, in review). The purpose of this paper is to use empirical methods to study centaur teachers and the division…
Descriptors: Artificial Intelligence, Technology Uses in Education, Secondary School Teachers, Secondary Education
Kwok-cheung Cheung; Pou-seong Sit; Jia-qi Zheng; Chi-chio Lam; Soi-kei Mak; Man-kai Ieong – British Journal of Educational Psychology, 2024
Background: Given that students from socio-economically disadvantaged family backgrounds are more likely to suffer from low academic performance, there is an interest in identifying features of academic resilience, which may mitigate the relationship between disadvantaged socio-economic status and academic performance. Aims: This study sought to…
Descriptors: Achievement Tests, Foreign Countries, International Assessment, Secondary School Students
Adelson de Araujo; Pantelis M. Papadopoulos; Susan McKenney; Ton de Jong – Journal of Computer Assisted Learning, 2024
Background: Sustaining productive student-student dialogue in online collaborative inquiry learning is challenging, and teacher support is limited when needed in multiple groups simultaneously. Collaborative conversational agents (CCAs) have been used in the past to support student dialogue. Yet, research is needed to reveal the characteristics…
Descriptors: Learning Analytics, Computer Mediated Communication, Artificial Intelligence, Dialogs (Language)
Gülçin Kurkan; Münevver Çetin – International Journal of Contemporary Educational Research, 2024
Artificial intelligence technologies are used in many fields and have become a part of our lives. The field of artificial intelligence, which has an important place, especially in the field of education and digital leadership, is constantly developing and is expected to create even greater impacts in the future. The main purpose of this research…
Descriptors: Administrator Attitudes, Artificial Intelligence, Technology Uses in Education, Instructional Leadership
Ramon Mayor Martins; Christiane G. Von Wangenheim; Marcelo F. Rauber; Adriano F. Borgatto; Jean C. R. Hauck – ACM Transactions on Computing Education, 2024
As Machine Learning (ML) becomes increasingly integrated into our daily lives, it is essential to teach ML to young people from an early age including also students from a low socioeconomic status (SES) background. Yet, despite emerging initiatives for ML instruction in K-12, there is limited information available on the learning of students from…
Descriptors: Artificial Intelligence, Computer Science Education, Socioeconomic Status, Correlation
K. G. Srinivasa; Aman Singh; Kshitij Kumar Singh Chauhan – IEEE Transactions on Education, 2024
Contribution: This article investigates the impact of gamified learning on high school students (grades 9-12) in computer science, emphasizing learner engagement, knowledge improvement, and overall satisfaction. It contributes insights into the effectiveness of gamification in enhancing educational outcomes. Background: Gamification in education…
Descriptors: High School Students, Gamification, Computer Science Education, Critical Thinking
Seong-Won Kim; Youngjun Lee – Education and Information Technologies, 2024
In this study, the influence of socio-cultural factors on attitudes toward artificial intelligence (AI) was investigated. In total, 1,677 Korean middle school students were selected to participate, and a test tool was used to measure the attitude toward AI. As a result, according to socio-cultural factors, middle school students' attitudes toward…
Descriptors: Foreign Countries, Middle School Students, Artificial Intelligence, Sociocultural Patterns
Tarek Ait Baha; Mohamed El Hajji; Youssef Es-Saady; Hammou Fadili – Education and Information Technologies, 2024
Artificial Intelligence (AI) technologies have increasingly become vital in our everyday lives. Education is one of the most visible domains in which these technologies are being used. Conversational Agents (CAs) are among the most prominent AI systems for assisting teaching and learning processes. Their integration into an e-learning system can…
Descriptors: Secondary School Students, Public Schools, Foreign Countries, Artificial Intelligence
Rashmi Khazanchi; Daniele Di Mitri; Hendrik Drachsler – Journal of Computer Assisted Learning, 2025
Background: Despite educational advances, poor mathematics achievement persists among K-12 students, particularly in rural areas with limited resources and skilled teachers. Artificial Intelligence (AI) based systems have increasingly been adopted to support the diverse learning needs of students and have been shown to enhance mathematics…
Descriptors: Mathematics Achievement, Rural Areas, Artificial Intelligence, Individualized Instruction
Rugh, Michael Sze-hon; Capraro, Mary Margaret; Capraro, Robert Michael – Electronic Journal of e-Learning, 2023
The Dynamic and Interactive Mathematical Expressions (DIME) Map system automatically generates DIME maps, which are personalizable and manipulable concept maps that allow students to interact with the mathematical concepts contained in any portable document format (PDF) textbook or document. A teacher can automatically upload a PDF textbook…
Descriptors: Self Efficacy, Artificial Intelligence, Concept Mapping, Technology Integration
Leitner, Maxyn; Greenwald, Eric; Wang, Ning; Montgomery, Ryan; Merchant, Chirag – International Journal of Artificial Intelligence in Education, 2023
Artificial Intelligence (AI) permeates every aspect of our daily lives and is no longer a subject reserved for a select few in higher education but is essential knowledge that our youth need for the future. Much is unknown about the level of AI knowledge that is age and developmentally appropriate for high school, let alone about how to teach AI…
Descriptors: Instructional Design, Game Based Learning, High School Students, Artificial Intelligence
Zayet, Tasnim M. A.; Ismail, Maizatul Akmar; Almadi, Sara H. S.; Zawia, Jamallah Mohammed Hussein; Mohamad Nor, Azmawaty – Education and Information Technologies, 2023
Online learning has significantly expanded along with the spread of the coronavirus disease (COVID-19). Personalization becomes an essential component of learning systems due to students' different learning styles and abilities. Recommending materials that meet the needs and are tailored to learners' styles and abilities is necessary to ensure a…
Descriptors: Electronic Learning, Individualized Instruction, Artificial Intelligence, Cognitive Style
Ziyi Zhang – ProQuest LLC, 2023
As artificial intelligence (AI) plays a more prominent role in our everyday lives, it becomes increasingly important to introduce basic AI concepts to K-12 students. Currently, most K-12 AI research focuses on introducing fundamental AI concepts using pure virtual platforms like webpages or software. However, robots, as helpful and popular tools…
Descriptors: Artificial Intelligence, Elementary Secondary Education, Educational Technology, Robotics
Zexuan Pan; Maria Cutumisu – AERA Online Paper Repository, 2023
Computational thinking (CT) is a fundamental ability for learners in today's society. Although CT assessments and interventions have been studied widely, little is known about CT predictions. This study predicted students' CT achievement in the ICILS 2018 using five machine learning models. These models were trained on the data from five European…
Descriptors: Computation, Thinking Skills, Artificial Intelligence, Prediction