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Nurassyl Kerimbayev; Karlygash Adamova; Rustam Shadiev; Zehra Altinay – Smart Learning Environments, 2025
This review was conducted in order to determine the specific role of intelligent technologies in the individual learning experience. The research work included consider articles published between 2014 and 2024, found in Web of Science, Scopus, and ERIC databases, and selected among 933 ?articles on the topic. Materials were checked for compliance…
Descriptors: Intelligent Tutoring Systems, Artificial Intelligence, Computer Software, Databases
Linghong Li; Wayne F. Patton – Journal of Educational Technology Systems, 2025
The evolving landscape of higher education demands integrating advanced technologies to foster engaging and inclusive learning environments. This paper examines the practical integration of Stellarium, a virtual planetarium software, and ChatGPT, an AI conversational agent, in an asynchronous online undergraduate astronomy course. Stellarium…
Descriptors: Electronic Learning, Astronomy, Science Education, Artificial Intelligence
Lei Shi – Journal of Educational Computing Research, 2025
This study explores the integration of advanced AI technologies, including emotion detection and adaptive learning systems, to enhance second language acquisition among 274 English as a Foreign Language (EFL) learners. Utilizing a pretest-posttest randomized control trial, the research evaluates the effects of AI-enhanced interventions on…
Descriptors: Artificial Intelligence, Technology Uses in Education, Emotional Response, Intelligent Tutoring Systems
Conrad Borchers; Tianze Shou – Grantee Submission, 2025
Large Language Models (LLMs) hold promise as dynamic instructional aids. Yet, it remains unclear whether LLMs can replicate the adaptivity of intelligent tutoring systems (ITS)--where student knowledge and pedagogical strategies are explicitly modeled. We propose a prompt variation framework to assess LLM-generated instructional moves' adaptivity…
Descriptors: Benchmarking, Computational Linguistics, Artificial Intelligence, Computer Software
Anitia Lubbe; Elma Marais; Donnavan Kruger – Education and Information Technologies, 2025
Amalgamating generative artificial intelligence (Gen AI), Bloom's taxonomy and critical thinking present a promising avenue to revolutionize assessment pedagogy and foster higher-order cognitive skills needed for learning autonomy in the domain of self-directed learning. Gen AI, a subset of artificial intelligence (AI), has emerged as a…
Descriptors: Critical Thinking, Computer Software, Learning Analytics, Intelligent Tutoring Systems
Ling Zhang; Zijun Yao; Arya Hadizadeh Moghaddam – Journal of Teacher Education, 2025
Educator preparation, personalized learning (PL) implementation, and applications of Generative AI converge as three interrelated systems that, when carefully designed, can help achieve the long-sought goal of providing inclusive education for all learners. However, realizing this potential comes with challenges resulting from theoretical…
Descriptors: Artificial Intelligence, Computer Software, Technology Integration, Faculty Development
Moses Kumi Asamoah; Jessica Amarteifio – Discover Education, 2025
This systematic review explores the use of Intelligent Tutoring Systems (ITS) in fostering creativity, innovation, and personalized learning experiences among university students in Ghana. The review also examines the challenges associated with the implementation of ITS, along with the ethical considerations involved. Employing an interpretive…
Descriptors: Ethics, Barriers, Intelligent Tutoring Systems, Technology Integration
Terry L. Howard; Gregory W. Ulferts – Research in Higher Education Journal, 2025
Artificial Intelligence (AI) is profoundly reshaping higher education by introducing innovative tools and systems that enhance learning outcomes, streamline administrative processes, and address global educational challenges. This white paper examines AI's transformative impact on higher education, drawing on a comprehensive analysis of empirical…
Descriptors: Artificial Intelligence, Higher Education, Computer Software, Policy Formation
Peer reviewedDevika Venugopalan; Ziwen Yan; Conrad Borchers; Jionghao Lin; Vincent Aleven – Grantee Submission, 2025
Caregivers (i.e., parents and members of a child's caring community) are underappreciated stakeholders in learning analytics. Although caregiver involvement can enhance student academic outcomes, many obstacles hinder involvement, most notably knowledge gaps with respect to modern school curricula. An emerging topic of interest in learning…
Descriptors: Homework, Computational Linguistics, Teaching Methods, Learning Analytics
Murat Polat; Ibrahim Hakan Karatas; Nurgün Varol – Leadership and Policy in Schools, 2025
The incorporation of artificial intelligence (AI) into educational management offers personalized learning, adaptive tutoring, and efficient resource management. However, ethical considerations such as fairness, transparency, accountability, and privacy are crucial. This paper reviews literature and conducts a bibliometric analysis on ethical AI…
Descriptors: Ethics, Artificial Intelligence, Technology Uses in Education, Leadership
Conrad Borchers; Alex Houk; Vincent Aleven; Kenneth R. Koedinger – Grantee Submission, 2025
Active learning promises improved educational outcomes yet depends on students' sustained motivation to engage in practice. Goal setting can enhance learner engagement. However, past evidence of the effectiveness of setting goals tends to be limited to non-digital learning settings and does not scale well as it requires active teacher or parent…
Descriptors: Learner Engagement, Educational Benefits, Goal Orientation, Rewards
Nikola M. Luburic; Luka Z. Doric; Jelena J. Slivka; Dragan Lj. Vidakovic; Katarina-Glorija G. Grujic; Aleksandar D. Kovacevic; Simona B. Prokic – IEEE Transactions on Learning Technologies, 2025
Software engineers are tasked with writing functionally correct code of high quality. Maintainability is a crucial code quality attribute that determines the ease of analyzing, modifying, reusing, and testing a software component. This quality attribute significantly affects the software's lifetime cost, contributing to developer productivity and…
Descriptors: Intelligent Tutoring Systems, Coding, Computer Software, Technical Occupations
Jonathan Brazil; Suijing Yang; Fabienne van der Kleij – Australian Council for Educational Research, 2025
This document provides guiding principles and practical examples for using AI in teaching and learning. Underpinned by a human-centred approach, the PATH principles serve as key guidance to ensure the ethical and effective integration of AI systems into teaching and learning. The PATH principles are: Promote teaching and learning; Advance…
Descriptors: Artificial Intelligence, Computer Software, Technology Integration, Educational Principles
Bunyamin Celik; Yunus Yildiz; Saban Kara – Australian Journal of Applied Linguistics, 2025
Expressing thoughts and feelings efficiently is fundamental in daily, academic, and professional life. Accordingly, self-efficacy beliefs play pivotal roles in shaping learners' speaking performance through various dimensions. Higher education institutions assume responsibility for boosting students' speaking self-efficacy, thereby contributing to…
Descriptors: Self Efficacy, English (Second Language), Second Language Instruction, Second Language Learning
Kole A. Norberg; Husni Almoubayyed; Logan De Ley; April Murphy; Kyle Weldon; Steve Ritter – International Journal of Artificial Intelligence in Education, 2025
Large language models (LLMs) offer an opportunity to make large-scale changes to educational content that would otherwise be too costly to implement. The work here highlights how LLMs (in particular GPT-4) can be prompted to revise educational math content ready for large scale deployment in real-world learning environments. We tested the ability…
Descriptors: Artificial Intelligence, Computer Software, Computational Linguistics, Educational Change
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