Predicting learner performance with precision is critical within educational systems, offering a basis for tailored interventions and instruction. The advent of big data analytics presents an opportunity to employ Machine Learning (ML) techniques to this end. Real-world data availability is often hampered by privacy concerns, prompting a shift…

This paper discusses the ethical challenges presented by artificial intelligence (AI) within the instructional process for students with disabilities. The authors briefly overview technological changes in education and applications of AI in education in addition to policy. Discussion is directed toward the development of an ethical pedagogy using…

Purpose: This paper aims to consolidate empirical studies between 2013 and 2022 to investigate the impact of artificial intelligence (AI) in higher education. It aims to examine published research characteristics and provide insights into the promises and challenges of AI integration in academia. Design/methodology/approach: A systematic…

Artificial intelligence (AI), utilizing machine learning techniques to 'learn' from data, as humans learn from experiences, has the promise to perform various cognitive tasks, such as learning, thinking, problem-solving, and decision-making, which usually demands human cognition. In recent decades, the remarkable advancements in AI have led to its…

The autoethnographic study investigates the transformative impact of generative AI on educational research, instructional design, and teaching practices over a 5-month period (May-October 2024). By integrating AI tools into every phase of the research process, the study examines AI's role as both a research partner and a subject of inquiry. Field…

Online education has been expected to be the future of learning; it will never replace the value of traditional classroom experiences fully. Technical problems have less impact on offline education, which gives students more freedom to plan their time and stick to it. In addition, teachers cannot observe their students' behavior and activities…

Classroom engagement is a critical factor for evaluating students' learning outcomes and teachers' instructional strategies. Traditional methods for detecting classroom engagement, such as coding and questionnaires, are often limited by delays, subjectivity, and external interference. While some neural network models have been proposed to detect…

As AI increasingly permeates education, understanding teachers' perceptions and interactions with these technologies is essential. The Technological Pedagogical Content Knowledge (TPACK) framework provides a comprehensive lens to examine these interactions with educational AI tools (EAITs). This study explored the factors influencing teachers'…

To effectively avoid subjective bias in manual evaluation. This article proposes a MIDI piano teaching performance evaluation method based on bidirectional LSTM. This method utilizes a three-layer bidirectional LSTM neural network mechanism to make it easier for the model to capture useful information. In addition, the Spark clustering training…

This comprehensive bibliometric analysis examines the dynamic impact and influence of artificial intelligence (AI) within the domain of English as a Foreign Language (EFL) from 2013 to 2023. By analysing 3,300 documents from the Web of Science database, the study reveals a positive trend in AI integration, with notable growth attributed to various…

Skilled orators and their public speaking skills have been held in high esteem since antiquity. Artificial intelligence-powered presentation platforms (AI-PPPs) are an emerging educational technology that provides a virtual venue and AI audience for users to practice a presentation or pitch and receive feedback to improve their public speaking…

This study examines the influence of AI-powered and emerging technologies on pedagogical practices in higher education, focusing on their role on behavioural intention (BI) and actual usage among educators and students. The research hypothesises that the relationship between each Unified Theory of Acceptance and Use of Technology (UTAUT)…

In using large language models (LLMs) for education, such as distractors in multiple-choice questions and learning by teaching, error-containing content is used. Prompt tuning and retraining LLMs are possible ways of having LLMs generate error-containing sentences in the learning content. However, there needs to be more discussion on how to tune…

This qualitative study examines the risks educators in Jordan face with the integration of ChatGPT, an emerging AI technology, into academic settings. While considerable attention has been given to risks affecting university students, there remains a gap in understanding the specific challenges encountered by educators themselves. Through…

Despite extensive research on the concerns with the use of ChatGPT that evolve around cheating, academic integrity, and plagiarism, very little is known on the assessment practices in school history, obscuring our understanding on how history teachers can assess students amid ChatGPT driven challenges. This conceptual paper discusses the…