This dissertation examines the effectiveness of machine learning algorithms and feature engineering techniques for analyzing process data and predicting test performance. The study compares three classification approaches and identifies item-specific process features that are highly predictive of student performance. The findings suggest that…

The automated classification of examination questions based on Bloom's Taxonomy (BT) aims to assist the question setters so that high-quality question papers are produced. Most studies to automate this process adopted the machine learning approach, and only a few utilised the deep learning approach. The pre-trained contextual and non-contextual…

Short-form development is an important topic in psychometric research, which requires researchers to face methodological choices at different steps. The statistical techniques traditionally used for shortening tests, which belong to the so-called exploratory model, make assumptions not always verified in psychological data. This article proposes a…

Knowledge tracing is a well-known problem in AI for education, consisting of monitoring how the knowledge state of students changes during the learning process and accurately predicting their performance in future exercises. In recent years, many advances have been made thanks to various machine learning and deep learning techniques. Despite their…

The rapid advancement of generative AI technology offers new opportunities for the innovation and transformation of education. However, this also brings forth risks and challenges, including the potential to exacerbate educational inequality and integrity. This study aims to address the extensive controversies surrounding the application of…

This study provides research-based evidence to profile: (1) the roles of artificial intelligence in nursing; (2) its research applications; and (3) the research trends for future study. On the basis of the PRISMA statement, a series of AI and nursing education related keywords from the literature were used to retrieve high-quality journal articles…

Computer-based simulations for learning offer affordances for advanced capabilities and expansive possibilities for knowledge construction and skills application. Virtual agents, when powered by artificial intelligence (AI), can be used to scaffold personalized and adaptive learning processes. However, a synthesis or a systematic evaluation of the…

Background/purpose: With the advent of Artificial Intelligence (AI), it has become possible to invent computer systems that can perform human-like processes to tackle large data and solve complex problems. AI has manifested itself in the field of education through several technologies such as intelligent tutoring systems, adaptive…

Background/purpose: Exploring intelligent agents in digital learning raises questions about the essence of Artificial Intelligence (AI) and its potential impact on education. This article provides insights into these inquiries and outlines outcomes from various experimental implementations, emphasizing the pivotal role of intelligent agents and…

Predicting student performance using educational data is a significant area of machine learning research. However, class imbalance in datasets and the challenge of developing interpretable models can hinder accuracy. This study compares different variations of the Synthetic Minority Oversampling Technique (SMOTE) combined with classification…

This study explores the perspectives of Indian library professionals on the use of artificial intelligence (AI) in libraries. It aims to understand their knowledge, awareness, and views on AI and its challenges and opportunities. The research adopts a quantitative approach, using a closed-ended survey to collect data from 386 library professionals…

Purpose: Research shows that postsecondary students are largely unaware of the impact of algorithms on their everyday lives. Also, most noncomputer science students are not being taught about algorithms as part of the regular curriculum. This exploratory, qualitative study aims to explore subject-matter experts' insights and perceptions of the…

As blended learning moved toward a new phase during the COVID-19 pandemic, advancements in artificial intelligence (AI) technology provided opportunities to develop more diverse and dynamic blended learning. This systematic review focused on publications related to the use of AI applications in blended learning. The original studies from January…

Purpose: The purpose of this paper is to share findings from empirically driven conceptual research into the implications for English teachers of understanding generative AI as a "tool" for writing. Design/methodology/approach: The paper reports early findings from an Australian National Survey of English teachers and interrogates the…

As the artificial intelligence (AI) ethics field is currently working towards its operationalisation, ethics review as carried out by research ethics committees (RECs) constitutes a powerful, but so far underdeveloped, framework to make AI ethics effective in practice at the research level. This article contributes to the elaboration of research…