Data extraction is a time-consuming and resource-intensive task in the systematic review process. Natural language processing (NLP) artificial intelligence (AI) techniques have the potential to automate data extraction saving time and resources, accelerating the review process, and enhancing the quality and reliability of extracted data. In this…

With the advent of readily accessible generative artificial intelligence (GAI), a concern exists within the academic community that research data collected in the context of conducting doctoral dissertation research is authentic. The purpose of the present study was to explore the role of GAI in the production of new research paying particular…

Artificial intelligence in education (AIEd) is a fast-growing field of research. In previous work, we described efforts to explore the possible futures of AIEd by identifying key variables and their future prospects. This paper re-examines our discussions on the governance of data and the role of students and teachers by considering the…

The amount of grey literature and 'softer' intelligence from social media or websites is vast. Given the long lead-times of producing high-quality peer-reviewed health information, this is causing a demand for new ways to provide prompt input for secondary research. To our knowledge, this is the first review of automated data extraction methods or…

Learning analytics (LA) involves the measurement, collection, analysis, and reporting of data about learners and their contexts, aiming to understand and optimize both the learning process and the environments in which it occurs. Among many themes that the LA community considers, natural language processing (NLP) algorithms have been widely…

During data collection, field interviewers often append notes or comments to a case in open text fields to request updates to case-level data. Processing these comments can improve data quality, but many are non-actionable, and processing remains a costly manual task. This article presents a case study using a novel application of machine learning…

Professional and lifelong learning are a necessity for workers. This is true both for re-skilling from disappearing jobs, as well as for staying current within a professional domain. AI-enabled scaffolding and just-in-time and situated learning in the workplace offer a new frontier for future impact of AIED. The hallmark of this community's work…

This article describes the development process of the Thai cultural knowledge graph, which facilitates a more precise and rapid comprehension of the culture and customs of Thailand. The construction process is as follows: First, data collection technologies and techniques were used to obtain text data from the Wikipedia encyclopedia about cultural…

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…

Advances in Information Technology (IT) and computer science have without a doubt had a significant impact on our daily lives. The past few decades have witnessed the advancement of IT enabled processes in generating actionable insights in various fields, encouraging research based applications of modern Data Science methods. Among many other…

The exponential growth of scientific publications increases the effort required to identify relevant articles. Moreover, the scale of studies is a frequent barrier to research as the majority of studies are low or medium-scaled and do not generalize well while lacking statistical power. As such, we introduce an automated method that supports the…

With the aim to provide teachers with more specific, frequent, and actionable feedback about their teaching, we explore how Large Language Models (LLMs) can be used to estimate "Instructional Support" domain scores of the CLassroom Assessment Scoring System (CLASS), a widely used observation protocol. We design a machine learning…

Accounting for complex interactions between contextual variables and learners' individual differences in aptitudes and background requires building the means to connect and access learner data at large scales, across time, and in multiple contexts. This paper describes the ASU Learning@Scale (L@S) project to develop a digital learning network…

Introduction. Taxonomy is an effective mean of managing and accessing a large amount of digital information. Various techniques have been developed to generate taxonomy automatically. The purpose of this study is threefold: (i) review methods and approaches adopted during taxonomy generation, (ii) identify the factors influencing the choice of a…

Despite best efforts, the student experience remains poorly understood. One under-explored approach to understanding the student experience is the use of big data analytics. The reported study is a work in progress aimed at exploring the value of big data methods for understanding the student experience. A big data analysis of an open dataset of…