Large language models (LLMs) are increasingly adopted in educational contexts to provide personalized support to students and teachers. The unprecedented capacity of LLM-based applications to understand and generate natural language can potentially improve instructional effectiveness and learning outcomes, but the integration of LLMs in education…

Algorithmic systems shape every aspect of our daily lives and impact our perceptions of the world. The ubiquity and profound impact of algorithms mean that algorithm literacy--awareness and knowledge of algorithm use, and the ability to evaluate algorithms critically and exercise agency when engaging with algorithmic systems--is a vital competence…

This article proposes a pragmatic approach to data justice in education that draws upon Nancy Fraser's theory. The main argument is premised on the theoretical and practical superiority of a deontological framework for addressing algorithmic bias and harms, compared to ethical guidelines. The purpose of a deontological framework is to enable the…

Purpose: This paper aims to consider the role of social media platforms as educational technologies given growing evidence of harms to democracy, society and individuals, particularly through logics of efficiency, racism, misogyny and surveillance inextricably designed into the architectural and algorithmic bones of social media. The paper aims to…

Risk of bias (RoB) assessments are a core element of evidence synthesis but can be time consuming and subjective. We aimed to develop a decision rule-based algorithm for RoB assessment of seroprevalence studies. We developed the SeroTracker-RoB algorithm. The algorithm derives seven objective and two subjective critical appraisal items from the…

Given the rapid adoption of machine learning methods by education researchers, and the growing acknowledgment of their inherent risks, there is an urgent need for tailored methodological guidance on how to improve and evaluate the validity of inferences drawn from these methods. Drawing on an integrative literature review and extending a…

As algorithmic decision making is increasingly deployed in every walk of life, many researchers have raised concerns about fairness-related bias from such algorithms. But there is little research on harnessing psychometric methods to uncover potential discriminatory bias inside decision-making algorithms. The main goal of this article is to…

The global pandemic has brought about significant changes in education, which have led to concerns regarding fairness and accessibility in a technology-driven learning environment. This article focuses on the use of Artificial Intelligence (AI) in education and examines the potential for bias in AI-powered tools. By using the example of a…

Predictive student models are increasingly used in learning environments due to their ability to enhance educational outcomes and support stakeholders in making informed decisions. However, predictive models can be biased and produce unfair outcomes, leading to potential discrimination against certain individuals and harmful long-term…

There has been a conscious effort in the past decade to produce a more theoretical account of the use of technology for learning. At the same time, advances in artificial intelligence (AI) are being rapidly incorporated into learning technologies, significantly changing their affordances for teaching and learning. In this article I address the…

Purpose: Computing technology is becoming ubiquitous within modern society and youth use technology regularly for school, entertainment and socializing. Yet, despite societal belief that computing technology is neutral, the technologies of today's society are rife with biases that harm and oppress populations that experience marginalization. While…

This article explores the efficacy of AI used for teaching and learning tools. First, it examines three critical aspects of AI use in teaching and learning: AI complexity, algorithmic transparency, and AI bias. Second, it reviews recent literature that investigates the benefits and challenges of implementing AI within college classrooms. It…

The last decade has seen a rise in big data approaches, including in the humanities, whereby large quantities of data are collected and analysed. In this paper, we discuss long-form audio recordings that result from individuals wearing a recording device for many hours. Linguists, psychologists and anthropologists can use them, for example, to…

This paper reviews algorithmic or artificial intelligence (AI) bias in education technology, especially through the lenses of speculative fiction, speculative and liberatory design. It discusses the causes of the bias and reviews literature on various ways that algorithmic/AI bias manifests in education and in communities that are underrepresented…

Predictive analytics are increasingly pervasive in higher education. However, algorithmic bias has the potential to reinforce racial inequities in postsecondary success. We provide a comprehensive and translational investigation of algorithmic bias in two separate prediction models--one predicting course completion, the second predicting degree…