In light of fast-growing popular, political and professional discourses around AI in education, this article outlines five broad areas of contention that merit closer attention in future discussion and decision-making. These include: (1) taking care to focus on issues relating to 'actually existing' AI rather than the overselling of speculative AI…

We consider the problem of assessing the changing performance levels of individual students as they go through online courses. This student performance modeling problem is a critical step for building adaptive online teaching systems. Specifically, we conduct a study of how to utilize various types and large amounts of log data from earlier…

The application of artificial intelligence (AI) in STEM education (AI-STEM), as an emerging field, is confronted with a challenge of integrating diverse AI techniques and complex educational elements to meet instructional and learning needs. To gain a comprehensive understanding of AI applications in STEM education, this study conducted a…

As big data algorithm usage becomes more ubiquitous, it will become critical for all young people, particularly those from historically marginalized populations, to have a deep understanding of data science that empowers them to enact change in their local communities and globally. In this study, we explore the concept of critical machine…

The context for this paper is the "Synthetic Training Environment Experiential Learning -- Readiness" (STEEL-R) project [1], which aims to estimate individual and team competence using data collected from synthetic, semi-synthetic, and live scenario-based training exercises. In STEEL-R, the "Generalized Intelligent Framework for…

Machine learning is a powerful method for predicting the outcomes of interactions with educational software, such as the grade a student is likely to receive. However, a predicted outcome alone provides little insight regarding how a student's experience should be personalized based on that outcome. In this paper, we explore a generalizable…

Computing has become an essential part of today's primary and secondary school curricula. In recent years, K-12 computer education has shifted from computer science itself to the broader perspective of computational thinking (CT), which is less about technology than a way of thinking and solving problems--"a fundamental skill for everyone,…

When working with multimodal Bayesian posterior distributions, Markov chain Monte Carlo (MCMC) algorithms have difficulty moving between modes, and default variational or mode-based approximate inferences will understate posterior uncertainty. And, even if the most important modes can be found, it is difficult to evaluate their relative weights in…

The objective of this study is to survey existing and emerging post-secondary computing and technology programs and employment trends in Pennsylvania to determine if college graduates are being prepared for careers in artificial intelligence. Due to low enrollment, colleges and universities are continually revising or restructuring their existing…

The article analyzes the main problems and contradictions in the formation of legal regimes for the regulation of robotics, artificial intelligence and other innovative technologies of our time. The work content shows the unpreparedness of modern legal science and practice to conceptual and legal design, legal and technical development of the…

Education data scientists, learning engineers and precision education specialists are new experts in knowledge production in educational research. By bringing together data science methodologies and advanced artificial intelligence (AI) systems with disciplinary expertise from the psychological, biological and brain sciences, they are building a…

In order to effectively grade persuasive writing we must be able to reliably identify and extract extract argument structures. In order to do this we must classify arguments by their structural roles (e.g., major claim, claim, and premise). Current approaches to classification typically rely on statistical models with heavy feature-engineering or…

Presently, the universities are under exceeding strain of increasing faculty engagement and job satisfaction since due to COVID-19 the faculty is being overwhelmed by having to adapt to new ways of teaching (online, hybrid, flip, etc.) while conducting other activities such as administrative work, service, research, and continuous learning. This…

Understanding a student's problem-solving strategy can have a significant impact on effective math learning using Intelligent Tutoring Systems (ITSs) and Adaptive Instructional Systems (AISs). For instance, the ITS/AIS can better personalize itself to correct specific misconceptions that are indicated by incorrect strategies, specific problems can…

Artificial intelligence (AI) education for K-12 students is an emerging necessity, owing to the rapid advancement and deployment of AI technologies. It is essential to take teachers' perspectives into account when creating ecologically valid AI education programmes for K-12 settings. However, very few studies investigated teacher perception of AI…