The use of artificial intelligence (AI) in higher education has mostly focused on issues associated with teaching and assessment. In this paper I used AI to support the analysis of data which consisted of public comments on a newspaper article. This small, low risk research was chosen to demonstrate the potential use of AI and how it may support…

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…

This chapter investigates the integration of generative AI (GenAI), specifically ChatGPT, into institutional and course-level assessment at Western New England University. It explores the potential of GenAI to streamline the assessment process, making it more efficient, equitable, and objective. Through the development of a proprietary GenAI tool,…

Recent developments in human-computer interaction technologies raised the attention towards gamification techniques, that can be defined as using game elements in a non-gaming context. Furthermore, advancement in machine learning (ML) methods and its potential to enhance other technologies, resulted in the inception of a new era where ML and…

The aim of this paper is to survey recent research publications that use Soft Computing methods to answer education-related problems based on the analysis of educational data 'mined' mainly from interactive/e-learning systems. Such systems are known to generate and store large volumes of data that can be exploited to assess the learner, the system…

This review explores the integration and effects of the Internet of Things (IoT) in education, highlighting its importance in transforming traditional teaching and learning techniques. It examines the early uses and historical growth of IoT, its development, and the turning points in its adoption. It explores IoT platforms, tools, and technologies…

This paper examines visions of 'learning' across humans and machines in a near-future of intensive data analytics. Building upon the concept of 'learnification', practices of 'learning' in emerging big data-driven environments are discussed in two significant ways: the "training" of machines, and the "nudging" of human…

The arc of quantitative educational research should not be etched in stone but should adapt and change over time. In this article, I argue that it is time for a reshaping by offering my personal view of the past, present and future of our field. Educational research--and research in the social and life sciences--is at a crossroads. There are many…

The introduction of artificial intelligence in education (AIED) is likely to have a profound impact on the lives of children and young people. This article explores the different types of artificial intelligence (AI) systems in common use in education, their social context and their relationship with the growth of commercial knowledge monopolies.…

Responsible Operations is intended to help chart library community engagement with data science, machine learning, and artificial intelligence (AI) and was developed in partnership with an advisory group and a landscape group comprised of more than 70 librarians and professionals from universities, libraries, museums, archives, and other…

The initial vision for intelligent tutoring systems involved powerful, multi-faceted systems that would leverage rich models of students and pedagogies to create complex learning interactions. But the intelligent tutoring systems used at scale today are much simpler. In this article, I present hypotheses on the factors underlying this development,…

This paper provides a novel, conceptually driven stance on the state of the contemporary analytic challenges faced in the treatment of dialogue as a form of data across on- and offline sites of learning. In prior research, preliminary steps have been taken to detect occurrences of such dialogue using automated analysis techniques. Such advances…

How does AI&EdAIED today compare to 25 years ago? This paper addresses this evolution by identifying six trends. The trends are ongoing and will influence learning technologies going forward. First, the physicality of interactions and the physical space of the learner became genuine components of digital education. The frontier between the…

Learning analytics and educational data mining are introducing a number of new techniques and frameworks for studying learning. The scalability and complexity of these novel techniques has afforded new ways for enacting education research and has helped scholars gain new insights into human cognition and learning. Nonetheless, there remain some…

Clustering is one of the most common data mining tasks, used frequently for data organization and analysis in various application domains. Traditional machine learning approaches to clustering are fully automated and unsupervised where class labels are unknown a priori. In real application domains, however, some "weak" form of side…