ChatGPT, as the pioneer of advanced generative AI tools, has triggered scholarly discussions about the potential use of such AI technologies in interdisciplinary fields. With a focus on the surge of AI-related preprints since the introduction of ChatGPT by OpenAI, the study investigated what the surge implies for AI literature, particularly in terms of credibility and quality. A scoping review was initially conducted to study the characteristics of the AI-related preprints in the Web of Science (WoS) database and also in five (5) preprint platforms (ArXiv, MedRxiv, SocArxiv, SSRN, and Research Square). The publication date range was set at January 01, 2023 to September 08, 2023. This was followed up by an interpretive phenomenological analysis (IPA) of the perceptions of experts in the AI field about the preprints. Employing a scoping review of AI-related preprints across six databases and a qualitative analysis of 15 AI experts’ opinions, our study reveals concerns about the research accuracy, quality, and credibility of preprints, and advocates for a robust evaluation and high-quality assurance process to promote open science objectives during their dissemination. Specifically, 45,918 AI-related preprints were found in the six preprint databases or repositories across different fields. The nine themes from the IPA showed that preprints can be of value. However, experts advocated for the safe and responsible use of AI-related preprints, involving such tenets as maintaining ethical integrity and high-quality work on the part of authors and establishing sound AI-content guidelines from publishers and editors. Future studies are recommended to investigate the impact of preprints on decision-making processes in educational research and practice.

Recent advanced AI technologies, especially large language models (LLMs) like GPTs, have significantly advanced the field of data mining and led to the development of various LLM-based applications. AI for education (AI4EDU) is a vibrant multi-disciplinary field of data mining, machine learning, and education, with increasing importance and extraordinary potential. In this field, LLM and adaptive learning-based models can be utilized as interfaces in human-in-the-loop education systems, where the model serves as a mediator among the teacher, students, and machine capabilities, including its own. This perspective has several benefits, including the ability to personalize interactions, allow unprecedented flexibility and adaptivity for human-AI collaboration and improve the user experience. However, several challenges still exist, including the need for more robust and efficient algorithms, designing effective user interfaces, and ensuring ethical considerations are addressed. This workshop aims to bring together researchers and practitioners from academia and industry to explore cutting-edge AI technologies for personalized education, especially the potential of LLMs and adaptive learning technologies.

Recently we have seen a rapid rise in the amount of education data available through the digitization of education. This huge amount of education data usually exhibits in a mixture form of images, videos, speech, texts, etc. It is crucial to consider data from different modalities to build successful applications in AI in education (AIED). This tutorial targets AI researchers and practitioners who are interested in applying state-of-the-art multimodal machine learning techniques to tackle some of the hard-core AIED tasks. These include tasks such as automatic short answer grading, student assessment, class quality assurance, knowledge tracing, etc.

Systematic investigation of the collaborative problem solving process in open-ended, hands-on, physical computing design tasks requires a framework that highlights the main process features, stages and actions that then can be used to provide 'meaningful' learning analytics data. This paper presents an analysis framework that can be used to identify crucial aspects of the collaborative problem solving process in practice-based learning activities. We deployed a mixed-methods approach that allowed us to generate an analysis framework that is theoretically robust, and generalizable. Additionally, the framework is grounded in data and hence applicable to real-life learning contexts. This paper presents how our framework was developed and how it can be used to analyse data. We argue for the value of effective analysis frameworks in the generation and presentation of learning analytics for practice-based learning activities.

Constructive emotional experiences are strongly related to effective learning. Yet, it is challenging for teachers, researchers and students alike to understand the emotions experienced in the classroom setting. Advances in wireless and sensor technologies open up possibilities for better supporting emotions. However, little work has explored how affective technologies in the classroom might operate. This paper describes a study where 15 high school students used the Subtle Stone: a tangible technology designed to support students' active emotional communication in the classroom. We report on how the students used and experienced this technology, and the values they demonstrated through this use: flexibility, privacy, agency, voice and reflection. We conclude by examining future possibilities for affective technologies in the classroom.

An assumption behind much work on the use of tangibles for learning is that there are individual cognitive benefits related to the physical manipulation of materials. However, previous work that has shown learning benefits in using physical materials often hasn't adequately controlled for the covariates of physicality.

In order to build learning systems that care we need to increase our understanding of the affective and motivational dimensions of learning. This will allow us to develop a model of our learners which extends beyond their behaviours to the feelings and motivations which underlie those behaviours. In this paper we focus on children's (10 yrs) help-seeking behaviour when using an interactive learning environment (ILE) called Ecolab II. We relate help-seeking strategies to lachievement goal orientation (AGO) by constructing detailed child profiles and directly observing behaviour.

Integration of e-Science and Grid technologies into curriculum teaching is currently an ambitious aim for teachers and school infrastructures to organise. However, it can expose classroom learners and teachers to a wider community of specialists and interested others, enriching the classroom experience beyond the knowledge of the local teacher. This paper reflects on two practical e-Science projects that utilised mobile hand-held technologies to bring the concepts of collaborative e-Science and the Grid to young scientists. Students engaged in hands-on exploration of their surroundings, and were able to communicate with pollution specialists and with a remote classroom of children who had used similar sensors. Communication and data sharing activities in these sessions exposed a requirement for a suite of tools and technologies not currently accessible to schools. From qualitative analysis of data across these two projects, we present a collection of supporting tools to help achieve this aim and future research direction.

This papers explores the theme of the conference: "Learning communities on the Internet-pedagogy in implementation". It discusses the changing nature of both interactive educational content and the technology that can engage and connect learners to knowledge resources both human and artefact. This exploration of technological and user diversity combined with the possibilities afforded by interactive learning resources is used to specify an underlying pedagogical framework that can guide the creation of effective learning experiences across different communities of practice.

Narrative is fundamental to the ways we make sense of texts of all kinds because it provides structure and coherence, but it is difficult to see how this works in the context of multimedia interactive learning environments (MILES). We tested our hypotheses about the form and function of narrative in MILES by developing three versions of material on CD-ROM which had different narrative structures and analysed the impact of the different versions on learner behaviour. We present a theoretical framework in which we explain the concepts of narrative guidance and narrative construction and their application to the design of MILES.