In this document, we provide details and supplementary materials that cannot fit into the main manuscript due to the page limit. The specific form ofcenter distribution isunknown, but we can still train a generatorG to approximate it. IfR(G,D,T)),wechooseλ=0, i.e., no restriction onR(G,D,T)), to obtain the minimal cost. IfR(G,D,T)) >, then a large λshould be applied as apenalization. According to the derivation of Eq. (3), we obtain arelaxed versionoftheintractableEq.(2),expressedasfollows: min Inknowledge distillation, student models arecrafted using unlabeled datasets, where only thesoft targets from teachers are utilized.

Virtual Reality simulators offer a powerful tool for teacher training, yet the integration of AI-powered student avatars presents a critical challenge: determining the optimal level of avatar realism for effective pedagogy. This literature review examines the evolution of avatar realism in VR teacher training, synthesizes its theoretical implications, and proposes a new pedagogical framework to guide future design. Through a systematic review, this paper traces the progression from human-controlled avatars to generative AI prototypes. Applying learning theories like Cognitive Load Theory, we argue that hyper-realism is not always optimal, as high-fidelity avatars can impose excessive extraneous cognitive load on novices, a stance supported by recent empirical findings. A significant gap exists between the technological drive for photorealism and the pedagogical need for scaffolded learning. To address this gap, we propose Graduated Realism, a framework advocating for starting trainees with lower-fidelity avatars and progressively increasing behavioral complexity as skills develop. To make this computationally feasible, we outline a novel single-call architecture, Crazy Slots, which uses a probabilistic engine and a Retrieval-Augmented Generation database to generate authentic, real-time responses without the latency and cost of multi-step reasoning models. This review provides evidence-based principles for designing the next generation of AI simulators, arguing that a pedagogically grounded approach to realism is essential for creating scalable and effective teacher education tools.

As summer winds down and the familiar hum of school buses returns to our neighborhoods, millions of American students are gearing up for another year of learning. But as we stand on the cusp of an artificial intelligence (AI) revolution, this annual ritual is about to face a seismic shift--especially for students with intellectual and developmental disabilities (IDD). The decisions that school leaders make in the next academic year are likely to determine whether this technological wave creates more inclusive learning environments, or exacerbates existing disparities. A recent study from the Special Olympics Global Center for Inclusion in Education reveals a complex landscape of attitudes towards AI in education and a fear of leaving students with IDD behind. The study found the majority of educators (64%) and parents (77%) of students with IDD view AI as a potentially powerful mechanism to promote more inclusive learning.

How to conduct teacher training for knowledge distillation is still an open problem. It has been widely observed that a best-performing teacher does not necessarily yield the best-performing student, suggesting a fundamental discrepancy between the current teacher training practice and the ideal teacher training strategy. To fill this gap, we explore the feasibility of training a teacher that is oriented toward student performance with empirical risk minimization (ERM). Our analyses are inspired by the recent findings that the effectiveness of knowledge distillation hinges on the teacher's capability to approximate the true label distribution of training inputs. We theoretically establish that the ERM minimizer can approximate the true label distribution of training data as long as the feature extractor of the learner network is Lipschitz continuous and is robust to feature transformations. In light of our theory, we propose a teacher training method SoTeacher which incorporates Lipschitz regularization and consistency regularization into ERM. Experiments on benchmark datasets using various knowledge distillation algorithms and teacher-student pairs confirm that SoTeacher can improve student accuracy consistently.

Recent advances in using language models to obtain cross-modal audio-text representations have overcome the limitations of conventional training approaches that use predefined labels. This has allowed the community to make progress in tasks like zero-shot classification, which would otherwise not be possible. However, learning such representations requires a large amount of human-annotated audio-text pairs. In this paper, we study unsupervised approaches to improve the learning framework of such representations with unpaired text and audio. We explore domain-unspecific and domain-specific curation methods to create audio-text pairs that we use to further improve the model. We also show that when domain-specific curation is used in conjunction with a soft-labeled contrastive loss, we are able to obtain significant improvement in terms of zero-shot classification performance on downstream sound event classification or acoustic scene classification tasks.

Existing methods for distillation use the conventional training approach where all samples participate equally in the process and are thus highly inefficient in terms of data utilization. In this paper, a novel data-efficient approach to transfer the knowledge from a teacher model to a student model is presented. Here, the teacher model uses self-regulation to select appropriate samples for training and identifies their significance in the process. During distillation, the significance information can be used along with the soft-targets to supervise the students. Depending on the use of self-regulation and sample significance information in supervising the knowledge transfer process, three types of distillations are proposed - significance-based, regulated, and hybrid, respectively. Experiments on benchmark datasets show that the proposed methods achieve similar performance as other state-of-the-art methods for knowledge distillation while utilizing a significantly less number of samples.

In this session we will discuss the definition of computational thinking and how it exists in everyday life.

Despite becoming an increasingly common phrase, there is still some confusion around machine learning (ML) and how it relates to artificial intelligence (AI). Key to machine learning is data; algorithms are designed to learn from this data and then make a determination or prediction about the subject. In machine learning, computers don't have to be programmed to complete tasks, it's about getting them to actually acquire knowledge. Machine learning is a subset of the much broader world of artificial intelligence, however, AI is more focused on developing a machine that can do something that only a human would normally be able to do. In the field of education, there are many opportunities for machine learning to make an impact. However, there are also concerns that need to be addressed, not least the vast amounts of data that have to be stored and analysed in order to create effective machine learning algorithms.

On a visit to China this summer, I was asked by a local journalist if I thought their country's education and training system was ready for artificial intelligence (AI) and the fourth industrial revolution (4IR). This got me thinking about what it means for any country – let's say the UK – to be AI-ready in educational terms. In this country, there is increasing discussion and debate around the use of technology, including AI, in teaching and learning. Most teachers know the technology exists, but perhaps not necessarily how it can help them in their everyday work. Need to know: What is the fourth industrial revolution?

Informatics for all is a coalition whose aim is to establish informatics as a fundamental discipline to be taken by all students in school. Informatics should be seen as important as mathematics, the sciences, and the various languages. It should be recognized by all as a truly foundational discipline that plays a significant role in education for the 21st century. In Europe, education is a matter left to the individual states. However, education, competencies, and preparedness of the workforce are all important matters for the European Union (EU).