National and international policy documents emphasize the need for AI-related competencies “for all”, but there is little clarity on what these competencies should include, and determining what non-experts need to know remains a challenge. AI literacy has become a widely discussed topic in this context, often referring to a set of skills that empower individuals to critically evaluate AI, communicate and collaborate effectively with AI systems, and utilize AI as a tool across diverse contexts, including online environments, homes, schools, and workplaces. However, what AI literacy looks like in practice depends on factors such as age, level of education, and individual background. In this article, we frame AI literacy based on a qualitative analysis of the views of 33 international experts from various disciplines on what AI literacy in K-12 education should encompass. This analysis builds on existing AI literacy frameworks, with a focus on understanding and critically evaluating AI’s role in daily life, recognizing and using AI, and designing AI solutions for everyday problems. The findings show that experts emphasize a wide range of knowledge, skills, and attitudes, highlighting the importance of multiple perspectives when exploring this emerging field.

AI literacy in school education is booming within the scientific discourse of AI in education. How AI literacy is currently being framed serves diverse educational, political, and commercial purposes influencing how we imagine postdigital classrooms today and in the future. More importantly, how AI literacy emerges in primary education notably impacts how children understand AI and their own agency in a society where AI is ubiquitous. This study reviews how scientific literature conceptualises AI literacy, focusing on middle school students. An AI-adapted literacy framework (GeST) is used in the analysis to distinguish three perspectives of AI literacy (Generic, Situated, and Transformative). Forty-four papers from 2016–2024 were included in the final descriptive and qualitative analysis, showing an exponential growth in scientific papers. While still vaguely defined and poorly theorised, AI literacy materialises into different AI curricula and technology-supported teaching activities. The GeST analysis indicates that AI literacy is primarily viewed as a set of measurable skills related to generalisable theoretical knowledge that is expected to make children more competitive in a globalised and technologised world. Although some papers consider empowering students with specific competencies to challenge the AI development, critical considerations of AI in education is less visible. The paper highlights the necessity to steer the conceptualisation of AI literacy to put a stronger emphasis on critical orientations that enable students as well as teachers to examine claims about AI, and pose ethical questions to its adoption and use in classrooms and beyond.

As Artificial Intelligence-driven decision-making systems become increasingly popular, ensuring fairness in their outcomes has emerged as a critical and urgent challenge. AI models, often trained on open-source datasets embedded with human and systemic biases, risk producing decisions that disadvantage certain demographics. This challenge intensifies when multiple sensitive attributes interact, leading to intersectional bias, a compounded and uniquely complex form of unfairness. Over the years, various methods have been proposed to address bias at the data and model levels. However, mitigating intersectional bias in decision-making remains an under-explored challenge. Motivated by this gap, we propose a novel framework that leverages knowledge distillation to promote intersectional fairness. Our approach proceeds in two stages: first, a teacher model is trained solely to maximize predictive accuracy, followed by a student model that inherits the teacher's representational knowledge while incorporating intersectional fairness constraints. The student model integrates tailored loss functions that enforce parity in false positive rates and demographic distributions across intersectional groups, alongside an adversarial objective that minimizes protected attribute information within the learned representation. Empirical evaluation across multiple benchmark datasets demonstrates that we achieve a 52% increase in accuracy for multi-class classification and a 61% reduction in average false positive rate across intersectional groups and outperforms state-of-the-art models. This distillation-based methodology provides a more stable optimization opportunity than direct fairness approaches, resulting in substantially fairer representations, particularly for multiple sensitive attributes and underrepresented demographic intersections.

The generation of high-quality, long-sequenced time-series data is essential due to its wide range of applications. In the past, standalone Recurrent and Convolutional Neural Network based Generative Adversarial Networks (GAN) were used to synthesize time-series data. However, they are inadequate for generating long sequences of time-series data due to limitations in the architecture, such as difficulties in capturing long-range dependencies, limited temporal coherence, and scalability challenges. Furthermore, GANs are well known for their training instability and mode collapse problem. To address this, we propose TransFusion, a diffusion, and transformers-based generative model to generate high-quality long sequence time-series data. We extended the sequence length to 384, surpassing the previous limit, and successfully generated high quality synthetic data. Also, we introduce two evaluation metrics to evaluate the quality of the synthetic data as well as its predictive characteristics. TransFusion is evaluated using a diverse set of visual and empirical metrics, consistently outperforming the previous state-of-the-art by a significant margin.

Exploration in dynamic and uncertain real-world environments is an open problem in robotics and it constitutes a foundational capability of autonomous systems operating in most of the real-world. While 3D exploration planning has been extensively studied, the environments are assumed static or only reactive collision avoidance is carried out. We propose a novel approach to not only avoid dynamic obstacles but also include them in the plan itself, to deliberately exploit the dynamic environment in the agent's favor. The proposed planner, Dynamic AutonomousExploration Planner (DAEP), extends AEP [1] to explicitly plan with respect to dynamic obstacles. Furthermore, addressing prior errors within AEP in DAEP has resulted in enhanced exploration within static environments. To thoroughly evaluate exploration planners in dynamic settings, we propose a new enhanced benchmark suite with several dynamic environments, including large-scale outdoor environments. DAEP outperforms state-of-the-art planners in dynamic and large-scale environments and is shown to be more effective at both exploration and collision avoidance.

Exploration in dynamic and uncertain real-world environments is an open problem in robotics and it constitutes a foundational capability of autonomous systems operating in most of the real-world. While 3D exploration planning has been extensively studied, the environments are assumed static or only reactive collision avoidance is carried out. We propose a novel approach to not only avoid dynamic obstacles but also include them in the plan itself, to deliberately exploit the dynamic environment in the agent’s favor. The proposed planner, Dynamic Autonomous Exploration Planner (DAEP), extends AEP to explicitly plan with respect to dynamic obstacles. Furthermore, addressing prior errors within AEP in DAEP has resulted in enhanced exploration within static environments. To thoroughly evaluate exploration planners in such settings we propose a new enhanced benchmark suite with several dynamic environments, including large-scale outdoor environments. DAEP outperforms state-of-the-art planners in dynamic and large-scale environments and is shown to be more effective at both exploration and collision avoidance.

Synthetic data generation offers a promising solution to enhance the usefulness of Electronic Healthcare Records (EHR) by generating realistic de-identified data. However, the existing literature primarily focuses on the quality of synthetic health data, neglecting the crucial aspect of fairness in downstream predictions. Consequently, models trained on synthetic EHR have faced criticism for producing biased outcomes in target tasks. These biases can arise from either spurious correlations between features or the failure of models to accurately represent sub-groups. To address these concerns, we present Bias-transforming Generative Adversarial Networks (Bt-GAN), a GAN-based synthetic data generator specifically designed for the healthcare domain. In order to tackle spurious correlations (i), we propose an information-constrained Data Generation Process (DGP) that enables the generator to learn a fair deterministic transformation based on a well-defined notion of algorithmic fairness. To overcome the challenge of capturing exact sub-group representations (ii), we incentivize the generator to preserve sub-group densities through score-based weighted sampling. This approach compels the generator to learn from underrepresented regions of the data manifold. To evaluate the effectiveness of our proposed method, we conduct extensive experiments using the Medical Information Mart for Intensive Care (MIMIC-III) database. Our results demonstrate that Bt-GAN achieves state-of-the-art accuracy while significantly improving fairness and minimizing bias amplification. Furthermore, we perform an in-depth explainability analysis to provide additional evidence supporting the validity of our study. In conclusion, our research introduces a novel and professional approach to addressing the limitations of synthetic data generation in the healthcare domain. By incorporating fairness considerations and leveraging advanced techniques such as GANs, we pave the way for more reliable and unbiased predictions in healthcare applications.

The development of artificial intelligence has likely reached an inflection point, with significant implications for how research needs to address emerging technologies and how they drive long-term socioeconomic development of importance for climate change scenarios.

We present FairX, an open-source Python-based benchmarking tool designed for the comprehensive analysis of models under the umbrella of fairness, utility, and eXplainability (XAI). FairX enables users to train benchmarking bias-mitigation models and evaluate their fairness using a wide array of fairness metrics, data utility metrics, and generate explanations for model predictions, all within a unified framework. Existing benchmarking tools do not have the way to evaluate synthetic data generated from fair generative models, also they do not have the support for training fair generative models either. In FairX, we add fair generative models in the collection of our fair-model library (pre-processing, in-processing, post-processing) and evaluation metrics for evaluating the quality of synthetic fair data. This version of FairX supports both tabular and image datasets. It also allows users to provide their own custom datasets. The open-source FairX benchmarking package is publicly available at https://github.com/fahim-sikder/FairX.

In the last decade, there has been a growing interest in applying AI technologies to implement complex data analytics over data streams. To this end, researchers in various fields have been organising a yearly event called the "Stream Reasoning Workshop" to share perspectives, challenges, and experiences around this topic.

In this paper, the previous organisers of the workshops and other community members provide a summary of the main research results that have been discussed during the first six editions of the event. These results can be categorised into four main research areas: The first is concerned with the technological challenges related to handling large data streams. The second area aims at adapting and extending existing semantic technologies to data streams. The third and fourth areas focus on how to implement reasoning techniques, either considering deductive or inductive techniques, to extract new and valuable knowledge from the data in the stream.

This summary is written not only to provide a crystallisation of the field, but also to point out distinctive traits of the stream reasoning community. Moreover, it also provides a foundation for future research by enumerating a list of use cases and open challenges, to stimulate others to join this exciting research area.