AI technologies have long-term societal implications that impact youth, prompting a need for critical AI literacy for students. While current K-12 AI curricula have increasingly integrated societal impact and ethics concepts in AI curricula, there is a need to center youth’s agency in decision-making around AI systems that impact them. In this work, we engaged 94 middle and high school art students in a Policy Design learning activity as a part of an Art and AI learning workshop. Students worked in groups to create policies around AI's use in art, considering stakeholders like artists, AI companies, and consumers. Findings revealed that students developed nuanced, actionable policies that reflected a deep understanding of AI's impact on the art ecosystem, including issues of copyright, artist compensation, and transparency. The activity empowered students to think critically about AI’s ethical implications on various systems in the AI and art ecosystem and fostered a sense of agency in shaping its future. This work demonstrates the value of integrating policy design into K-12 AI curricula, providing youth with the skills and perspectives to become informed, ethical citizens in an AI-driven world.

Automated mosquito species identification is critical for combating vector-borne diseases. We introduce Q-MoFusion, a novel hybrid quantum-classical framework that fuses deep features from pre-trained Audio Spectrogram Transformer (AST) and Whisper models using a Variational Quantum Circuit (VQC). Our approach significantly outperforms individual backbones and prior state-of-the-art benchmarks, demonstrating superior accuracy and robustness, particularly on imbalanced classes. Q-MoFusion demonstrates the potential of hybrid quantum computing to enhance bioacoustic surveillance for addressing critical public health challenges.

Ongoing calls for critical data literacy emphasize the importance of designing learning opportunities for youth to build relationships with data as its author and analyst.By generating and interpreting personal and embodied data, learners can develop relational lenses about data to support these types of skills.We apply actor network theory to analyze teen interactions in a summer program on sports technologies, where youth generate, visualize, and interpret their group's personal movement data.We examine youth's arguments about what data visualizations can depict through qualitative coding of their presentations.We notice youth relying on data as an actor within their arguments and mixing the data'a actions with their remembered experiences.As collaborative actors, youth and data engage in social sensemaking with the data.We offer this frame to highlight the plurality of participation across actors.This contributes to our understanding of community data science practices for teaching and learning.We offer this as an analytical lens for researchers and educators to pay attention to, in their designs and implementations of data literacy tools and activities.Specifically, how to notice and help learners explicate the multiple actors with diverse relationships engaged in making sense of data, and how engaging youth in group, embodied data activities enables unique personal and social ways of data interpretations.

Deep Neural Networks (DNNs), backbone architecture in ‘almost’ every computer vision task, are vulnerable to adversarial attacks, particularly physical out-of-distribution (OOD) adversarial patches. Existing defense models often struggle with interpreting these attacks in ways that align with human visual perception. Our proposed AdvPatchXAI approach introduces a generalized, robust, and explainable defense algorithm designed to defend DNNs against physical adversarial threats. AdvPatchXAI employs a novel patch decorrelation loss that reduces feature redundancy and enhances the distinctiveness of patch representations, enabling better generalization across unseen adversarial scenarios. It learns prototypical parts self-supervised, enhancing interpretability and correlation with human vision. The model utilizes a sparse linear layer for classification, making the decision process globally interpretable through a set of learned prototypes and locally explainable by pinpointing relevant prototypes within an image. Our comprehensive evaluation shows that AdvPatchXAI closes the "semantic" gap between latent space and pixel space and effectively handles unseen adversarial patches even perturbed with unseen corruptions, thereby significantly advancing DNN robustness in practical settings1.

Amidst the burgeoning landscape of thousands of deep neural networks (DNNs), selecting a robust architecture for image classification poses a formidable challenge. The prime reason is the vulnerability of these DNNs to image corruption. Surprisingly, the literature still does not understand which network is sensitive to which kind of corruption and to what extent. Our study rigorously analyzes DNNs across the classification spectrum, from pure convolutional neural networks (CNNs: without attention layers) to state-of-the-art Vision Transformers (ViTs). To reach a robust conclusion, we have performed extensive experiments using 18 DNNs, 5 diverse datasets, and 15 corruption types of varying severity. Our analysis uncovers insightful and surprising findings concerning the robustness of different DNNs across corruption types. For example, it is observed that the ViT trained on CIFAR10 is found to be highly robust in handling noise corruptions, even of considerable severity (say, severity 4), but is found vulnerable to environmental corruptions of the same severity. At the same time, while the performance of pure CNNs is lower than that of ViT, they can handle environmental factors better than noise corruption. Another interesting observation showcased that these corruptions can even fool one of the popular network explainability algorithms, Grad-CAM. The heat map highlights the same region of interest on the noisy image, similar to clean images, but the class label differs. Shedding light on the vulnerabilities of deep learning models and revealing their vulnerability against particular corruption ensures the deployment of a correct network in the real world that deals with specific corruption frequently.

Can the general-purpose pre-trained image classifier be effective for biometric recognition? Due to the prevalence of generic features in these models, we assert that they can be used to extract the discriminating feature of the biometrics images which help encode the object images. The utilization of features from the pre-trained model can significantly limit the training requirements and can be deployed on computationally limited devices. In this research, we have conducted an extensive study to perform biometric recognition using existing state-of-the-art pre-trained deep neural networks. The experiments are not limited to any modality or imaging spectrum to ensure the findings are trustworthy and thorough. The experiments conducted on various modalities including multi-modal biometric recognition as well showcase that the pre-trained deep networks can be a suitable option for cost-effective biometric recognition. Further, we have also evaluated the fusion of deep neural networks to see if the performance can further be improved without significantly increasing the computational cost.

An essential element of K-12 AI literacy is educating learners about the ethical and societal implications of AI systems. Previous work in AI ethics literacy have developed curriculum and classroom activities that engage learners in reflecting on the ethical implications of AI systems and developing responsible AI. There is little work in using game-based learning methods in AI literacy. Games are known to be compelling media to teach children about complex STEM concepts. In this work, we developed a competitive card game for middle and high school students called “AI Audit” where they play as AI start-up founders building novel AI-powered technology. Players can challenge other players with potential harms of their technology or defend their own businesses by features that mitigate these harms. The game mechanics reward systems that are ethically developed or that take steps to mitigate potential harms. In this paper, we present the game design, teacher resources for classroom deployment and early playtesting results. We discuss our reflections about using games as teaching tools for AI literacy in K-12 classrooms.

Culturally relevant and sustaining implementations of computing education are increasingly leveraging young learners' passion for sports as a platform for building interest in different STEM (Science, Technology, Engineering, and Math) concepts. Numerous disciplines spanning physics, engineering, data science, and especially AI based computing are not only authentically used in professional sports in today's world, but can also be productively introduced to introduce young learnres to these disciplines and facilitate deep engagement with the same in the context of sports. In this work, we present a curriculum that includes a constellation of proprietary apps and tools we show student athletes learning sports like basketball and soccer that use AI methods like pose detection and IMU-based gesture detection to track activity and provide feedback. We also share Scratch extensions which enable rich access to sports related pose, object, and gesture detection algorithms that youth can then tinker around with and develop their own sports drill applications. We present early findings from pilot implementations of portions of these tools and curricula, which also fostered discussion relating to the failings, risks, and social harms associated with many of these different AI methods – noticeable in professional sports contexts, and relevant to youths' lives as active users of AI technologies as well as potential future creators of the same.

In this demo, we present PaintBall, a tool that facilitates creative art creation and manipulation from movement data. It is designed as a public computation project aimed to foster conversation and connection between youth with different interests (specifically art, sports, or computing) at a community center around the shared activity of creating these rich visualizations and art pieces. We expect the design features of PaintBall – public art and tinkering work, cross interest engagement, and discrete artifact generation – to be key ideas that can be used across a variety of contexts and enable rich community development among youth by providing novel touchstones for conversation as well as reflection on these preexisting activities themselves. The following URL will host a working demo of this project – https://tiilt.northwestern.edu/projects/sportsense/paintball

This paper examines facilitator and refugee youth co-design for a summer program and asks: “how do researchers and interns understand ‘facilitation’ and ‘co-design’ in a summer program with refugee youth?” We highlight interactions with Rbekka, a refugee youth who participated in programming since 2015. We share findings that help us understand how expansive social emotional learning (SEL) and co-design create an assemblage of relationships and mutual space of learning that resists adult/child binaries and puts relational care and justice into practice.

In this paper, we propose a new metric – Joint Choice Time (JCT) – to measure how and when visitors are collaborating around an interactive museum exhibit. This extends dwell time, one of the most commonly used metrics for museum engagement – which tends to be individual, and sacrifices insight into activity and learning details for measurement simplicity. We provide an exemplar of measuring JCT using a common “diversity metric” for collaborative choices and potential outcomes. We provide an implementable description of the metric, results from using the metric with our own data, and potential implications for designing museum exhibits and easily measuring social engagement. Here, we apply JCT to an interactive exhibit game called “Rainbow Agents” where museum visitors can play independently or work together to tend to a virtual garden using computer science concepts. Our data showed that diversity of meaningful choices positively correlated with both dwell time and diversity of positive and creative outcomes. JCT - as a productive as well as easy to access measure of social work - provides an example for learning analytics practitioners and researchers (especially in museums) to consider centering social engagement and work as a rich space for easily assessing effective learning experiences for museum visitors.

Here, we present findings from our research study conducted on a series of STEM workshops we developed and implemented in a local summer program for young Black boys. Data were collected and analyzed from surveys, student work, and field observations. Our preliminary findings suggest that youth began to acknowledge benefits in having both tech and sports within the same learning space. Specifically, they began to connect digital technologies with sports and consider them as mediating tools for acquiring and refining athletics skills.

Research on bridging sports and computer science education tends to center computing as primary, and sports as secondary. In contrast, this project aims to center sports as primary while introducing technology and computing as avenues to engage with sports more deeply. We collaborated with basketball coaches to design and implement campamento:bit, a summer program with a basketball team of Latinx students in Puerto Rico. This program integrated computing artifacts in a traditional basketball camp. We present insights from our experience testing this program with 11 participants over 5 days. We present select case studies of participant experiences and developing understandings using a qualitative-methods approach. These analyses present different shifts in athletes’ perceptions of computing and instantiated students’ interests in future possibilities involving computing. Furthermore, the analyses suggest strategies for organizing such technology-integrated experiences for youth in sports contexts.

In this paper, we present the motivation for, and design of, City Settlers, a participatory simulation. In City Settlers learners engage in collaborative embodied play, competition, and sensemaking within the domains of sustainability, environmental complex systems, and city building. Learners work in teams running separate but interconnected cities, and need to deal with the interrelatedness of economic, ecological, and social systems which are integral to understanding sustainable development. Competing over some shared resources, and developing ad-hoc alliances during play -- players have the ability to optimize for different goals (industrial progress, agricultural progress, or social longevity). Learners are also scaffolded to reflect on the ways different desired goals lead to different individual and collective outcomes.

Although educators, researchers, and designers have increasingly advocated for developing computational thinking (CT) in young children, the vast majority of CT learning environments fail to support the development of positive attitudes towards problem solving, confidence in dealing with complexity, and communicating and working with others to achieve a goal. To address this issue, our design team developed a music-based puzzle game called SynthSync. The game challenges players to work collaboratively to "debug" jumbled musical compositions through close listening, tinkering, and communication. SynthSync players manipulate controls to adjust musical variables (pitch, note length, and the length of rests) in arhythmic and dissonant musical puzzles based on popular songs until they "discover" the original piece of music.

This paper presents Connected Spaces (C-S) -- a tool designed to promote collaboration in makerspaces. It also describes a pilot study designed to test C-S's effectiveness in enabling people to seek help from peers. In our pilot, some (but not all) students were able to leverage C-S's affordances. We explore both supporting and mitigating factors, and highlight design features for environments as well as tools to support divergent, open-ended exploration and learning.

In this paper, we describe a tablet application designed around an interactive game-based science museum exhibit. It is aimed to help provide museum docents useful information about the visitors' actions, in a way that is actionable, and enables docents to provide assistance and prompts to visitors that are more meaningful, compared to what they are typically able to do without this interface augmentation.

We present the design of Note Code -- a music programming puzzle game designed as a tangible device coupled with a Graphical User Interface (GUI). Tapping patterns and placing boxes in proximity enables programming these "note-boxes" to store sets of notes, play them back and activate different sub-components or neighboring boxes. This system provides users the opportunity to learn a variety of computational concepts, including functions, function calling and recursion, conditionals, as well as engage in composing music. The GUI adds a dimension of viewing the created programs and interacting with a set of puzzles that help discover the various computational concepts in the pursuit of creating target tunes, and optimizing the program made.

The 2014 i2b2/UTHealth Natural Language Processing (NLP) shared task featured a new longitudinal corpus of 1304 records representing 296 diabetic patients. The corpus contains three cohorts: patients who have a diagnosis of coronary artery disease (CAD) in their first record, and continue to have it in subsequent records; patients who do not have a diagnosis of CAD in the first record, but develop it by the last record; patients who do not have a diagnosis of CAD in any record. This paper details the process used to select records for this corpus and provides an overview of novel research uses for this corpus. This corpus is the only annotated corpus of longitudinal clinical narratives currently available for research to the general research community.