GenAI4ELab/papercli-papers huggingface dataset:
Supporting AI Literacy Teaching Through the Development of Assessments for Classroom Use
Authors: John Masla; Christina Bosch; Prerna Ravi; Lydia Guterman; Sarah Wharton; Mary Cate Gustafson-Quiett; Samar Abu Hegly; Calvin Macatantan; Eric Klopfer; Cynthia Breazeal; Hal Abelson
Venue: AAAI 2025 (main)

Abstract: Initial discussion of AI literacy assessment has focused on competency frameworks and learning standards rather than materials for classroom use. Responsible AI for Computational Action (RAICA), a constructionist AI curriculum for middle and high school students, includes assessment materials to support teachers with the evaluation of student AI literacy competencies in their classrooms. These materials include exit tickets used as formative assessments at the end of each lesson and both teacher and student-facing rubrics. After beta-testing a module of the curriculum with nine teachers and 282 students, we reviewed teacher usage data and feedback as well as student responses. The review process surfaced a number of improvements to the materials to better align them with classroom teaching practice. These included clarifying language and adding visual scaffolds. We present the assessment materials and iterative design process used to bridge the gap between the theoretical AI literacy competencies and their practical implementation in classrooms.
Learning About Algorithm Auditing in Five Steps: Scaffolding How High School Youth Can Systematically and Critically Evaluate Machine Learning Applications
Authors: Luis Morales-Navarro; Yasmin B. Kafai; Lauren Vogelstein; Evelyn Yu; Danaë Metaxa
Venue: AAAI 2025 (main)

Abstract: While there is widespread interest in supporting young people to critically evaluate machine learning-powered systems, there is little research on how we can support them in inquiring about how these systems work and what their limitations and implications may be. Outside of K-12 education, an effective strategy in evaluating black-boxed systems is algorithm auditing—a method for understanding algorithmic systems’ opaque inner workings and external impacts from the outside in. In this paper, we review how expert researchers conduct algorithm audits and how end users engage in auditing practices to propose five steps that, when incorporated into learning activities, can support young people in auditing algorithms. We present a case study of a team of teenagers engaging with each step during an out-of-school workshop in which they audited peer-designed generative AI TikTok filters. We discuss the kind of scaffolds we provided to support youth in algorithm auditing and directions and challenges for integrating algorithm auditing into classroom activities. This paper contributes: (a) a conceptualization of five steps to scaffold algorithm auditing learning activities, and (b) examples of how youth engaged with each step during our pilot study.
What Can Youth Learn About Artificial Intelligence and Machine Learning in One Hour? Examining How Hour of Code Activities Address the Five Big Ideas of AI
Authors: Luis Morales-Navarro; Yasmin B. Kafai; Eric Yang; Asep Suryana
Venue: AAAI 2025 (main)

Abstract: The prominence of artificial intelligence and machine learning in everyday life has led to efforts to foster AI literacy for all K–12 students. In this paper, we review how Hour of Code activities engage with the five big ideas of AI, in particular with machine learning and societal impact. We found that a large majority of activities focus on perception and machine learning, with little attention paid to representation and other topics. A surprising finding was the increased attention paid to critical aspects of computing. However, we also observed a limited engagement with hands-on activities. In the discussion, we address how future introductory activities could be designed to offer a broader array of topics, including the development of tools to introduce novices to artificial intelligence and machine learning and the design of more unplugged and collaborative activities.
An XAI Social Media Platform for Teaching K-12 Students AI-Driven Profiling, Clustering, and Engagement-Based Recommending
Authors: Nicolas Pope; Juho Kahila; Henriikka Vartiainen; Mohammed Saqr; Sonsoles López-Pernas; Teemu Roos; Jari Laru; Matti Tedre
Venue: AAAI 2025 (main)

Abstract: This paper presents an explainable AI (XAI) education tool designed for K-12 classrooms, particularly for students aged 11-16.  The tool was designed for interventions on the fundamental processes behind social media platforms,  focusing on four AI- and data-driven core concepts: data collection, user profiling, engagement metrics, and recommendation algorithms.  An Instagram-like interface and a monitoring tool for explaining the data-driven processes make these complex ideas accessible and engaging for young learners.  The tool provides hands-on experiments and real-time visualizations, illustrating how user actions influence their personal experience on the platform as well as the experience of others.  This approach seeks to enhance learners' data agency, AI literacy, and sensitivity to AI ethics.  The paper includes a case example from 12 two-hour test sessions involving 209 children, using learning analytics to demonstrate how they navigated their social media feeds and the browsing patterns that emerged.
Learning to Think Like a Neuron in Middle School
Authors: David Touretzky; Christina Gardner-McCune; Will Hanna; Angela Chen; Neel Pawar
Venue: AAAI 2025 (main)

Abstract: Neuron Sandbox is a browser-based tool that helps middle school students grasp basic principles of neural computation. It simulates a linear threshold unit applied to binary decision problems, which students solve by adjusting the unit's threshold and/or weights. Although Neuron Sandbox provides extensive visualization aids, solving these problems is challenging for students who have not yet been exposed to algebra. We collected survey, video, and worksheet data from 21 seventh grade students in two sections of an AI elective, taught by the same teacher, that used Neuron Sandbox.  We present a scaffolding strategy that proved effective at guiding these students to achieve mastery of these problems. While the amount of scaffolding required was more than we originally anticipated, by the end of the exercise students understood the computation that linear threshold units perform and were able to generalize their understanding of the worksheet’s "solve for threshold" strategy to also solve for weights.
AI Chef Trainer: Introducing Students to the Importance of Data in Machine Learning
Authors: Saniya Vahedian Movahed; Fred Martin
Venue: AAAI 2025 (main)

Abstract: The AI Chef Trainer is an educational web app that introduces children to the role of data in machine learning (ML) through the engaging task of recipe recommendation. Initially, students tested the AI Chef's capabilities by selecting from a list of ingredients to see what the system recommended as possible recipes. After observing the recommendations, they contributed by adding their own recipes—each being a set of ingredients and a corresponding recipe-name—which were used to retrain the model and finally re-tested recipe suggestions. This cyclical process of testing, contributing, retraining, and post-training testing provided students with hands-on experience in how AI systems learn and adapt over time based on new data. We tested our software with middle school students.  The results indicated that students recognized the importance of both data quantity and specificity in the training process. 45 of 52 students entered recipes, and 26 of the 52 tested their own recipes using the specific ingredients they entered. Students were introduced to the concept of confidence percentages via the AI recipe suggestions. Even as the primary focus was the role of data in  machine learning, the AI Chef Trainer software also served as a window into students' cultural expression and personal preferences.
Word2Vec4Kids: Interactive Challenges to Introduce Middle School Students to Word Embeddings
Authors: Nathan Wiatrek; Yash Verma; Fred Martin
Venue: AAAI 2025 (main)

Abstract: As Artificial Intelligence (AI) continues to integrate into more aspects of society, equipping younger generations with foundational AI knowledge becomes increasingly critical. This paper presents Word2Vec4Kids (W2V4K), an interactive application designed to familiarize middle school students with word embeddings, a key aspect of Natural Language Processing (NLP). W2V4K leverages the Word2Vec model, allowing students to explore word associations, similarity, and vector arithmetic through engaging game modes. The application was tested with 38 middle school students aged 11-14 at a Science Technology Engineering Math (STEM)-focused charter school. Data were collected on students' interactions with the application, including screen recordings, audio, and survey responses. Results demonstrated that W2V4K effectively introduces NLP concepts to students. Qualitative observations revealed high levels of engagement with students expressing excitement and curiosity about word relationships. As they progressed through the game modes, students showed increasing confidence in predicting word associations, brainstorming relevant words, and connecting the concepts to real-world applications. Quantitative data from post-interaction surveys indicated positive learning outcomes with 44.5% of students achieving perfect scores on concept-related items. Additionally, students demonstrated an ability to critically think about language representation. This study suggests that W2V4K provides an effective and engaging method for introducing NLP concepts to middle school students, contributing to the broader goal of enhancing AI literacy among younger generations.
Model AI Assignments 2025
Authors: Todd W. Neller; Rasika Bhalerao; Eun Kyung Ko; Vishodana Thamotharan; Lisa Zhang; Sonya Allin; Mahdi Haghifam; Michael Pawliuk; Rutwa Engineer; Florian Shkurti; Cunyan Ma; Daniella DiPaola; Cynthia Breazeal; Loreto Alonzi; Brian Wright; Ali Rivera; Kristin Fasiang; Duri Long; Shruthi Chockkalingam; Giulia Toti; Evan Shieh; Princewill Okoroafor; Thema Monroe-White; Mustafa Haiderbhai; Carolyn Quinlan; Ashwin R. Bharadwaj; Anio Zhang; Rajagopal Venkatesaramani; Sarah Wharton; John Masla; Lydia Guterman; Mary Cate Gustafson-Quiett; Christina Bosch; Samar Abu Hegley; Calvin Macatantan; Eric Klopfer; Hal Abelson; Shira Wein; Mercy Wairimu Gachoka; Li-Hsin Chang; Maryam Mirzaei; Mohammad Mahdi Ajallooeian
Venue: AAAI 2025 (main)

Abstract: The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning experience, we here present abstracts of thirteen AI assignments from the 2025 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment specifications and supporting resources may be found at http://modelai.gettysburg.edu
Scalable Solutions for Decision-Making Systems Using Explainable Policy Representations
Authors: Muqsit Azeem
Venue: AAAI 2025 (main)

Abstract: Despite significant advancements in solving Markov Decision Processes (MDPs) and Simple Stochastic Games (SGs), scalability remains a challenge due to the exponential growth of their state spaces. This thesis aims to push the boundaries of state-of-the-art methods by tackling this issue using 1) explainability and 2) exploiting the model structure. First, we introduce the *1-2-3-Go* approach, which learns explainable policies from small MDP models and generalizes them to larger instances, improving scalability in MDPs. We then extend *Optimistic Value Iteration (OVI)* and *Sound Value Iteration (SVI)*—originally designed for MDPs—to SGs, improving efficiency in adversarial settings. Finally, we aim to exploit the *explainable policy representations* and the *model structure* to enhance both scalability and interpretability in SGs. This thesis contributes to both theoretical advancements and practical solutions for decision-making systems under uncertainty.
Explanations for Multi-Agent Reinforcement Learning
Authors: Kayla Boggess
Venue: AAAI 2025 (main)

Abstract: Explainable reinforcement learning (xRL) provides explanations for ``black-box" decision making systems. However, most work in xRL is based on single-agent settings instead of the more complex multi-agent reinforcement learning (MARL). Several different types of post-hoc explanations must be provided to increase understanding of both centralized and decentralized MARL systems. For centralized MARL, this research develops methods to generate global policy summaries, query-based explanations, and temporal explanations. For decentralized MARL, this research develops global policy summaries and query-based explanations.
De Novo Molecular and Crystal Design with Latent Space Bayesian Optimization
Authors: Onur Boyar
Venue: AAAI 2025 (main)

Abstract: This thesis explores Latent Space Bayesian Optimization (LSBO) for the generation and optimization of de novo molecules and crystal materials. Our goal is to develop practical, sample-efficient de novo discovery algorithms with a focus on real-world applicability, and our results so far demonstrate significant progress toward practical implementation.
Towards Better Robot Learners: Leveraging Implicit and Explicit Human Feedback Together in Human Robot Interactions
Authors: Kate Candon
Venue: AAAI 2025 (main)

Abstract: My work aims to enable robots to better learn from human feedback in human-robot interactions. The way in which people want to collaborate with a robot can vary person-to-person, interaction-to-interaction, or even within an interaction with a given person. Thus, robots need to be able to adapt their behavior during interactions. Robots typically learn from humans via explicit feedback, such as evaluative feedback, preferences, or demonstrations. We know that humans also provide additional information implicitly through non-verbal behavior that gives clues about their internal states during interactions. My work investigates how we can incorporate both kinds of feedback into robot learning paradigms.
Stress-Testing of Multimodal Models in Medical Image-Based Report Generation
Authors: Flávia Carvalhido; Henrique Lopes Cardoso; Vítor Cerqueira
Venue: AAAI 2025 (main)

Abstract: Multimodal models, namely vision-language models, present unique possibilities through the seamless integration of different information mediums for data generation. These models mostly act as a black-box, making them lack transparency and explicability. Reliable results require accountable and trustworthy Artificial Intelligence (AI), namely when in use for critical tasks, such as the automatic generation of medical imaging reports for healthcare diagnosis. By exploring stress-testing techniques, multimodal generative models can become more transparent by disclosing their shortcomings, further supporting their responsible usage in the medical field.
Towards Addressing Frontiers in Graph Generation
Authors: Alex O. Davies
Venue: AAAI 2025 (main)

Abstract: This doctoral dissertation establishes and addresses frontiers in graph generation. I first apply a Graph Neural Network (GNN) model on social network data, a new domain, to establish what frontiers exist for graph generators. I establish that GNN models are currently limited in the diversity of feature sets that they can produce, the variety of graph structure types they can generate, and highly limited in the size of generated graphs. Further, I find that the quality metrics available for graph generation are aggregate-based and un-expressive. To address the issue of scale I propose Hierarchical Generation of Graphs (HiGGs), a framework for producing graphs orders of magnitude larger than is possible with a single model. As a step towards more expressive metrics I develop Topology only Pre-training (ToP), a pre-training framework for graph models that is capable of representing multiple domains of graphs simultaneously, without relying on tertiary models in downstream applications. The next stage of research will adapt ToP as a model based metric for graph generators.
Privacy, Utility and Fairness: Navigating Trade-offs in Differentially Private Machine Learning
Authors: Lea Demelius
Venue: AAAI 2025 (main)

Abstract: Developing trustworthy AI requires advancing methods that meet key requirements such as privacy or fairness while maintaining strong utility, as well as understanding the intricate interdependencies between these dimensions, which often manifest as trade-offs. My PhD research focuses on differential privacy, which is widely regarded as the state-of-the-art for protecting privacy in data analysis and machine learning. I investigate the relationships between differential privacy, utility and fairness, with the goal of advancing the adoption of differentially private machine learning in real-world settings.
Scalable Hierarchical Deep Neural Network for Time Series Analysis in Wearable Sensor-based Human Activity Recognition
Authors: Zahra Ghorrati
Venue: AAAI 2025 (main)

Abstract: Artificial Intelligence (AI) continues to evolve rapidly, impacting numerous fields, including time series (TS) classification and human activity recognition (HAR). Despite the advancements in deep learning models, such as Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs), these models face several challenges, including the need for extensive labeled datasets, significant computational resources, and lack of interpretability. 
This research aims to address these limitations by developing an adaptive hierarchical deep neural network framework that integrates fuzzy logic principles and adaptive learning techniques for robust, computationally efficient, and interpretable real-time TS analysis. The reduction in the number of parameters and the efficient learning of hierarchical features mean that less training data is needed to achieve robust performance. The model's ability to generalize from hierarchical representations allows it to make effective use of smaller datasets, which is particularly advantageous in scenarios where data is limited or expensive to obtain.The proposed framework specifically targets HAR applications using data from wearable sensors.
A Hybrid Approach for Visual Recognition of Object States
Authors: Filippos Gouidis
Venue: AAAI 2025 (main)

Abstract: The basic objective of my research work is to address the challenging problem of recognizing object states in a visual context by integrating data-driven and symbolic approaches. In particular, I focus on the Zero-shot variation of this task. The contributions made so far include the development of novel methods that exhibit state-of-the-art (SOTA) performance, the creation of a new object states dataset, the formulation of novel problems, the successful integration of low-level and high-level approaches, and comprehensive analyses that highlight the specific challenges posed by the problem.
Explainable Robot Navigation
Authors: Amar Halilovic
Venue: AAAI 2025 (main)

Abstract: As the use of autonomous mobile robots expands into dynamic and complex environments, the need for them to provide understandable explanations for their actions becomes crucial. This thesis addresses the challenge of developing explainability for robot navigation by leveraging a hybrid model that combines machine learning techniques with symbolic reasoning methods. Furthermore, the thesis explores the modeling of human explanation preferences and the impact of different explanation attributes on explanation recipients' understanding, satisfaction, and trust. The goal is to integrate different explanation aspects and approaches into a unified framework to support explainable navigation in robotics.
Tracking and Identifying International Propaganda and Influence Networks Online
Authors: Hans W. A. Hanley
Venue: AAAI 2025 (main)

Abstract: Misinformation and propaganda undermine trust in institutions, spread falsehoods, and sometimes incite violence. However, recent advancements in transformer-based AI models can help combat the proliferation of disinformation globally and in real time. In this work, I propose and develop a system using these models to scalably identify, track, and analyze the spread of narratives from over 40,000 international news websites. First, by employing novel multilingual Matryoshka embeddings and hierarchical level-wise clustering, my proposed system identifies news stories, topics, and themes across these thousands of news websites. Second, by utilizing multilingual stance detection, my system assesses the biases and factual inconsistencies in news articles, enabling the identification of websites that spread propaganda or misinformation. Finally, through network inference methods, my system uncovers connections among websites disseminating slanted or false content. My approach illustrates how AI can be utilized to mitigate the global spread of harmful misinformation and propaganda.
From Gambits to Assurances: Game-Theoretic Integration of Safety and Learning for Interactive Robotics
Authors: Haimin Hu
Venue: AAAI 2025 (main)

Abstract: Autonomous robots are becoming more versatile and widespread in our daily lives. From autonomous vehicles to companion robots for senior care, these human-centric systems must demonstrate a high degree of reliability in order to build trust and, ultimately, deliver social value. How safe is safe enough for robots to be wholeheartedly trusted by society? Is it sufficient if an autonomous vehicle can avoid hitting a fallen cyclist 99.9% of the time? What if this rate can only be achieved by the vehicle always stopping and waiting for the human to move out of the way? I argue that, for trustworthy deployment of robots in human-populated space, we need to complement standard statistical methods with clear-cut robust safety assurances under a vetted set of operation conditions. We need runtime learning to minimize the robot’s performance loss during safety-enforcing maneuvers by reducing its inherent uncertainty induced by its human peers, for example, their intent (does a human driver want to merge, cut behind, or stay in the lane?) or response (if the robot comes closer, how will the human react?). We need to close the loop between the robot’s learning and decision-making so that it can optimize efficiency by anticipating how its ongoing interaction with the human may affect the evolving uncertainty, and ultimately, its long-term performance.
Natural Language Generation with Expert Standards
Authors: Joseph Marvin Imperial
Venue: AAAI 2025 (main)

Abstract: Standards, or expert-defined preferences, are documented guidelines describing strict specifications for text-based content such as books, manuals, and reports. These guidelines are curated, defined, and continuously improved by domain experts in various fields, such as education, policy, and healthcare, and are used for maintaining quality. In my dissertation, I focus on evaluating and teaching large language models (LLMs) to capture standards to improve generation quality across diverse language generation tasks. I draw motivation from my preliminary published works, where I explored how open and commercial LLMs can learn complex constraints from standards in education and language assessment to produce classroom-ready narrative content. In this proposal, I also discuss the technical novelty, impact, and target contributions and highlight how this line of work can be scaled and generalized for other domains where standards are also used as a reference of quality.