In this student paper, we report on our project to enhance road safety in South Carolina (SC) by analyzing traffic data provided by the Department of Transportation and evaluating the impact of a school-level student driver education program called Alive@25. We improve the understanding of road safety using these traffic and training data to understand collision patterns and areas for improvement and assess training coverage gaps. Our approach combines geospatial analysis, economic impact assessment, temporal trend analysis, and interactive visualizations while leveraging AI techniques to clean and analyze extensive datasets. Key findings revealed higher collision rates in urban counties and rising collision rates in mostly rural areas, where Alive@25 participation is declining. These insights led to recommendations for improving road infrastructure and expanding safety training programs. This research demonstrates the potential of AI-driven insights to inform timely, cost-effective interventions and promote multi-stakeholder engagement in addressing public safety challenges while teaching students data science and AI skills and civic engagement.

With the 2024 elections impacting nearly half the world’s population, the need for accurate election information has never been more urgent. However, stakeholders continue to face difficulties in accessing reliable data, especially with rising concerns about generative AI, misinformation, and bots. We introduce ElectionBot-SC, a chatbot tool designed to provide personalized and reliable election-related information from a primary source (e.g., official election commission) and, if necessary, from a secondary source (e.g., nonprofit) through a user-friendly interface. This demo highlights its multi-engine functionality, allowing users to choose between SafeChat (rule-based and powered by Rasa), Google Search, and an LLM (Mixtral 8x7b) to receive responses. ElectionBot-SC ensures transparency by clearly indicating the provenance of the information and encouraging users to evaluate responses from various AI engines critically. The tool was used for the 2024 elections in South Carolina to understand the effectiveness of chatbots in assisting users at a University comprising of students, including first-time voters, staff, and faculty, with election queries. Although we focus on elections and verifiable information dissemination using chatbots, our proposed approach is widely applicable like in health, traffic, education, and water. Demo Video link - https://shorturl.at/1A7cc

Ontologies are known for their ability to organize rich metadata, support the identification of novel insights via semantic queries, and promote reuse. In this paper, we consider the problem of automated planning, where the objective is to find a sequence of actions that will move an agent from an initial state of the world to a desired goal state. We hypothesize that given a large number of available planners and diverse planning domains, they carry essential information that can be leveraged to improve many ontology applications. We use open data on planning domains and planners to construct the most comprehensive planning ontology to date, based on supported competency questions, and demonstrate its applications in two practical use cases - planner selection and plan explanation. We have also made the ontology and associated resources available to the AI and data communities to promote further research.