Generative artificial intelligence (GenAI) tools such as ChatGPT, Gemini, and Claude are transforming instructional design, yet most PK–12 educators lack effective strategies for engaging with them. To address this gap, we developed the CRAFT framework—a teacher-centered model for GenAI prompt design that emphasizes Context, Role, Audience, Format, and Tone. CRAFT translates principles from prompt engineering, instructional design, and Universal Design for Learning into a practical structure aligned with teachers' professional planning language. We implemented the framework in a professional development program with 94 PK–6 teachers who used GenAI to create lessons integrating computational thinking. Their 688 prompts and AI-generated responses were coded across 30 analytical features representing critique, launch behaviors, refinements, and supplemental material generation. Findings suggest that CRAFT helped teachers produce standards-aligned, differentiated, and instructionally coherent lessons while increasing confidence, creativity, and reflective engagement with GenAI tools.

During the UnboxingCT project summer professional development, the Integrated CT Assessment was piloted with 72 elementary teachers. The assessment is based on computational thinking integration literature and asks teachers to identify different computational thinking concepts in content area scenarios. The assessment allowed us to identify which computational thinking concepts teachers were most familiar with prior to the professional development and assess changes in their understanding following the professional development. Our next step will be validation of the assessment with a larger group of teachers.

Social unrest, a multifaceted phenomenon that is influenced by a variety of interconnected factors, presents substantial obstacles to societal stability and governance. The comprehension of local nuances is frequently restricted by the analysis of drivers of unrest at broad geographic scales or the isolation of specific causes in traditional studies. This paper introduces the SCEIGE framework, which classifies unrest drivers into six essential categories: Socio-demographic, Cultural, Environmental, Infrastructural, Geographic, and Economic. This framework is designed to address these challenges. SCEIGE offers a comprehensive perspective on the fundamental causes of social unrest by modeling geographic spaces at fine resolutions and incorporating a wide range of variables. We further enhance this framework by introducing a novel clustering and machine learning methodology, SC-XG (SCEIGE Clustering with XGBoost), which organizes geographic regions according to SCEIGE patterns. SC-XG not only reveals the local drivers of unrest but also facilitates the predictive analysis of social unrest events. This paper also illustrates the effectiveness of high-resolution SCEIGE geo-rasters in analyzing social unrest and confirms that the drivers of unrest differ across regions, underscoring the necessity of a local-level understanding. We identify critical, region-specific unrest drivers and address the broader implications for predicting and mitigating social unrest globally by applying SC-XG to unrest patterns in India.

This paper proposes Conflict-RAG, a method of working around the limitations of large language models (LLMs) to improve their efficacy in understanding current events and global conflicts. This method includes several steps. First, we create a database of Arabic news sources through web scraping. Then, we use weak supervision to create labels for the data to ensure they are relevant. Next, we use retrieval augmented generation (RAG) to inform the LLM about regional perspectives and current events that it would not otherwise know. Finally, we use an LLM to generate a response to a user’s query in order to answer their question. Our method provides an interface that allows non-Arabic-speaking users to gain an understanding of Arabic news sources. We demonstrate how our method improves response generation from an LLM by investigating the Israel-Hamas conflict.

In this poster, we present our efforts to engage elementary teachers with learning trajectories as a tool for developing both their own and their students' comprehension of computational thinking (CT) and strategies for integrating CT learning in their classroom. Eleven teachers, who voluntarily joined a teacher professional development (PD) program to develop teacher leaders for CT integration in the elementary context, attended a one-day PD session aimed at reviewing their knowledge of CT, participating in CT-infused lessons, and engaging with CT learning trajectories. Over the next year, teachers will participate in monthly virtual PD to continue to grow both their CT content knowledge and pedagogical knowledge. Our goal is to develop these teachers as teacher leaders who will support others as they integrate CT. This poster will show our current progress on CT learning trajectories and teacher leaders' responses to the tool.

There is growing attention for developing professional learning experiences for content area teachers to infuse computational thinking (CT). However, there is little reporting on how teachers continue to implement the CT lessons once professional development (PD) is over. This study provides initial results on our efforts of building project sustainability through teacher autonomy in designing their own CT infusion projects or PDs for their schools. Our initial analysis indicates the need to continue to build teacher autonomy within the professional learning experiences for developing teacher confidence and sustainability of the project.

In 2018 and 2019, Infusing Computing offered face-to-face summer PD workshops to support middle and high school teachers in integrating computational thinking into their classrooms through week-long summer PD workshops and academic-year support. Due to COVID-19, 151 teachers attended the Summer 2020 PD workshops in a week-long virtual conference format. In this paper, we describe Virtual Pivot: Infusing Computing, which employed emerging technology tools, pre-PD training, synchronous and asynchronous sessions, Snap! pair programming, live support, and live networking. Drawing on findings from participant interviews and post-PD surveys, we argue that three categories of changes (digital tools, formats, and supports for teacher engagement and collaboration) were effective in increasing participants' self-efficacy in teaching CT, supporting collaboration, and enabling participants to design CT-infused content-area lessons. We conclude by discussing how elements of this virtual PD can be replicated to increase teacher and student access to CT practices in middle and high school classrooms

Despite the increasing attention to infusing CT into middle and high school content area classrooms, there is a lack of information about the most effective practices and models to support teachers in their efforts to integrate disciplinary content and CT principles. To address this need, this paper proposes the Code, Connect and Create (3C) professional development (PD) model, which was designed to support middle and high school content area teachers in infusing computational thinking into their classrooms. To evaluate the model, we analyzed quantitative and qualitative data collected from Infusing Computing PD workshops designed for in-service science, math, English language arts, and social studies teachers located in two Southeastern states. Drawing on findings from our analysis of teacher-created learning segments, surveys, and interviews, we argue that the 3C professional development model supported shifts in teacher understandings of the role of computational thinking in content area classrooms, as well as their self-efficacy and beliefs regarding CT integration into disciplinary content. We conclude by offering implications for the use of this model to increase teacher and student access to computational thinking practices in middle and high school classrooms.

In summer 2018, we conducted two week-long professional development workshops for 116 middle and high school teachers interested in infusing computational thinking (CT) into their classrooms. Teachers learned to program in Snap!, connect CT to their disciplines, and create infused CT learning segments for their classes. This paper investigates the extent to which teachers were able to successfully infuse CT skills of pattern recognition, abstraction, decomposition, and algorithms into their learning products.

One way to increase access to education on computing is to integrate computational thinking (CT) into K12 disciplinary courses. However, this challenges teachers to both learn CT and decide how to best integrate CT into their classes. In this position paper, we present PRADA, an acronym for Pattern Recognition, Abstraction, Decomposition, and Algorithms, as a practical and understandable way of introducing the core ideas of CT to non-computing teachers. We piloted the PRADA model in two, separate, week-long professional development workshops designed for in-service middle and high school teachers and found that the PRADA model supported teachers in making connections between CT and their current course material. Initial findings, which emerged from the analysis of teacher-created learning materials, survey responses, and focus group interviews, indicate that the PRADA model supported core content teachers in successfully infusing CT into their existing curricula and increased their self-efficacy in CT integration.

Social Unrest Reconnaissance Gazetteer (or SURGE) is a Web-based application that provides an open system to visualize and integrate spatio-temporal data about social unrest events with related data layers in South Asia to facilitate data-driven as well as model-based investigations and analyses. Currently, the system displays eight categories of unrest, based primarily on the Global Database of Events, Language and Tone (GDELT) and the Global Terrorism Database (GTD). Users have the ability to select a single day or a range of dates along with the category of unrest they are interested to investigate. The users also have the option to normalize the raw event counts by population density. Additionally, the users can view infrastructure layers that facilitate or hinder the diffusion of unrest events (e.g., collated from an open GIS data-source: OpenStreetMap (www.openstreetmap.org)) and choropleth layers to display various socio-economic indicators (e.g., derived from global surveys and government census data such as the 2011 India census data (cenusindia.gov.in) and IPUMS Terra (data.terrapop.org)). Currently, SURGE displays unrest events for India, Pakistan and Bangladesh as heat map layers in multiple spatial resolutions. Challenges have involved geo-synchronization, data conversions, and displaying multiple layers of dense geospatial datasets. Future capabilities include automatic ingestion of raw data and standardizing levels of unrest using significant predictors.

Redistricting is the process of dividing a geographic area consisting of spatial units-often represented as spatial polygons-into smaller districts that satisfy some properties. It can therefore be formulated as a set partitioning problem where the objective is to cluster the set of spatial polygons into groups such that a value function is maximized [1]. Widely used algorithms developed for point-based data sets are not readily applicable because polygons introduce the concepts of spatial contiguity and other topological properties that cannot be captured by representing polygons as points. Furthermore, when clustering polygons, constraints such as spatial contiguity and unit distributedness should be strategically addressed. Toward this, we have developed the Constrained Polygonal Spatial Clustering (CPSC) algorithm based on the A* search algorithm that integrates cluster-level and instance-level constraints as heuristic functions. Using these heuristics, CPSC identifies the initial seeds, determines the best cluster to grow, and selects the best polygon to be added to the best cluster. We have devised two extensions of CPSC-CPSC* and CPSC*-PS-for problems where constraints can be soft or relaxed. Finally, we compare our algorithm with graph partitioning, simulated annealing, and genetic algorithm-based approaches in two applications-congressional redistricting and school districting.

Clustering is an important task in spatial data mining and spatial analysis. We propose a clustering algorithm P-DBSCAN to cluster polygons in space. P-DBSCAN is based on the well established density-based clustering algorithm DBSCAN. In order to cluster polygons, we incorporate their topological and spatial properties in the process of clustering by using a distance function customized for the polygon space. The objective of our clustering algorithm is to produce spatially compact clusters. We measure the compactness of the clusters produced using P-DBSCAN and compare it with the clusters formed using DBSCAN, using the Schwartzberg index. We measure the effectiveness and robustness of our algorithm using a synthetic dataset and two real datasets. Results show that the clusters produced using P-DBSCAN have a lower compactness index (hence more compact) than DBSCAN.

The traditional point-based clustering algorithms when applied to geospatial polygons may produce clusters that are spatially disjoint due to their inability to consider various types of spatial relationships between polygons. In this paper, we propose to represent geospatial polygons as sets of spatial and non-spatial attributes. By representing a polygon as a set of spatial and non-spatial attributes we are able to take into account all the properties of a polygon (such as structural, topological and directional) that were ignored while using point-based representation of polygons, and that aid in the formation of high quality clusters. Based on this framework we propose a dissimilarity function that can be plugged into common state-of-the-art spatial clustering algorithms. The result is clusters of polygons that are more compact in terms of cluster validity and spatial contiguity. We show the effectiveness and robustness of our approach by applying our dissimilarity function on the traditional k-means clustering algorithm and testing it on a watershed dataset.

Redistricting is the process of dividing a geographic area into districts or zones. This process has been considered in the past as a problem that is computationally too complex for an automated system to be developed that can produce unbiased plans. In this paper we present a novel method for redistricting a geographic area using a heuristic-based approach for polygonal spatial clustering. While clustering geospatial polygons several complex issues need to be addressed - such as: removing order dependency, clustering all polygons assuming no outliers, and strategically utilizing domain knowledge to guide the clustering process. In order to address these special needs, we have developed the constrained polygonal spatial clustering (CPSC) algorithm that holistically integrates do-main knowledge in the form of cluster-level and instance-level constraints and uses heuristic functions to grow clusters. In order to illustrate the usefulness of our algorithm we have applied it to the problem of formation of unbiased congressional districts. Furthermore, we compare and contrast our algorithm with two other approaches proposed in the literature for redistricting, namely-graph partitioning and simulated annealing.