The demand for cybersecurity professionals with advanced static analysis expertise has grown exponentially, driven by the increasing sophistication of malware, advanced persistent threats, and nation-state cyber attacks. Our project provides a design and implementation of an innovative static analysis course that integrates large language models (LLMs) to enhance student learning and engagement. Our approach leverages LLMs as intelligent assistants within a Capture The Flag (CTF) framework, enabling students to collaborate with LLMs to solve complex binary analysis tasks. We present our course structure, AI facilitation process, and evaluation results, highlighting how LLM integration impacts students' understanding of reverse engineering and symbolic execution concepts, how students adapt their learning strategies when working with LLMs, and cons and pros of LLMs in reverse engineering. This report provides valuable insights for educators seeking to incorporate LLMs technologies into cybersecurity curricula, addressing both technical and motivational challenges in static analysis education.

Computer Science (CS) professional development has increased opportunities to broaden K12 teachers' and students' exposure to CS learning. However, many Indigenous-serving teacher professional development (PD) participants could not facilitate in-classroom implementation without significant follow-up and support. This study aims to understand how the factors emerging from our CS PD affected teachers' PD transition to in-classroom implementation. We analyzed multiple data sources collected over three years of our project. We use logistic regression to explore the data from the PD course. Through analysis, our study indicates that providing teachers with targeted mentorship and ensuring the completion of detailed lesson plans are two factors in the transition of teachers' learning from PD to teachers' implementations in the classroom.

Native Americans (NA) have historically been the most underrep-resented population when it comes to participating in STEM and computing careers. The Navajo are one of the country's largest NA groups, and understanding the barriers and developing solutions to increase their participation will have far-reaching consequences on informing the research and practice on how computing can be taught at NA-serving high schools. The paper describes the experience gained over three years of working in this region via project Let's Talk Code, which aims to help math, science, and art teachers from Navajo high schools develop CS-based projects in their existing courses and provide mentorship and guidance. Let's Talk Code is constructed as a research-practice partnership (RPP) where the teachers (practitioners) work with a multi-institutional team of researchers and CS educators to improve the capacity-building needs of its partners (high schools). The paper details the evolution of the project over the years and highlights challenges, barriers, and strategies that were used to impact a significant number of teachers throughout the project.

Human-Autonomy Teaming (HAT) is a multi-disciplinary domain with a diverse set of research needs and goals stemming from fields such as computer science, robotics, and human factors. This melting pot of fields generates a unique challenge in that there exist many disjoint research methods (measures and tasks) that cause issues with knowledge transfer and comparison between researchers. One way to address this issue is by providing researchers with a testbed containing a standardized suite of analysis tools and tasks that allow direct comparison between different approaches. Therefore, this study attempts to bring the HAT community together in a collaborative discussion to collect and organize their research needs for the future development of these testbeds. Specifically, through thematic analysis, our work reveals three emergent prongs that underpin testbed needs of HAT experts: task, AI, and technical requirements. Also, we organize our thematic analysis by priority to suggest possible paths for HAT testbed development to maximize its immediate and continued utility. Our research indicates that the HAT community places significant importance on both the pre-established, standardized functions available within the testbed and the freedom to tailor and develop their unique tasks or AI solutions.

In this extended abstract we present the design, development, and evaluation of a Minecraft-based simulated task environment to conduct human and AI teaming research. With the deluge of AI-driven applications and their infiltration into many activities of daily living, it is becoming necessary to look at ways that humans and AI can work together. There is a tremendous research burden associated with accurately evaluating the best practices and trade-offs when humans and AI have to collaborate together in completing critical tasks. Minecraft offers a low-cost alternative as an early investigating tool for researchers to build answers to emerging research questions before significantly investing in human-AI teaming activities in the real world. We demonstrate successfully via a simple rule-based AI, insights that could highly influence human-AI teaming activities can be derived to improve practical and viable development of protocols and procedures. Our findings indicate that simulated task environments play a critical role in furthering human AI teaming activities.

Augmented reality (AR) provides a significant opportunity to improve collaboration between co-located team members jointly analyzing data visualizations, but existing rigorous studies are lacking. We present a novel method for qualitatively encoding the positions of co-located users collaborating with head-mounted displays (HMDs) to assist in reliably analyzing collaboration styles and behaviors. We then perform a user study on the collaborative behaviors of multiple, co-located synchronously collaborating users in AR to demonstrate this method in practice and contribute to the shortfall of such studies in the existing literature. Pairs of users performed analysis tasks on several data visualizations using both AR and traditional desktop displays. To provide a robust evaluation, we collected several types of data, including software logging of participant positioning, qualitative analysis of video recordings of participant sessions, and pre- and post-study questionnaires including the NASA TLX survey. Our results suggest that the independent viewports of AR headsets reduce the need to verbally communicate about navigating around the visualization and encourage face-to-face and non-verbal communication. Our novel positional encoding method also revealed the overlap of task and communication spaces vary based on the needs of the collaborators.

An alternative to the traditional classroom structure that has seen increased use in higher education is the flipped classroom. Flipping the classroom switches when assignments (e.g. homework) and knowledge transfer (e.g. lecture) occur. Flipped classrooms are getting popular in secondary and post-secondary teaching institutions as evidenced by the marked increase in the study, use, and application of the flipped pedagogy as it applies to learning and retention. The majority of the courses that have undergone this change use applied learning strategies and include a significant “learning-by-doing” component. The research in this area is skewed towards such courses and in general there are many considerations that educators ought to account for if they were to move to this form of teaching. Introductory courses in computer programming can appear to have all the elements needed to move to a flipped environment; however, initial observations from our research identify possible pitfalls with the assumption. In this work in progress the authors discuss early results and observations of implementing a flipped classroom to teach an introductory programming course (CS1) to engineering, engineering technology, and software engineering undergraduates.

Heat map based data visualization and mining is an emerging area in game engine design and architecture. Employed by many state of the art game engines and popular commercial games, this technology helps populate and collate player activity and behavior to better inform the system for further action. Simulation and serious games can tremendously benefit by applying heat map based visualization for the purposes of analyzing and tracking player behavior. Heat maps are time varying texture maps that represent a chosen activity over a certain grid at any particular interval of elapsed time. In this paper results of applying a real-time heat map data capture and generation tool on two military simulations: 1) Ground-based combat scenario and 2) Unmanned Aerial Vehicle sensor operator scenario is presented. The research showcases several real-time visualization techniques developed into the simulation with the main goal of understanding participant behavior. Novice and expert data is populated as part of the experiment to validate the effectiveness of our methods.

The increased dependence on computer programming in engineering has made it essential for engineering students to learn about programming throughout their undergraduate education. In the same vein, computing students benefit when given an opportunity to learn more about engineering design and systematic thinking. This paper discusses how one college embedded computing and engineering into a combined first-year introductory course. The course fuses computing and engineering using game design and robotics as an offering for both cohorts of students to work together in a multidisciplinary environment. Over the course of the semester, students learn introductory computing and engineering design concepts by designing games and robots using informatics tools to solve design challenges. Interdisciplinary teams consisting of computing and engineering students work together to prototype a game design idea and then bring that idea to life using robots as part of their final project.

Socially Relevant Simulation Games (SRSG), a new medium for social interaction, based on real-world skills and skill development, creates a single gaming framework that connects both serious and casual players. Through a detailed case study this paper presents a design process and framework for SRSG, in the context of mixed-reality golf swing simulations. The SRSG, entitled "World of Golf", utilizes a real-time expert system to capture, analyze, and evaluate golf swing metrics. The game combines swing data with players' backgrounds, e.g., handicaps, to form individual profiles. These profiles are then used to implement a golf simulation game using artificially controlled agents who inherit the skill levels of their corresponding human users. The simulation and assessment modules provide the serious player with tools to build golf skills while allowing casual players to engage within a simulated social world. A framework that incorporates simulated golf competitions among these social agents is presented and validated by comparing the usage statistics of 10 PGA Golf Management (PGM) students with 10 non-professional students.