AFIT Generative AI Teaching Guidebook

AFIT is proud to highlight the Generative AI Teaching Guidebook, a resource designed to provide military educators with practical insights, strategies, and use cases for integrating Generative AI (Gen AI) into their teaching practices. Developed through a collaborative effort involving AFIT faculty across various departments within the Graduate School of Engineering and Management and the School of Systems and Logistics, this digital resource serves as a starting point for educators exploring how to leverage Gen AI in their classrooms. It offers accessible examples and best practices, ensuring utility for instructors of all technical backgrounds. The guidebook provides a comprehensive overview of how Gen AI can enhance education, offering actionable use cases, illustrative examples, and best practices tailored to diverse teaching environments. By addressing both opportunities and challenges—such as ethical considerations and data privacy—it empowers educators to design meaningful learning experiences while fostering discussions about AI\u27s potential and limitations. The release of this guidebook comes at a key moment as the adoption of Gen AI in education accelerates. It highlights how these tools can be integrated into both traditional academic settings and professional continuing education, such as utilizing Gen AI for educational simulations or modeling in systems engineering. By fostering critical thinking, innovation, and ethical awareness, the Generative AI Teaching Guidebook empowers educators to prepare students for an AI-driven world while advancing AFIT’s defense-focused educational mission

Global Sporadic-E Prediction and Climatology Using Deep Learning

Sporadic-E (Es) is an ionospheric phenomenon defined by strong layers of plasma which may interfere with radio wave propagation. In this work, we develop deep learning models to improve the understanding of Es, including the presence, intensity and height of the layers. We developed three separate models. The first, building off earlier work in (J. A. Ellis et al., 2024, link in AFIT Scholar, 10.1029/2023sw003669), includes only the main features from radio occultation (RO) measurements. The second adds to that time, date, location, geomagnetic and solar indices, solar winds, x-ray flux, weather and lightning. A third model excludes RO measurements but includes the rest. In training the first two models, the Es ordinary mode critical frequency (foEs), a measure of intensity, and height (hEs) parameters extracted from ionosondes were used as the “ground truth” target variables. In training the third model, estimated foEs and hEs values from the RO model were added as target variables to augment the data set and produce physically reasonable model predictions globally. We find that the second model performs well with intensity prediction tasks, but struggles with height estimations, which is likely due to the tangent point assumption made during RO signal processing and errors inherent in the ionosonde extracted virtual heights. The third model performed reasonably well considering the lack of in situ RO measurement. The third model performs the best on height predictions, which points to the height being very climatologically driven, whereas the intensity is a more complex interaction of several variables

Book Talk on Brief: \u3ci\u3eMake a Bigger Impact by Saying Less\u3c/i\u3e by Jonathan McCormack: Ten Key Concepts and Six Lessons for Improving Communication in a World Full of Distractions

This Lunch and Learn presentation, delivered by Wheeler Hall from the D’Azzo Research Library (DRL) and hosted by the Center for Innovation in Education (CIE), focuses on the principles outlined in Joseph McCormack’s Brief: Make a Bigger Impact by Saying Less (2014). The session serves as a summary for the book and explores strategies for effective communication in a distraction-filled world, emphasizing brevity, clarity, and audience engagement. After an introduction from Jonathan Zemmer (CIE), Mr. Hall relays McCormack’s background, the practical workplace applications in Brief, and key concepts from the book, including the BRIEF mnemonic (Background, Reason, Information, Ending, Follow-up), the seven capital sins of communication, and the importance of storytelling. The presentation concludes with six lessons for refining communication, including thorough research, concise delivery, and respect for others’ time. Viewers are encouraged to borrow copies of the book from the library to further enhance their communication skills, applicable in both professional and personal contexts

Performance Evaluation of Utilizing Rust for PCAP Analysis in Satellite Cybersecurity

Previously, launched satellites were not designed with the necessary resource capacity or safety protocols to integrate essential Intrusion Detection Systems (IDS). This paper proposes the use of Rust to develop a statistics-based IDS, leveraging the language’s fast, efficient, and memory-safe attributes. The paper begins by providing an overview of cybersecurity threats to space infrastructure, introducing the fundamentals of intrusion detection, and outlining the architecture of space systems as background knowledge. It then details the proposed methodology for using Rust to build a statistics-based IDS. By comparing this approach with traditional methods, such as Python’s pandas, the paper aims to evaluate Rust’s speed and efficiency, advocating for its adoption in the development of more secure space systems

Structural Index Parameter for Capturing Structural & Aerothermal Effects in Conceptual Level Vehicle Design

The three phases of vehicle conceptual design include parametric sizing, configuration layout, and configuration evaluation. During the parametric sizing phase, the ability to define and quantify the technology level of an aerospace system allows the assessment of candidate designs based on feasibility given current technology or indicates if one must advance a particular technology. To meet this need, the structural Index (Istr) parameter merits exploration to consider structural and aerothermal effects during the parametric sizing phase of conceptual design given materials, structural concepts, and manufacturing capability. This study showcases the utility of this structural/materials technology parameter for high-speed vehicles by modernizing and expanding upon Paul Czysz\u27s original structural index (Istr) versus the surface temperature map. The modernized and expanded structural index (Istr) map is constructed by selecting a temperature-through-thickness method for a given thermal protection system (TPS) that simplifies a given surface temperature and atmospheric pressure profile into a constant heat pulse. One can then size the TPS to keep the structural temperature within material limits. The newly generated structural index (Istr) maps allow one to observe trends with variations in surface temperature, cruise time, average atmospheric pressure (Pavg), and TPS materials

Leveraging Python Interpreters for Concurrency in SeQUeNCe

With the advent of the Navy Research Laboratory’s announcement of the establishment of the Washington D.C. Metropolitan Quantum Research Consortium (DC-QNet), there has been much interest in the modeling and simulation of the quantum communication network testbed. To that end, we explore in this research the basic functionality of the Simulator of QUantum Network Communication (SeQUeNCe), the developmental Python/C API Interpreters module, and their viability as technologies to be used for high-performance simulation of quantum networks. In this paper, we outline the integration of sub-interpreters with a parallel SeQUeNCe experiment to demonstrate true multi-threading concurrency in quantum network simulations

Biot Number Error in Low-Temperature Inconel Overall Effectiveness Experiments

To predict the performance of turbine materials at engine conditions, experiments are often performed at low-temperature laboratory conditions. In order to ensure the low-temperature, laboratory results accurately predict the nondimensionalized surface temperature at engine conditions, several nondimensional parameters must be matched in the experiment, including the Biot number. Matching the Biot number requires that the ratio of the thermal conductivity of the material to the thermal conductivity of the air must be matched between laboratory experiments and engine conditions. With traditional nickel alloys such as Inconel, it is sometimes assumed that the Biot number is matched since Inconel\u27s thermal conductivity variation with temperature scales relatively closely with that of air. However, the thermal conductivity ratio does not scale perfectly and therefore some Biot number error does indeed exist, with the problem exacerbated at lower testing temperatures. To date, there has been no experimentally verified quantification of the error in the overall effectiveness, ϕ, that might be caused by this Biot number error. Ti-6Al-4V is predicted to allow for a better Biot number match, thereby better simulating Inconel at engine conditions in typical low-temperature experiments. In this research, we utilized geometrically identical models constructed of Ti-6Al-4V and Inconel 718 to evaluate the error in overall effectiveness that might occur through simply using an actual engine nickel alloy part at experimental conditions. While the Ti-6Al-4V model has a nearly perfectly matched Biot number, the Inconel model\u27s Biot number was 73% higher than appropriate. The results demonstrate that ϕ measured in low-temperature tests performed on an Inconel turbine component do not suffer markedly from Biot number error. The theoretically more Biot number appropriate Ti-6Al-4V model produced area-averaged overall effectiveness values that differed by only 0.01 from its Inconel counterpart. These results suggest that typical nickel superalloys used in turbine components may be tested at low temperature without the use of a surrogate material to better match Biot number

A Standardized Methodology for Evaluating a Digital Badging System [ Data Package ]

Digital badges, a form of micro-credentials, have grown in popularity over the past decade. However, few standard processes exist to assess the potential of digital badging systems within an organization. This study proposes a generalizable methodology for comparing a badging system with other methods of recording skills and competencies. The experimental design is tested using the military\u27s cyber operations community as the target organization. Finally, mixed-method data from thirty-six participants is analyzed in accordance with the methodology. Based on the results, digital badging systems are perceived to be more valuable and usable than a current method of military talent management. This approach supports efforts to enhance formal and informal learning, competency-based learning, granular decision-making, and to build trustworthy systems of record