Large language models (LLMs) are increasingly framed as force multipliers for cyberattacks, yet most existing evaluations focus on isolated artifact generation rather than the construction and execution of full offensive workflows. This paper presents a controlled empirical study of LLM-assisted cyberattack construction across multiple representative attack classes, including automated SQL injection exploitation, spyware assembly, reverse shell establishment, and denial-of-service traffic generation. We evaluate several contemporary models—including ChatGPT-4o, ChatGPT-5.2, ChatGPT-5.1-instant, Claude Sonnet 4.6, and Gemini 3—within fully sandboxed virtualized environments, treating each model strictly as an advisory system embedded within a human-driven workflow.

Our experimental design decomposes attacks into staged operational workflows encompassing reconnaissance, payload generation, system integration, troubleshooting, and persistence. This structure enables systematic analysis of where automation succeeds or fails during real execution rather than relying on single-shot demonstrations. Across scenarios, LLMs consistently reduce effort for localized technical tasks such as command syntax recall, tool configuration, payload scaffolding, and procedural troubleshooting. However, reliable end-to-end attack execution remains limited. SQL injection automation succeeds primarily when established tools encapsulate complex orchestration, while more complex scenarios such as spyware assembly fail at system-level integration, environment-specific dependency resolution, and evasion of host defenses.

Across models and attack classes, automation consistently breaks at environment-dependent boundaries requiring global reasoning, state awareness, and cross-stage workflow coordination. These findings suggest that contemporary LLMs do not autonomously execute cyberattacks but instead function as workflow accelerators that lower the expertise threshold required to operationalize existing offensive techniques. This capability-boundary perspective provides a more realistic foundation for threat modeling, defensive planning, and future evaluation of AI-assisted cybersecurity risks.

Medical students lack effective tools for developing clinical reasoning, as most resources emphasize memorization rather than decision-making. DiseaseQuest is an AI-powered, gamified platform that addresses this gap through realistic patient simulations and decision-based scenarios. It enables students to work through complete clinical cases using interactive, patient-centered dialogue. Supported by a multi-agent framework, the platform provides adaptive guidance, diagnostic feedback, and personalized evaluations. By promoting active learning and problem-solving, DiseaseQuest offers a transformative approach that replaces passive study with immersive, hands-on practice, helping students strengthen diagnostic thinking and better prepare for real-world clinical decision-making.

As obesity continues to rise in the United States, bariatric surgery has become as increasingly common medical intervention to support significant and sustained weight loss. However, the procedure presents challenges, as patients must adopt strict dietary guidelines, develop consistent meal tracking habits, and maintain long-term lifestyle changes. Existing weight-loss applications fail to address the unique nutritional requirements of bariatric patients, which include surgery-specific restrictions, medical conditions, personal preference in food, and individualized lifestyle factors. Along with that, they lack integrated long-term monitoring tools that allow healthcare providers to effectively track patient progress and adherence after surgery. This senior design project presents a prototype mobile application developed from scratch to support patients throughout the bariatric journey. The application integrates AI-driven personalization to generate tailored daily nutritional guidance, adapt to individual health data, and provide meal tracking support. In addition, the platform centralizes patient data for healthcare providers, improving long-term monitoring, increasing tracking accuracy, and reducing manual workload. By combining personalized patient support with provider-facing analytics, this solution aims to enhance postoperative adherence and improve long-term surgical outcomes.

The Brazos Safety Systems Weather Application provides insight into weather conditions that affect aviation operations. Weather plays a critical role in flight safety, and understanding conditions during flight operations can help explain incidents or risky behavior that occur. The application integrates aviation weather data sources, including METAR reports and radar imagery, to visualize weather conditions around airports and during historical flights. Users can upload flight records and review the weather conditions associated with those flights through the application. By presenting aviation weather data in a centralized and accessible format, the application supports post-flight analysis and helps identify weather-related factors associated with flight incidents. The goal is to provide insights that assist in understanding past flight conditions and help prevent similar issues in future aviation operations.

Solar flares are among the most significant drivers of space-weather disturbances, motivating ongoing efforts to develop reliable forecasting methods from solar observations. The Solar Dynamics Observatory continuously produces high-resolution full-disk solar imagery used for monitoring solar activity. These observations have enabled substantial progress in machine learning–based flare prediction; however, most models remain confined to research studies, with limited deployment in operational systems that support continuous forecasting and systematic performance validation. This work presents a lightweight operational framework for near-real-time solar flare forecasting built around machine learning models proposed in the literature. The system retrieves full-disk solar imagery from the Helioviewer API, performs automated preprocessing, and generates predictions using a convolutional neural network–based forecasting model. Predictions and corresponding observations are stored to enable periodic forecast verification using standard performance metrics, allowing model performance to be monitored over time and potential prediction drift to be identified. The framework is implemented as an interactive application using Streamlit, providing an integrated interface for automated data ingestion, near-real-time inference, and ongoing model evaluation. The proposed system enables continuous monitoring of solar flare forecasts while providing a practical framework for tracking model performance and detecting prediction drift in operational settings.