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- Creators: Computer Science and Engineering Program
Description
This paper outlines the development of an efficient glossary navigation system that uses
artificial intelligence to provide instant results through a dynamic search bar. The project uses
several tools, including Python, Hypertext Markup Language (HTML), Cascading Style Sheets
(CSS), JavaScript, and the Google Gemini API. Market research was conducted to ensure the
glossary stands out from other products, as few competitors integrate AI. Unlike the traditional
long, continuous pages of most competitors, this project displays only the words, showing
definitions only when needed. The integration of the Google Gemini-powered dynamic search
engine was successful, achieving the goal of eliminating manual navigation and simplifying the user experience with a streamlined, quick-access layout.
ContributorsPatel, Samir (Author) / Osburn, Steven (Thesis director) / Pokidaylo, Boris (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2025-05
Description
Maps are crucial in emergency response operations, offering vital geographical data that aids teams in navigation and strategy. However, during catastrophic events like forest fires or floods, which can sever major routes, maps can quickly become outdated. In such scenarios, search and rescue teams and other first responders may find themselves scrambling to navigate and plan without dependable maps, complicating their mission.
Emergency response teams, specifically aerial imaging companies and wildfire teams, have a clear need to generate maps using aerial imaging. The conventional approach to synthesizing maps from aerial imagery is taking aerial images and accurately stitching them together, called an orthomosaic. Unfortunately, this process is both very expensive and slow. Highly accurate sensors and cameras required for real time map creation can cost hundreds of thousands of dollars. In other cases where this equipment is not available, producing maps can take overnight. This results in issues of inaccessibility and information arriving too slowly for effective emergency response.
This study investigates a cost effective, efficient solution utilizing standard cameras mounted on drones to capture aerial images and projecting them over satellite data in real time. By utilizing homography estimation image transforms, we evaluate two methods of image transformations: standard key point based matching and EXIF data camera extrinsics estimation. Both methods have strengths and weaknesses that inform future developments, including creating a hybrid model where the strengths of both methods are leveraged.
Ultimately, the work done in this experiment serves as a foundation for future research into achieving real time cost effective orthomosaic creation. Successfully implementing these methods would empower emergency responders with situational awareness enhancing the speed and accuracy of critical missions.
ContributorsKattenbraker, Luke (Author) / Nayak, Samik (Co-author) / Osburn, Steven (Thesis director) / O'Connor, Peter (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2025-05
Description
Athletic Breakdown is an interactive and educational website detailing the nuances of the current NFL landscape, using relevant computer science technologies and practices. This platform is used to help break down the strengths and weaknesses of each player and team in an easy to understand format for those who are looking to expand their knowledge of the game. It consists of a home page, player pages, and teams pages. The home page contains relevant news and game scores. Each player and team page, there are high-level overviews of their strengths and weaknesses, with each aspect broken down into easier to understand terms. The website is built using TypeScript, React, Vite, and TailwindCSS for the front-end. The back-end is built using PostgreSQL database and SupaBase for the back-end as a service.
ContributorsWill, Adrian (Author) / Atkinson, Robert (Thesis director) / Osburn, Steven (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2025-05
Description
As interest in food and beverage education grows among consumers and professionals alike, there is increasing demand for learning tools that are accessible, interactive, and tailored to individual needs. Traditional methods such as static tasting guides or instructor-led classes often lack personalization and real-time feedback, limiting their impact on learners with varying experience levels. In response to this gap, this thesis presents the design, development, and evaluation of the Sip & Savor Study chatbot: an AI-powered virtual sommelier embedded within a mobile application that supports personalized beverage education.
Built using React Native, FastAPI, Firebase, Render and OpenAI’s GPT-4 API, the chatbot delivers real-time recommendations and interactive learning experiences across four major beverage categories: wine, beer, saké, and cocktails. It dynamically adapts to user preferences (e.g., dietary needs, favorite drink types, and taste profiles) and supports contextual conversations that simulate expert guidance. The system architecture was developed through modular backend/frontend integration, and iteratively refined through usability feedback and internal testing cycles.
A user study involving thirty participants at Arizona State University was conducted to evaluate the chatbot’s effectiveness. Results from post-interaction surveys showed high user satisfaction in areas such as response clarity, beverage recommendation accuracy, and conversational tone. Most users found the chatbot easy to use, educational, and engaging, while personalization features were well-received—though opportunities for refinement in response speed and interface clarity were identified. Updates made based on this feedback included onboarding instructions, improved preference visibility, and backend optimizations to reduce latency.
This work demonstrates how generative AI models can be applied meaningfully in experiential learning contexts, particularly those requiring nuanced guidance and dynamic user engagement. The findings contribute to ongoing discussions about the role of large language models in education and present a scalable model for future AI-driven learning applications within lifestyle and hospitality domains.
ContributorsLin, Waley (Author) / Echeagaray, Maria (Thesis director) / Ortiz, Michael (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2025-05
DescriptionThis thesis is explores the positive psychological effects narrative video games have in relation to the grieving process.
ContributorsSoriano, Ash (Author) / Bauer, DB (Thesis director) / Kirtz, Jaime (Committee member) / Barrett, The Honors College (Contributor) / Arts, Media and Engineering Sch T (Contributor)
Created2025-05
Description
While introductory machine learning education emphasizes both theoretical foundations and programming syntax, the ability to confidently navigate data workflows and make informed decisions is typically developed through repeated, project-based experience. Interest in machine learning education is expanding simultaneously with the widespread availability of generative artificial intelligence (GAI) tools, capable of automating data analysis, decision-making, and code generation. While convenient, GAI tools may risk undermining students’ development of essential reasoning skills when used without a solid understanding of the underlying concepts. This issue may be particularly pressing for machine learning students attempting to conduct a machine learning workflow, already balancing the dual demands of learning programming and machine learning theory.
DataPylot is an interactive application designed to support early learners in machine learning by fostering strategic intuition around effectively applying machine learning techniques to data. The tool provides a structured interface that enables users to upload a dataset of their choice and apply common supervised machine learning tasks step-by-step. At each step, users make explicit choices through a graphical interface, and the application deterministically maps these selections into formatted Python code ready for execution. DataPylot supports code generation for many typical tasks within a machine learning workflow, including importing a raw dataset, applying exploration and preprocessing techniques, and training and evaluating machine learning models. This approach places the reasoning and decision-making process in the hands of the learner while reducing programming barriers and minimizing common errors.
To evaluate DataPylot’s educational value, a user study was conducted with Arizona State University students who had prior experience with machine learning. Participants successfully completed a guided machine learning project using only code generated within DataPylot to explore, preprocess, and model a provided dataset. Afterwards, participants completed a questionnaire assessing perceived challenges in learning machine learning, the ease of use and value of using the tool, and its comparative utility relative to GAI tools. Results indicated that participants found DataPylot useful for applying machine learning and supporting independent reasoning, particularly among lower-experience learners. These findings provide support for the potential of structured, interactive, and deterministic tools like DataPylot to foster conceptual understanding and strategic intuition surrounding applying machine learning to data through guided hands-on engagement.
ContributorsRice Nulty, Seth (Author) / Chavez Echeagaray, Maria Elena (Thesis director) / Zhu, Qiyun (Committee member) / Barrett, The Honors College (Contributor) / Department of Psychology (Contributor) / Computer Science and Engineering Program (Contributor)
Created2025-05
Description
This project introduces a new platform built on Kubernetes that makes it easy to run small, efficient programs (serverless functions) using a technology called WebAssembly. It aims to improve upon existing systems that don't work well with WebAssembly. Users can simply upload their code through a web interface, and the platform automatically handles all the complex steps needed to package and run it on Kubernetes using standard industry approaches. The goal is to provide developers with a straightforward way to build and deploy WebAssembly-based serverless functions in a modern cloud environment.
ContributorsMykhaylov, Michael (Author) / Bazzi, Rida (Thesis director) / Cook, Corey (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2025-05
Description
Canada’s dynamic immigration system attracts thousands of newcomers each year, yet many immigrants face persistent challenges in achieving full economic, social, and cultural integration. This thesis addresses these challenges through the design and development of a mobile application aimed at supporting immigrant settlement and integration. The application functions as a personalized tool that calculates the gap between an immigrant’s current status, based on key metrics such as immigration status, student history, skill metrics, employment history, and desired integration goals. By leveraging user input and publicly available settlement benchmarks, the app provides targeted, actionable recommendations tailored to each individual’s circumstances. The goal of the app is to empower users with clear, data-driven pathways toward successful integration. The thesis includes a review of integration theory, an analysis of current settlement support infrastructures in Canada, and a usability evaluation of the app among recent immigrants. The findings suggest that digital tools like this app can play a vital role in bridging information gaps, promoting self-advocacy, and enhancing long-term settlement outcomes.
ContributorsCantoni, Emma (Author) / Osburn, Steven (Thesis director) / Khan, Saad (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2025-05
Description
A custom physical-layer networking protocol was created to enable file transfer between two Ultra-Wideband boards. This is an exciting advancement because Ultra-Wideband isn't widely used in the consumer space for data transfer. The newly defined protocol allows Ultra-Wideband to be utilized for data transfer on smaller, more affordable hardware than its industrial counterparts.
ContributorsSweeney, Zachary (Author) / Osburn, Steven (Thesis director) / Kim, Hokeun (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / The Sidney Poitier New American Film School (Contributor)
Created2025-05
Description
Selective radiative coolers (SRCs) were incorporated into a high-resolution WRF simulation of Arizona for May to June 2024 to assess urban cooling efficacy. SRCs delivered 0.5 to 0.9 °C cooling in Tucson. Model validation against urban weather stations showed reasonable levels of error in the model, supporting reliability and comparison. Overall, this marginal cooling underscores the need for broader strategies to be employed to combat urban heat in Arizona
ContributorsSwart, Ryan (Author) / Georgescu, Matei (Thesis director) / Moustaoui, Mohamed (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2025-05