Matching Items (17)
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- All Subjects: AI
- Creators: Computer Science and Engineering Program
Description
The Therapist Assistant AI project focuses on the development of several chatbots based on ChatGPT 4o to aid therapists by functioning as a general assistant, allowing Therapists to bounce ideas off them, suggesting solutions, and letting Therapists use natural language to interact with the client’s data to retrieve and analyze it. It is integrated with the overall Empath project it is a part of, including Empath's APIs. It uses synthetic data generation to compensate for the lack of publicly available datasets for therapist assistant models.
ContributorsReza, Saqib (Author) / Osburn, Steven (Thesis director) / Patil, Karan (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-12
Description
This paper details the process for designing both a simulation of the board game Jaipur, and an artificial intelligence (AI) agent that can play the game against a human player. When designing an AI for a card game, there are two major problems that can arise. The first is the difficulty of using a search space to analyze every possible set of future moves. Due to the randomized nature of the deck of cards, the search space rapidly leads to an exponentially growing set of potential game states to analyze when one tries to look more than one turn ahead. The second aspect that poses difficulty is the element of uncertainty that exists from opponent feedback. Certain moves are weak to specific opponent reactions, and these are difficult to predict due to hidden information. To circumvent these problems, the AI uses a greedy approach to decision making, attempting to maximize the value of its plays immediately, and not play for future turns. The agent utilizes conditional statements to evaluate the game state and choose a game action that it deems optimal, a heuristic to place an expected value (EV) of the goods it can choose from, and selects the best one based on this evaluation. Initial implementation of the simulation was done using C++ through a terminal application, and then was translated to a graphical interface using Unity and C#.
ContributorsOrr, James Christopher (Author) / Kobayashi, Yoshihiro (Thesis director) / Selgrad, Justin (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
The integration of Artificial Intelligence (AI) algorithms and tools in cloud computing is revolutionizing business processes with its increased adoption among leading technology companies. By analyzing the AI Cloud solutions that companies offer across diverse industries– including healthcare, gastronomy, streaming applications, and service providers– this thesis determines their impact on overall company productivity and user experience. Some of the platforms offering these tools that were evaluated include those from major technology and business intelligence companies, such as Salesforce’s AI Cloud Tools, Oracle’s Cloud Infrastructure, Amazon Web Services, Microsoft Azure’s OpenAI Service, and Google Cloud. In order to determine the impact of AI Cloud on both company productivity and user experience, this thesis cross-analyzed shifts in metrics such as workload efficiency, customer resource management, sales performance, and financial outcomes following AI Cloud implementation. The initial implementation of AI Cloud can be costly and as an increasingly pervasive technology with potential to attract security threats, it can be met with uncertainty and doubt. Despite these initial disadvantages, the metrics used in this thesis suggest that AI Cloud solutions have an overall positive impact on company productivity and user experience when intentional, proper deployment is exercised. The literature in this thesis suggests that achieving this successful deployment requires consideration of ethical guidelines, security practices, and human-AI integrated mediation when implementing AI solutions. By ensuring that the AI Cloud solutions companies offer are optimal for their processes, have proper safeguards in place for any potential errors and security concerns, and prioritize a positive user experience for both employees and customers, companies can increase their productivity and overall efficiency of their business processes.
ContributorsLecuru, Sophia (Author) / Wang, Amy (Co-author) / Martin, Thomas (Thesis director) / Roumina, Kavous (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / School of International Letters and Cultures (Contributor)
Created2025-05
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
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
Description
This honors thesis project is an extension of my computer science capstone project, Almanac: Idea to MVP. The capstone project involves an AI agentic workflow that takes in a product idea and generates a functional MVP, and this thesis project adds to its functionality by allowing a customer persona to provide feedback on the generated MVP and iterate on the final product. This project changes the static generation of MVPs into a dynamic, user-centered approach that allows for product iteration based on customer feedback.
ContributorsKelwalkar, Aditi (Author) / Osburn, Steven (Thesis director) / Kommuri, Sai Charan Tej (Committee member) / Barrett, The Honors College (Contributor) / School of Public Affairs (Contributor) / Computer Science and Engineering Program (Contributor) / Computing and Informatics Program (Contributor)
Created2025-05
Description
The perfect anti-cheat software for a first person shooter that balances protecting user privacy and effective cheat detection in a modern age where dishonest methods of gameplay are rampant within competitive games. By utilizing the inherent protections servers have against third party attacks, by removing the software off of the client, all of the detection methods are placed in an external area, where cheaters are determined by behavior that is tracked through statistical trackers placed in the game. By measuring multiple key features including Illegal Trace Time, Trigger Time, and Mouse Flick Speed. Each of these measured attributes relate to commonly used cheats in first person shooters, which is the target for this anti-cheat machine learning model. By gathering a wide range of statistics and figuring out the average player’s statistics, it would be possible to determine if a player is using external programs to gain an unfair advantage.
ContributorsKim, James (Author) / Kobayashi, Yoshihiro (Thesis director) / Baek, Jaejong (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2025-05