Matching Items (49)
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- All Subjects: Artificial Intelligence
- Creators: Computer Science and Engineering Program
Description
This thesis presents the design, development, and deployment of an AI-powered chatbot made for Abundant Harvest Aquaponics, an agriculture organization focused on sustainable eco-friendly food production and community engagement. The primary goal of this project was to modernize the organization's customer interaction capabilities through an intelligent, conversational interface that streamlines communication, enhances user experience, and supports operations such as donations, CSA (Community Supported Agriculture) subscriptions, and educational outreach.
Built using natural language processing (NLP) technologies and integrated into the renovated organization’s existing website infrastructure, the chatbot was trained to handle frequently asked questions, guide users through various services, and provide real-time information about aquaponics systems. The implementation process involved iterative design, feedback-based refinement, and aligning with the organization’s mission and audience, which includes health-conscious consumers and donors aged 24–60.
The chatbot has been successfully deployed, but metrics have not yet been collected. Early observations suggest it is functioning as intended, with the potential to improve user engagement, reduce the inquiry load on staff, and enhance overall customer satisfaction as intended. The project is a major stepping-stone for Abundant Harvest Aquaponics. It reflects how AI can help benefit small and medium-sized enterprises and support sustainable, community-oriented agriculture.
ContributorsAlvarado, Ivan (Author) / Miller, Grant (Co-author) / Osburn, Steven (Thesis director) / Hendrix, Charles (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2025-05
Description
This thesis presents the design, development, and deployment of an AI-powered chatbot made for Abundant Harvest Aquaponics, an agriculture organization focused on sustainable eco-friendly food production and community engagement. The primary goal of this project was to modernize the organization's customer interaction capabilities through an intelligent, conversational interface that streamlines communication, enhances user experience, and supports operations such as donations, CSA (Community Supported Agriculture) subscriptions, and educational outreach.
Built using natural language processing (NLP) technologies and integrated into the renovated organization’s existing website infrastructure, the chatbot was trained to handle frequently asked questions, guide users through various services, and provide real-time information about aquaponics systems. The implementation process involved iterative design, feedback-based refinement, and aligning with the organization’s mission and audience, which includes health-conscious consumers and donors aged 24–60.
The chatbot has been successfully deployed, but metrics have not yet been collected. Early observations suggest it is functioning as intended, with the potential to improve user engagement, reduce the inquiry load on staff, and enhance overall customer satisfaction as intended. The project is a major stepping-stone for Abundant Harvest Aquaponics. It reflects how AI can help benefit small and medium-sized enterprises and support sustainable, community-oriented agriculture.
ContributorsMiller, Grant (Author) / Alvarado, Ivan (Co-author) / Osburn, Steven (Thesis director) / Hendrix, Charles (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / Department of Military Science (Contributor)
Created2025-05
Description
This thesis introduces an intelligent database system that harnesses Natural Language Processing (NLP) and Machine Learning (ML) to enable seamless querying and visualization of sports data. Centered on the English Premier League—the top tier of English football—the system empowers users to interact with complex datasets through simple natural language queries. These queries are automatically translated into structured SQL commands, eliminating the need for technical expertise and making data retrieval more accessible.
In addition to flexible querying, the system supports dynamic data visualization, presenting results in user-specified formats such as tables, charts, or graphs. By integrating NLP and ML, the system streamlines the end-to-end process of data access, analysis, and presentation. This not only enhances the usability of sports data for analysts, researchers, and enthusiasts but also promotes data-driven exploration and insight generation. The proposed system represents a step toward democratizing sports analytics by bridging the gap between natural language understanding and structured data querying, enabling richer, more intuitive interactions with complex information.
ContributorsMartinez, Sebastian (Author) / Gupta, Vivek (Thesis director) / Bryan, Chris (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2025-05
Description
Artificial intelligence (AI) has rapidly progressed over the past couple of years, sparking worldwide concerns about whether these new technologies will take human jobs, especially in the film industry. This thesis delves deeper into the ethical implications of AI in filmmaking and its potential to change the film industry as we know it. AI technologies have found ways to streamline production processes, creating benefits such as cost efficiency for small-budget filmmakers. Although these developments can optimize pre-production, production, and post-production processes, it causes distress with factors such as ethical use of information, job security, and creative integrity. A qualitative methodology incorporating literature reviews and case studies of visual media, is used to explore the dual-sided sword AI can be: democratizing the filmmaking process while also making people question the authenticity of the new age of cinema. This study explores whether AI can maintain a human element in art without ruining authorship in AI-generated scripts, or the use of deep fakes. Recommendations are made to ensure transparency and consent are always prioritized when innovating, to maintain ethical integrity. This thesis argues that while AI can support filmmakers through creative inspiration, and streamlining certain processes, it should never completely replace human authorship. AI is there to complement, not supersede the authentic storytelling that defines film. By highlighting these obstacles, this thesis can add to the ongoing conversation about whether AI should be involved in cinema's future and our obligation to establish equitable and progressive regulations.
ContributorsPartha, Ajay (Author) / Malpe, Adwith (Thesis director) / Wheatley, Abby (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / Dean, W.P. Carey School of Business (Contributor)
Created2025-05
Description
In this project we are creating a tool for students that will help them gain dynamic educational
pathways based on their projects. By having a dataset of a college’s course standards and
matching the student’s work we get to show the student which courses they can gain potential
Microcredits for. By utilizing Artificial Intelligence (AI) and Natural Language Processing
(NLP) with Bidirectional Encoder Representations from Transformers (BERT) based sentence
embeddings, Facebook AI Similarity Search (Faiss) for similarity search, and KMeans for
clustering, we identify and group top-matching courses from a Firestore database to show to
students. In order to visualize the data, we use Neo4J to create a graphical nodal representation
of this data. This allows for dynamic and endless credit possibilities and creativity for students to
encourage them to try for new courses and learning.
ContributorsSingh, Suhani (Author) / Osburn, Steven (Thesis director) / Ernsberger, Karl (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2025-05
Description
Food waste is a prominent issue today in both environmental and economic terms with households being among the top contributors. The rise of artificial intelligence (AI) in consumer technology has created new opportunities to address everyday challenges, such as food waste. Solutions are being developed to bridge the gap between sustainable living and accessible solutions to make a difference in people’s lives. This thesis explores the market viability and societal impact of FridgeScan AI, an artificial intelligence (AI) driven mobile application designed to help users manage the contents of their refrigerators. The application was designed to use image recognition and machine learning to scan food items, manage fridge contents, generate shopping lists, and provide recipe suggestions based on what users have in their fridge. This research builds on the FridgeScan AI capstone project developed at Arizona State University. It combines a literature review and a user survey to assess the current market for smart kitchen technologies, specifically user expectations and concerns. Key themes include sustainability, cost savings, and data privacy. The survey was distributed among 53 individuals aged 18 and over to collect insights on the value, features, and ethical considerations of such an application. The results from the survey then informed the development of a simplified business model that analyzes potential revenue and strategies for deployment. Ultimately, the goal is to assess user interest in tools that may help reduce food waste and improve household organization, and how their concerns around data collection and camera use could impact that. This thesis concludes by exploring both the potential and the challenges of adopting AI-based home applications and offers possible directions for further development and evaluation.
ContributorsFarias, Sabrina (Author) / Chavez Echeagaray, Maria Elena (Thesis director) / Lee, Quak Foo (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2025-05
Description
Many small to mid-sized businesses face significant challenges in integrating artificial intelligence (AI) effectively, primarily due to a lack of internal expertise that bridges technical capabilities with core business operations. This gap leads to operational inefficiencies, increased costs, and potential competitive disadvantages. CAIO Labs addresses this critical issue by providing specialized AI consulting coupled with tailored training programs designed to empower internal team members as dedicated Chief AI Officers (CAIO). Through detailed operational audits and strategic training, CAIO Labs enables sustainable AI-driven transformations, significantly reducing dependency on costly external consultants. By specifically targeting businesses within professional services and e-commerce sectors, CAIO Labs positions itself uniquely in the market, offering personalized solutions that yield measurable improvements in productivity and scalability. This thesis outlines CAIO Labs' strategic approach, validates its core business assumptions, and demonstrates the viability and impact of embedding internal AI expertise within growing businesses.
ContributorsBhangale, Parth (Author) / Byrne, Jared (Thesis director) / Dearman, Jeremy (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2025-05
Description
In this project, my partner Archit and I looked to explore the future of no-code/low-code development. Throughout this project, we initially researched low-code/no-code development environments before settling on Microsoft Power Platform and Google Cloud to implement our capstone project, a retrieval augmented generation large language model teaching assistant that uses course-specific content pulled directly from Canvas and fed into the model. At the end of this project, we were able to fully replicate our capstone project's base frontend and the majority of our project's backend within two weeks, with a functional chat screen, two-factor authentication using a verification code, and a built-in user statistics report as compared to our capstone project, which took a whole semester to create.
ContributorsFeng, Gregory (Author) / Singh, Archit (Co-author) / Chen, Yinong (Thesis director) / De Luca, Gennaro (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2025-05
Description
A natural language processing (NLP) chatbot is a program that can communicate with a human by processing their language into understandable commands. While most associate AI with LLMs, these models are not as effective with specific, involved tasks. The goal of this thesis is to demonstrate how NLP can be combined with a small-scale generative AI model to create a chatbot that can complement larger projects. The thesis researches the benefits of a small-scale chatbot in contrast to larger models in cost, time efficiency, and accuracy, and it details an example of the implementation of a small-scale chatbot within a larger project. For the implementation, I have collaborated with my sponsor, Northrop Grumman, to integrate an NLP chatbot into their GSE Frontend project. The chatbot interacts with the user, requesting specific commands related to log history, graphing, and obtaining data from the main program. The result of the implementation is an effective tool that complements the main program’s purpose with little cost and error and has great expandability alongside the program to improve its functionality.
ContributorsKhondoker, Maheeb (Author) / Osburn, Steven (Thesis director) / Arora, Aman (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2025-05