HUMAN-CENTERED AI DEVELOPMENT FOR AUGMENTED COGNITION

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dc.contributor.advisorCrosby, Martha E.
dc.contributor.authorDoliashvili, Mariam
dc.contributor.departmentComputer Science
dc.date.accessioned2025-02-20T22:37:11Z
dc.date.available2025-02-20T22:37:11Z
dc.date.issued2024
dc.description.degreePh.D.
dc.identifier.urihttps://hdl.handle.net/10125/110249
dc.subjectComputer science
dc.subjectArtificial intelligence
dc.subjectAugmented Cognition
dc.subjectCode Comprehension
dc.subjectDeep Learning
dc.subjectHuman Computer Interaction
dc.subjectHuman-centered AI
dc.subjectLarge Language Models
dc.titleHUMAN-CENTERED AI DEVELOPMENT FOR AUGMENTED COGNITION
dc.typeThesis
dcterms.abstractThis dissertation examines the integration of artificial intelligence (AI) into daily life, emphasizing human-centered AI development for augmented cognition (AC), which focuses on enhancing rather than replacing human capabilities. It explores how individuals perceive and interact with AI systems compared to how they interact with other humans. In addition, it studies how AI systems can interpret and adapt to humans’ cognitive states.During the development of AI systems, the focus has been on creating fully autonomous systems, completely replacing manual human labor. However, there are tasks where humans outperform AI systems, and there are also tasks where human-AI systems (HAIS) can outperform both traditional human labor and autonomous AI systems. Rapid replacement of humans with AI systems raises security and privacy concerns and increases operational complexity. In addition, AI models have limited personalization, marginalizing user groups from diverse languages and cultures. This research examines designing AI systems to complement human capabilities: how they could detect, adapt to, and personalize responses based on a human’s cognitive state, encompassing intentions, languages, and cultural backgrounds. The following studies identify user intent through biometric markers (e.g., mouse or pen pressure), detect native and non-native users, assess the impact of linguistic and cultural diversity on user preferences and performance, and evaluate Large Language Models (LLMs) as tools for human enhancement. This dissertation underscores the importance of designing AI systems with consideration and adaptability to users’ diverse backgrounds, ensuring inclusivity and personalization and that they are being delivered to people in a way that enhances their capabilities.
dcterms.extent165 pages
dcterms.languageen
dcterms.publisherUniversity of Hawai'i at Manoa
dcterms.rightsAll UHM dissertations and theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission from the copyright owner.
dcterms.typeText
local.identifier.alturihttp://dissertations.umi.com/hawii:12438

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Ph.D. - Computer Science