Intelligent dialog agent modeling in human-centered artificial intelligence applications

[EMBARGOED UNTIL 08/01/2026] Dialog systems have been integral in which ways humans and AI models can interact across multiple domains such as healthcare, customer service, and education. With the recent advancements in deep learning and pre-trained language models (PLMs), dialog systems have shown impressive performance in various downstream natural language processing tasks for the purposes of providing confidence in human decision making in real-world applications. Despite the recent success, traditional dialog systems are challenged in two ways: (a) dialog systems that trained/pre-trained autonomously -- which does not require human involvement -- can lead to training inefficiencies, lack of human alignment, and lack of feedback loops; (b) fully autonomous systems that lack prediction justifications can be harmful if humans blindly follow the predictions outcomes. Thus, as humans continue to adopt dialog systems for their domain-specific or personal needs, synergistic efforts between dialog systems and humans are needed to increase trust, transparency, and user satisfaction in realistic applications. In addressing these major challenges, this thesis introduces the design and modeling of "Human- Centered Dialog Systems" (HCDS), which leverages human-centered AI (HCAI) techniques to integrate humans in every facet of the dialog development life cycle. Specifically, we present three novelty areas in which HCDS can be employed to alleviate the aforementioned challenges: (i) Holistic Multi-Layer Dialogs details the design of dialog systems in across a multi-layered development lifecycle, which includes the data manage layer, model management layer, and application layer; (ii) Efficient Human-in-the-Loop Dialogs aims to eliminate the inefficiencies of dialog training by incorporating humans for annotation and labeling feedback; (iii) Trustworthy and Factual Dialogs leverages human teaching and instruction to ensure deployed models for inference in application settings provide transparent and factual answers.

Name thesis:degree_name

Ph. D

Level thesis:degree_level

Doctoral

Discipline thesis:degree_discipline

Computer Science

Grantor dc:publisher

University of Missouri--Columbia

Year dc:date.issued

2025

Author dc:creator

• Oruche, Roland

Advisor dc:contributor.advisor

• Prasad, Calyam

Language dc:language.iso

eng, English

DOI

https://doi.org/10.32469/10355/110390

OAI identifier oai:identifier

oai:mospace.umsystem.edu:10355/110390