To build an Artificial Intelligence system that can assist us in daily lives, the ability to understand the world around us through visual input is essential. Prior studies train visual perception models by defining concept vocabularies and annotate data against the fixed vocabulary. It is hard to define a comprehensive set of everything, and thus…

The dream of intelligent assistants to enhance programmer productivity has now become a concrete reality, with rapid advances in artificial intelligence. Large language models (LLMs) have demonstrated impressive capabilities in various domains based on the vast amount of data used to train them. However, tasks which require structured reasoning or…

The rise of Artificial Intelligence technology has raised concerns about the potential compromise of privacy due to the handling of personal data. Private AI prevents cybercrimes and falsehoods and protects human freedom and trust. While Federated Learning offers a solution by model training across decentralized devices or servers, thereby…

Education is becoming more integrated with technology use. As Ammanath (2023) described, the older generation of educators need to move past their fears of technological advances to find the potential to better prepare younger generations for an artificially intelligent world. This dissertation addresses how teachers perceive the use of artificial…

Digital systems are pervasive in modern societies -- augmenting personal and commercial driving, detecting cancer, and exploiting transitory events in financial markets. Attacks on these systems are growing in number, sophistication, and impact. Current cyber defenses are proving inadequate against some of these attacks. Cyber defense tools that…

In an era where digital platforms increasingly shape the educational experiences of learners, this dissertation examines activity in the Mathematics Discord Server (MDS), an expansive online learning community used by hundreds of thousands of mathematics learners worldwide. Daily interactions, numbering in the tens of thousands, focused on…

Advances in Natural Language Processing (NLP) over the past decade have largely been driven by the scale of data and computation used to train large neural network-based models. However, these techniques are inapplicable to the vast majority of the world's languages, which lack the vast digitized text datasets available for English and a few other…

Mathematical reasoning, a fundamental aspect of human cognition, poses significant challenges for artificial intelligence (AI) systems. Despite recent advancements in natural language processing (NLP) and large language models (LLMs), AI's ability to replicate human-like reasoning, generalization, and efficiency remains an ongoing research…

This action research study's primary purpose was to explore student and instructor experiences of chatbot use in online and hybrid undergraduate English composition courses. This qualitative study used structured and open-ended survey questions, interviews, and chat transcripts to collect data. The collaboration process among researcher and…

This research explores the emerging field of data science from the scientometric, curricular, and altmetric perspectives and addresses the following six research questions: 1.What are the scientometric features of the data science field? 2.What are the contributing fields to the establishment of data science? 3.What are the major research areas of…

This dissertation investigates how collective process-oriented documentation tools, combined with Natural Language Processing (NLP) techniques, can enhance knowledge construction in hands-on, open-ended learning environments, such as makerspaces. Through a three-year design-based research, the study developed and tested a collective documentation…

With the increased adoption and use of smart home speakers in households, many young children have learned to use listening and speaking to interact with the voice assistants (VAs) for a variety of daily activities (e.g., asking questions, listening to music). Despite the proliferation of VAs, due to the technical limitations such as automatic…

This study explores using transfer learning in machine learning for natural language processing (NLP) to create generic automated essay scoring (AES) models, providing instant online scoring for statewide writing assessments in K-12 education. The goal is to develop an instant online scorer that is generalizable to any prompt, addressing the…

Data-driven learning uncontroversially plays a role in human language acquisition--how large a role is a matter of much debate. The success of artificial neural networks in NLP in recent years calls for a re-evaluation of our understanding of the possibilities for learning grammar from data alone. This dissertation argues the case for using…

Dialogue act (DA) classification plays an important role in understanding, interpreting and modeling dialogue. Dialogue acts (DAs) represent the intended meaning of an utterance, which is associated with the illocutionary force (or the speaker's intention), such as greetings, questions, requests, statements, and agreements. In natural language…