Cecilia Delgado Solorzano

Autonomous underwater vehicles (AUVs) depend on reliable vision in low visibility, yet we lack clear evidence on when specialized detectors (YOLO-World) outperform multimodal vision-language models (Gemini 2.5 Pro.) This gap limits the informed model selection for underwater tasks. To address it, we examine how these two paradigms behave when applied to the same operational task focused on object presence determination. The dataset was generated using a synthetic underwater simulator spanning Low, Medium, and High visibility. Both models are evaluated on accuracy and latency. YOLO-World performs better in Low visibility and runs faster, while Gemini improves in clearer scenes but requires more computation. These findings indicate that model choice should align with visibility and runtime constraints, with detectors suited for real-time use and a multimodal model for offline tasks.

This formative study explores how second-language (L2) speakers envision personal AI assistants (PAIAs) supporting face-to-face communication with native speakers. Through participatory sessions with 14 bilingual adults combining scenario generation, role-play, and diagramming, we surfaced preliminary tensions in AI-mediated L2 interaction: managing conversational flow, balancing transparency with naturalness, and navigating context-dependent privacy. Our exploratory findings suggest three candidate mediation patterns (solo, symmetric, and relayed assistance), each with distinct trade-offs that warrant further investigation. We contribute initial design considerations including reply-when-free defaults and role-based privacy profiles, alongside a replicable protocol for eliciting AI behavior expectations. These preliminary insights establish a foundation for future empirical work on PAIAs in multilingual communication contexts.

AI chatbots may reinforce existing beliefs and discourage critical examination. This study evaluates Belief Explorer, an AI system that uses Socratic dialogue and multi-perspective analysis to support epistemic reflection. Participants recruited via Prolific used Belief Explorer to examine personally held beliefs in contested domains (e.g., climate change, origin of life). Post-intervention surveys measured system usability, perceived epistemic impact, and self-reported reflection. Participants responded positively: a large majority reported that Belief Explorer felt substantially different from conventional AI chatbots, prompted deep reflection on their beliefs, and increased awareness of underlying assumptions. Thematic analysis of open-ended responses revealed that participants valued the tool’s non-judgmental approach, multi-perspective framing, and analytical feedback mechanisms. Belief change itself was modest and bidirectional, with similar proportions reporting increased and decreased confidence in their initial beliefs. This preliminary study offers proof-of-concept that AI systems with epistemic scaffolding can support critical thinking and self-reflection. Future research should use controlled experimental designs to establish causal relationships and examine long-term effects on epistemic attitudes.

Crowdsourcing platforms employ millions of workers worldwide to complete microtasks that fuel machine-learning pipelines, moderate user-generated content, and accelerate scientific discovery. The rapid ascent of large language models and other foundation models has shifted research attention from automation instead of humans to augmentation alongside humans. This chapter addresses a central challenge in this new landscape: understanding how, how much, and for whom AI-driven tools truly empower crowdworkers. We synthesize insights from a diverse body of empirical studies, spanning randomized lab experiments, field A/B tests, trace-log meta-analyses, and qualitative user studies, to evaluate the impact of AI on crowdwork. To structure this analysis, we introduce a multidimensional empowerment framework grounded in psychological and economic theory, focusing on five key dimensions: productivity and earnings, quality and reliability, learning and skill development, agency and autonomy, and well-being and inclusion. Our review of the evidence reveals a dual-edged reality: while AI assistance offers significant productivity gains, error reductions, and improvements in worker self-efficacy, it also introduces substantial risks, including automation bias, cultural exclusion, wage depression, and unequal access to tools. By distilling these findings, we formulate eight concrete design principles for creating more equitable and effective human-AI augmentation systems. The chapter concludes by outlining a forward-looking research agenda that treats crowdworkers not as replaceable cogs but as essential partners in designing the future of socio-technical systems.

This research explores the difficulties advanced second-language learners face in becoming fluent and the poten-tial of AI to enhance language learning apps. Through surveys, focus groups, and collaborative design sessions, we investigated learners' difficulties and practice habits. The participants created paper prototypes for an app targeting speaking and listening skills at an advanced level. Key findings suggest that fluency de-velopment requires exposure to diverse accents from both native and non-native speakers. The resulting design incorporates AI in the form of a chatbot with voice recognition capabilities. This study contributes to our understanding of advanced language learning needs and the potential of AI to address them, paving the way for innovative language learning solutions. Our findings highlight opportunities to develop more effective tools for learners striving to achieve fluency in their target languages.

As artificial intelligence (AI) and robotics have become more capable as technologies, human-AI teams (HATs) have sought to model how humans can interdependently work with AI to accomplish a shared goal. Specifically, HATs seek to leverage autonomy in both digital and physical AI agents to improve the teamwork and outcomes of these teams. In search-and-rescue (SAR) teams, where situations are dynamic and the environments are hazardous, HATs require complex considerations for interdependent work with AI. This qualitative interview study explores insights from 17 SAR professionals to investigate teamwork considerations that can benefit the formation of search-and-rescue human-AI teams (SAR-HATs). Participants’ concerns about time and affective needs suggest that SAR-HATs would benefit significantly from the explicit delineation of search and rescue tasks. HATs can expedite search tasks without forgoing safety by delegating roles that afford parallel completion of information acquisitions, search routines, and safety precautions. Additionally, search tasks can afflict human teammates with psychological trauma, but AI teammates benefit from their insusceptibility to trauma-related stressors. However, in rescue tasks, these capabilities have inverse benefits, wherein humans benefit from their capacity to feel emotion when interacting with the patients, and AI may struggle.

The increasing reliance on Large Language Models (LLMs) raises a crucial question: can these powerful AI systems be trusted to make ethical choices? This study presents an analysis of LLM ethical behavior, examining 25,200 queries across 24 different models, including both proprietary and open-source variants. We evaluate LLM responses to 70 ethical vignettes spanning six domains, employing a novel perturbation methodology to assess the robustness of their ethical decision-making under varying contexts and framing. Our findings reveal that while larger models generally exhibit higher consistency, particularly with Chat-style instructions, significant variations emerge when faced with contextual changes, stakeholder adjustments, and across different ethical domains. To explain these findings, we introduce a novel framework, survival-relevant pattern recognition, which argues that ethical behavior in both humans and AI arises from recognizing and responding to patterns associated with survival and social cohesion.

Consciousness remains one of the most elusive features to replicate in artificial agents. This paper proposes a novel framework for artificial consciousness based on four integrative pillars: (1) self-instantiation, a mechanism for continuous self-representation and identity; (2) temporal continuity, preserving an internal narrative through persistent memory; (3) disturbance-driven adaptation, an intrinsic feedback loop that triggers learning in response to surprises or anomalies; and (4) autonomous world-building, the ability to construct and simulate internal models of the world. We propose that current AI models, despite their sophistication, are fundamentally constrained by functionalist architectures and cannot fulfill these requirements through computational scaling alone. Unlike Integrated Information Theory or Global Workspace Theory, our approach emphasizes the necessity of autonomous world-building and genuine temporal flow. Our experiments demonstrate that combining these pillars can yield emergent conscious-like behaviors in AI systems, allowing them to exhibit self-awareness, resilience, and creative problem solving beyond the capabilities of conventional models. The significance of this framework lies in bridging theoretical foundations of consciousness with practical AI design, providing a roadmap for developing more adaptive and interpretable intelligent agents while raising important ethical considerations about the potential moral status of truly conscious artificial systems.

In this paper, we explore the use of Large Language Models (LLMs) to help students improve their information-seeking skills while encouraging the use of references to aid library literacy efforts. This study aims to expand the reach of library support by introducing an approach that leverages the capabilities of LLMs and well-structured prompts. Our approach begins with surveying the current changes students have faced in the last two years concerning their study habits and how they search for information. We subsequently propose a multi-step system prompt, referred as prompting architecture, for foundational and instructed LLMs. The proposed prompt architecture powers a web application named LibRef. We explore the adaptability of the prompting architecture to different information retrieval needs by refining search prompts and providing academic references. A field experiment is conducted using LibRef in academic settings. Our results suggest that the use of LibRef enhances students’ academic information-seeking experience. Our research underscores the potential of prompting architectures in procedural refinement of academic queries from students. We believe our findings can provide valuable insights on the current capabilities of LLMs for instructing students to provide more targeted prompts as well as incentivize the use of references.

This paper presents an analysis of the logical and fact-checking capabilities of Large Language Models (LLMs) in the context of syllogistic reasoning. The study evaluates both proprietary and open-source LLMs using a test suite of 256 syllogisms. Human participants provide a baseline for comparison. The results reveal that a few proprietary LLMs demonstrate superior performance in identifying valid syllogisms compared to open-source models and human participants. However, human participants exhibit unique fact-checking capabilities when dealing with ambiguous or nuanced information. The discussion explores the implications of these findings, highlighting the need for more sophisticated evaluation methods. This study contributes to the ongoing discourse on the logical and fact-checking capabilities of LLMs and provides insights into their current limitations and future development.

Self-quantifying technology enables users to evaluate their performance and define strategies for improvement. Workplace technology assists users in identifying activities and patterns that facilitate task completion. Software that measures workplace signals requires access to information from the user interface and peripherals. Current software solutions that track computer activity lack goal orientation and do not share raw data that could aid users and researchers in analyzing behavioral patterns at work. This paper presents Wellbot, an intelligent AI assistant capable of tracking workers' activities and providing personalized insights. The solution employs machine learning models to detect goal-oriented and recreational time. Based on users' goals and recent activities, Wellbot generates recommendations aided by Large Language Models. This work aims to enable tools that assist workers through an improved understanding of their context and goals.

This paper presents an exploration of the versatility of prompting architectures in large language models (LLMs), expanding the horizons of their application in learning and language interfaces. By leveraging the expansive capabilities of LLMs, this research probes the potential for creating structured prompts that can simultaneously support multiple use cases, namely paraphrasing, grammatical syntax guidance for introductory sentences, and conducting experiential conversations in a foreign language. In this study, we delve into the specifics of enabling such technology, including the design of the prompting architecture, the deployment process, and the intricacies of applying the same structure across diverse applications. An extensive field experiment incorporating interfaces powered by LLMs using this structured prompt has been conducted to evaluate the model's efficiency in real-world scenarios. Results from the field experiment highlight the promising adaptability of these prompting architectures, revealing remarkable efficiency across the multiple use cases explored. Furthermore, this research uncovers a new dimension of flexibility in the design and deployment of learning applications using LLMs, potentially revolutionizing language learning interfaces by establishing a one-size-fits-all solution. This paper aims to stimulate further research into refining and expanding the potential of LLMs, encouraging the exploration of how artificial intelligence can optimally benefit language learning and related applications.

Crowdsourcing platforms gather people from different backgrounds to work on completing microtasks. The workers on those platforms are presented with tasks defined by requesters from all over the world. The tasks are defined in multiple languages, with English being the predominant language. It is unclear how language barriers can affect workers whose primary language is not English while completing tasks. In this paper, we study their practices and the impact of AI-powered tools on the completion of microtasks. We conduct a pre-test test field experiment in which workers share their current experience with the platform and receive basic training in the use of AI-powered tools. After a week, workers provide feedback on their practices and experiences. We find that workers who complete tasks in their primary language report less confidence in completing English tasks, and in general, workers perceive the use of the tools as a language-learning mechanism. We aim that the understanding of language barriers in crowd markets can promote more inclusive work conditions.

The rapid advancement of Large Language Models (LLMs) has led to their widespread adoption in various academic and business applications. However, the reliability of these models remains a concern, particularly in situations where their outputs cannot be fully trusted. This paper presents an approach to identify potential errors in large LLM benchmarks by leveraging the consensus of frontier models. Our study focuses on the Massive Multitask Language Understanding (MMLU) benchmark, a popular dataset used to evaluate the performance of LLMs across a wide range of subjects. Our approach demonstrates the potential for using model consensus as a tool to detect benchmark errors and can lead to the creation of cleaner, more accurate datasets.

This paper introduces ReActIn, a framework designed to infuse human feedback into the intermediate prompting steps of large language models. The practicality and effectiveness of ReActIn are validated through experiments that apply four established prompting strategies, evaluated both with and without human feedback integration. The proposed architecture's performance is compared against traditional large language models across various tasks using four standard evaluation tests. Our findings reveal that the integration of human feedback has a direct impact on the reasoning, action prompting, and overall decision-making capabilities of the language models. This study underscores the potential of ReActIn to shape a future where sophisticated, context-aware AI systems, empowered by human feedback, can effectively navigate complex real-world scenarios.

In this paper, we propose SmartMonitor, a system that utilizes an edge hardware device to record, analyze and log user activity while not interfering with said activity. The analysis of the activity can help users identify the time they spend on different tasks and provide real-time feedback from a Large Language Model (LLM) to give better awareness of user activity. The SmartMonitor passes through the HDMI signal from the video card and analyzes the user’s activity on edge using two artificial intelligence models, logs user activity and sends the log for analysis to a Large Language Model for feedback. SmartMonitor enables non-intrusive self-quantifying technology that both records and analyzes user activity while protecting privacy and reducing the processing burden on the user’s device, which can serve as an excellent research framework for behavioral analysis in the workplace and a way to enhance user's work activity.

Large Language Models (LLMs), like ChatGPT, are fundamentally tools trained on vast data, reflecting diverse societal impressions. This paper aims to investigate LLMs' self-perceived bias concerning indigeneity when simulating scenarios of indigenous people performing various roles. Through generating and analyzing multiple scenarios, this work offers a unique perspective on how technology perceives and potentially amplifies societal biases related to indigeneity in social computing. The findings offer insights into the broader implications of indigeneity in critical computing.