arXiv subjects

Denise Wilson

Publications and source records attributed to Denise Wilson.

3 recordsLinked to original sources

arXiv2026

Designing Needs- and Attention-Aware AI Learning Tools for Engineering Education: Insights from Psychological Outcomes

Artificial Intelligence (AI) is transforming higher education, but its benefits can vary depending on where, how, and how often it supports learning. While prior research emphasizes cognitive and academic outcomes, this study examines how AI chatbots support the psychological needs and motivational states of engineering students. A survey of college engineering students (n = 206) examined perceived effects of AI chatbots on autonomy, relatedness, and relief from competence frustration. Structural equation modeling with latent interaction effects examined how baseline autonomy, competence frustration, relatedness, and personal agency contributed to perceived AI outcomes. Results indicate that students perceived that AI provided the greatest benefits as relief from competence frustration, smaller benefits for autonomy, and the weakest benefits for relatedness. Baseline motivational states mattered more than demographic factors, and inattention moderated how baseline competence frustration and autonomy related to perceived AI-related benefits. These results offer insights into formulating design principles for engineering-specific AI-based tools.

cs.HC↗

arXiv2026

Before Smelling the Video: A Two-Stage Pipeline for Interpretable Video-to-Scent Plans

Olfactory cues can enhance immersion in interactive media, yet smell remains rare because it is difficult to author and synchronize with dynamic video. Prior olfactory interfaces rely on designer triggers and fixed event-to-odor mappings that do not scale to unconstrained content. This work examines whether semantic planning for smell is intelligible to people before physical scent delivery. We present a video-to-scent planning pipeline that separates visual semantic extraction using a vision-language model from semantic-to-olfactory inference using a large language model. Two survey studies compare system-generated scent plans with over-inclusive and naive baselines. Results show consistent preference for plans that prioritize perceptually salient cues and align scent changes with visible actions, supporting semantic planning as a foundation for future olfactory media systems.

cs.HC↗

arXiv2023

Thriving Innovation Ecosystems: Synergy Among Stakeholders, Tools, and People

An innovation ecosystem is a multi-stakeholder environment, where different stakeholders interact to solve complex socio-technical challenges. We explored how stakeholders use digital tools, human resources, and their combination to gather information and make decisions in innovation ecosystems. To comprehensively understand stakeholders' motivations, information needs and practices, we conducted a three-part interview study across five stakeholder groups (N=13) using an interactive digital dashboard. We found that stakeholders were primarily motivated to participate in innovation ecosystems by the potential social impact of their contributions. We also found that stakeholders used digital tools to seek "high-level" information to scaffold initial decision-making efforts but ultimately relied on contextual information provided by human networks to enact final decisions. Therefore, people, not digital tools, appear to be the key source of information in these ecosystems. Guided by our findings, we explored how technology might nevertheless enhance stakeholders' decision-making efforts and enable robust and equitable innovation ecosystems.

cs.CY↗