Is this project an undergraduate, graduate, or faculty project?
Undergraduate
Project Type
group
Campus
Daytona Beach
Authors' Class Standing
Eden Tsouklaris, Senior Abriella Smith Brianna Broderick Carissa Aumack Victoria Cornaro
Lead Presenter's Name
Eden Tsouklaris
Lead Presenter's College
DB College of Arts and Sciences
Faculty Mentor Name
Barbara Chaparro
Abstract
With the exponential growth of Artificial Intelligence (AI), user interface (UI) designers have explored using AI to shorten design time. This study assessed the effectiveness of UIs designed with AI programs versus manual methods for an Unmanned Underwater Vehicle (UUV) control system. Participants were tasked with designing an interface that would allow submarine operators to monitor and coordinate three UUVs repairing a severed underwater communication cable at a depth of 2,000 meters. The scenario presented several operational challenges (zero visibility, sonar-only perception, data latency, and potential system degradation), requiring participants' designs to maintain spatial awareness and support remote repair tasks. Participants were assigned to either manual methods (on paper or an online wireframe tool) or AI (Claude or FigmaMake) and given 50 minutes to develop an interface that incorporated required elements (3D sonar visualizer, latency-adjusted vehicle positioning, acoustic link status indicators, and a tactical repair checklist). The interfaces’ effectiveness was analyzed by comparing it against the requirements outlined in human factors principles and military standards. Before and after designing their interface, participants were asked to rate their trust in AI. After completing their design, participants were also asked to complete questionnaires measuring their satisfaction with ease of completion, satisfaction with the time it took, and satisfaction with the support information they had when completing the task; rate how complete they feel their wireframe was; estimate extra time needed to complete or polish their wireframe; estimate their perceived workload; and provide final comments. Preliminary results show a higher average perceived workload among manual methods. We expected that AI methods would result in a more effective and higher fidelity interface.
Did this research project receive funding support (Spark, SURF, Research Abroad, Student Internal Grants, Collaborative, Climbing, or Ignite Grants) from the Office of Undergraduate Research?
No
Included in
Artificial Intelligence and Robotics Commons, Graphics and Human Computer Interfaces Commons, Systems Architecture Commons
How AI Influences the Design Process of Unmanned Underwater Vehicles’ (UUVs) 3D Sonar System
With the exponential growth of Artificial Intelligence (AI), user interface (UI) designers have explored using AI to shorten design time. This study assessed the effectiveness of UIs designed with AI programs versus manual methods for an Unmanned Underwater Vehicle (UUV) control system. Participants were tasked with designing an interface that would allow submarine operators to monitor and coordinate three UUVs repairing a severed underwater communication cable at a depth of 2,000 meters. The scenario presented several operational challenges (zero visibility, sonar-only perception, data latency, and potential system degradation), requiring participants' designs to maintain spatial awareness and support remote repair tasks. Participants were assigned to either manual methods (on paper or an online wireframe tool) or AI (Claude or FigmaMake) and given 50 minutes to develop an interface that incorporated required elements (3D sonar visualizer, latency-adjusted vehicle positioning, acoustic link status indicators, and a tactical repair checklist). The interfaces’ effectiveness was analyzed by comparing it against the requirements outlined in human factors principles and military standards. Before and after designing their interface, participants were asked to rate their trust in AI. After completing their design, participants were also asked to complete questionnaires measuring their satisfaction with ease of completion, satisfaction with the time it took, and satisfaction with the support information they had when completing the task; rate how complete they feel their wireframe was; estimate extra time needed to complete or polish their wireframe; estimate their perceived workload; and provide final comments. Preliminary results show a higher average perceived workload among manual methods. We expected that AI methods would result in a more effective and higher fidelity interface.