Objective
Our primary goal is to create a user-friendly AI assistant that offers reliable guidance to truckers for diagnosing and managing common mechanical problems, thereby enhancing their safety and efficiency on the road.
Scope
The Trucker AI app targets truck drivers dealing with standard mechanical issues, and while its advice complements professional mechanic diagnostics. Secondary enhancements may include the ability to analyze pictures of problems. Future enhancements may include preventive maintenance advice and integration with onboard diagnostic systems.
Human-Centered Design Approach
The Trucker AI app is designed with truckers’ needs as a focal point, ensuring its features and functionalities align with user requirements. By understanding truckers’ experiences and language, the app will provide relatable and effective support during mechanical troubleshooting. The app uses trucker profiles, derived from their common mechanical problems and preferred communication styles, to inform the design of its conversational model.
LUMA Exercises
Stakeholder Mapping: Key players including truckers, fleet managers, and maintenance staff are identified, clarifying their roles and interactions in the trucking ecosystem.
Truck Drivers (Primary): Truck drivers are the main users of the Trucker AI app. Their primary need is to diagnose and potentially fix mechanical problems on the road as quickly and efficiently as possible to minimize downtime.
Secondary Stakeholders
Independent Service Center: Mechanics and other maintenance personnel would use the information provided by the Trucker AI app to better understand the mechanical issues before the truck arrives at the repair facility, improving their preparedness and efficiency.
Contextual Inquiry: Interviews and job shadowing give insights into real-world challenges faced by truckers when diagnosing mechanical issues. This will be addressed through a cyclical series of workgroups with subject matter experts.
Interviews
Researchers will conduct interviews to gather more detailed information. Open-ended questions will enable drivers to share their experiences, difficulties, and expectations when dealing with mechanical issues. It’s also essential that subject matter experts are interviewed and included in reviewing and contributing to prototypes as they are developed.
Data Collection
The written records can offer insights into the truckers’ personal experiences and problem-solving strategies, as they typically include their firsthand observations, steps taken, and the results. Printed manuals are invaluable in understanding the technical information that is readily available to truckers, as well as their standard go-to reference material. Online resources indicate where existing information might be lacking or hard-to-understand, prompting truckers to seek additional advice or clarification on the internet.
Photographs or scans of these documents will be taken, ensuring the data they hold can be integrated into the overall data analysis. These resources will be valuable in enriching the information gathered from the observation and interview phases, offering more holistic insights into the truck drivers’ reality when facing mechanical issues.
User Journey Mapping: Illustrate the typical process truckers go through when they encounter a mechanical problem and seek help.
Truck Driver
Notice of Issue: The driver experiences an issue with the truck, triggering concern.
Interaction with App: The driver turns to the Trucker AI app and communicates the issue to the chatbot, seeking guidance.
Problem Diagnosed: The chatbot gathers details, makes a diagnosis, and offers immediate recommendations, providing some reassurance.
Resolution Process Initiated: Following the chatbot’s instructions, the driver initiates actions to safely navigate the situation.
Professional Repair: The driver reaches the service center where the problem is fixed, and the driver feels relief and satisfaction with the app’s help.
Independent Service Center
Informed of Incoming Repair: The admin learns of the incoming truck and its issue from the Fleet Manager, leading to preparation.
UX specific for Chatbots
Conversation Mapping for the Trucker Chatbot
Conversation mapping for the Trucker Chatbot develops a user-friendly dialogue flow. It guides users to describe their truck’s issue accurately, with the chatbot asking follow-up questions as necessary. The flow must account for various user inputs, including trucking terminologies and casual language, ensuring it caters to a broad range of truck drivers. It should also anticipate and swiftly rectify possible areas of confusion, creating a supportive user experience.
Sentiment Analysis for the Trucker Chatbot
Sentiment analysis allows the Trucker Chatbot to gauge the user’s emotional state during interactions, adjusting its responses to provide reassurance or clarity as needed. It serves as a tool to measure user satisfaction over time, helping to identify and address areas of user frustration or confusion. With sentiment analysis, developers can continually refine the chatbot’s responses and interaction flow, improving overall user experience and driving the chatbot’s continuous improvement.
