ScotBot

Your personal transit buddy at Carnegie Mellon University.

Conversational UI

Mobile App

Client: Graduate Student Assembly (GSA) at Carnegie Mellon University (CMU)
Duration: 2 Months | Oct.2018 - Dec.2018
My Contribution: UX Research, UI/UX Design, Motion Design

Problem Space

How to provide CMU students an official, friendly and comprehensive way to learn about all their transportation options, optimization, and also how to use the service?

Problem Background: Graduate Student Assembly (GSA) at Carnegie Mellon University (CMU) is expecting students to have a complete mental model of all transportation options available to them through the university, including basic information for use (e.g., location, routes, rules/requirements), facilitating the ability to contrast and compare options and efficiently make the best decisions for their needs especially for CMU transportation services (school shuttle and escort).

Solution

A native mobile chatbot App called ScotBot featured with:    

  • Friendly transit buddy that provides delightful chatting experience
  • All transportation information including stops, routes and instructions
  • Recommended daily transportation options based on personal settings
  • Optimized commuting methods for real-time request

We received great feedback from stakeholders and users about how they can envision people or themselves could be using the help from ScotBot.

01

Generative Research

To understand students' needs, behavior and expectations when exploring and using the transportation services around campus, we conducted contextual inquiries to learn user's habits and mental model when inquiring for information, send survey to research on channels of information delivery and also think-aloud to discover breakdowns.

With contextual inquiries, we sought to understand how riders used and interacted with the shuttle and escort systems, identifying well-functioning flows as well as breakdowns. We conducted four contextual inquiry interviews across the primary transportation modes used by CMU students--walking, biking, PAT, escort and shuttles. In each instance, we accompanied our participants from the beginning of their journeys until they arrived at the last major junction by their homes.

We used affinity diagram (see it here) to categorize our interview data and to understand how information are spread throughout campus in different commuters' perspectives.

Main Insights:

  • Transportation information are all over the place, the interfaces that deliver the information are also hard to use.
  • CMU’s education around transportation (what are the options, how to use them) options is inadequate.
  • Many transportation users learn how to ride through friends and social connections (which can be inaccurate).
  • Student wants to have experiential ways to learn about the information.

By distributing surveys we expected to learn about the demographics of different kinds of transportation riders, how they learn about transportation information and their priority when choosing the right transit method.

39%

Learned about transportation information
through world of mouth (social network).

32%

Did not go to all orientations whether
intentionally and inevitably.

62%

Use Google Map and Transit as their main
tool of tracking traffic.

74%

Chose PAT buses as their main
transportation methods.

Main Insights:

  • While orientation is the official way that CMU students find out about CMU transit options, it fails to cover main demographics.
  • PAT is the most preferred transportation option. Of CMU-provided transit options, students tend to prefer escort services to shuttle.
  • World of mouth is currently the most popular way of spreading transportation information which is potentially problematic.
  • Door-to-door Travel Time, Frequency of Service, Safety, and Cost are crucial factors for students when making decisions about transportation.
  • Further research is needed on pain points of learning information.

In order to understand students' behavior, pain points and breakdowns when they are looking for transportation information, we conducted Think-Aloud sessions with several CMU students.

Task

Find assigned transportation information using the tool that the user is familiar with.

Pain points we identified:

  • Broken visual elements, weak information hierarchy, and confusing descriptions on the CMU websites lead to user misunderstanding and frustration.
  • Some of the information hub is hard to locate, also information like PAT bus, bike sharing program and other transportations are all over the places.
  • Users were eager to give up on challenging tasks that specified a mode of transit and default to their primary mode of transportation to complete the tasks.

What we learned from generative research...

Transportation information around CMU through the official channels is:

Poorly Spread

Poorly Designed

De-centralized

02

Ideation

Based on our generative research findings, the main goal of the next phase is to generate potential solutions to address the current problems and also to validate those needs and how user would want the solution to look and feel like, which means what kind of service are they expecting.

Based on our generative research findings, we determined 12 potential needs from the users and brainstormed over 30 ideas according to the needs. After voting, we created storyboards that best address the needs:

Need 1: Identify optimal transit method

Need 2: Learn about all transportation methods (through an experiential way)

Need 3: Display and Compare of all available transportation options

To validate whether the needs we discovered were actual needs and also to evaluate the potential solutions, we conducted two rounds, 10 people Speed Dating sessions in total, validated most needs and gained new insights from the users:

Ideation Round 2

After we learned about their needs and expectation, we created more scenarios with solutions and conducted another round of Speed Dating to see which idea fits the best.

Among all the ideas that we generated to address this problem, one stands out as a friendly and smart solution, which is a chatbot who has all information and can optimize for the user.

03

Prototyping & Testing

To build a chatbot prototype, we started an account Facebook Messenger to simulate the process of using the chatbot. To begin with, we created slots, intents and prompts for the conversational system to build the decision tree. After finishing the prototype, we conducted Think-Aloud sessions to test the usability of the chatbot.

To build a conversational system, it is essential to analyze what are the:

  • Intents: what users want to do with the conversational system
  • Slots: what information does the system need to know to satisfy the user's need
  • Prompts: what question the system need to ask in order to get the slots (information being needed)

See all the elements we built for the ChatBot here.

Based on what we explored from the last step, we built the decision tree for the conversational system:

In order to test on our prototype, we used Facebook Messenger to prototype our initial chatbot design.

Based on the feedback from our user testing, we iterated on:

  • Tone: Make the user feel more welcomed and friendly
  • Logic of the decision tree: fixed some bugs that most users encountered
  • Redesigned the option cards: some of the user expressed confusion when seeing option cards

04

Final Design

The solution was appreciated by stakeholders who can foresee strong use cases of our product. For final iteration on my own, except for UI refinement I also envisioned a more futuristic design of the chatbot that featured with real-time tracking, machine-learning key word recognition, voice control etc.

  

Onboarding

  • Friendly tone helps bonding with user
  • Clear instructions with color coded path

  

Understanding Options

  • Clear path to find information
  • Colored cards representing transportation options

  

Optimization

  • Optimization based on user
  • Privacy concern addressed
  • Recommendation based on priority

  

Real-time Tracking

  • Real-time inquiries and optimization
  • Voice input and recognition

Thank you for reading!

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Interested in working with me?

pzq0127@gmail.com

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