<aside> 🔮 This project is part of Deshonesty — my design MFA thesis on documentation and reflection. Visit the interactive thesis site to see this work in greater detail!

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DARB is always in a good mood.

A chatbot writing coach with literary flair.

DARB — the Designer’s Assisted Reflection Bot — is a digital personality created to make reflective writing more approachable for instructors and students in design studio environments. Emerging from the findings from my Documentation for Designers qualitative research, DARB aims to close the gap between hands-on making and critical reflection by offering a structure for students to access what they think, feel, and notice through words — all from within the stream of creative practice itself. DARB helps designers explore the deeper, more self-reflective dimensions of the design process through reflective writing.

You can even play with DARB yourself below!

my roles

• preliminary research
• conceptual development and prototyping
• programming
• user testing and research

awards

Savageau Award in Design — UC Davis Department of Design 2022

DARB’s goals

• Bring writing into the design studio as a modular, off-the-shelf tool to help students reflect on a particular assignment, on their experience in an entire course, or on other key issues in design
• Break down some of the emotional barriers to self-exploration by allowing participants to benefit from the emotional support of an encouraging character while also being able to reflect in private
• Motivate designers to write by leveraging humor, narrative, and randomized text strings — rewarding participants who take the time to connect to the character and come back for a second visit
• Encourage reflection across scales by helping designers focus their thoughts inward or outward (on process OR practice — a key distinction between scales of reflection that I identified in my qualitative research on my Documentation for Designers course)

Programming

As a lo-fi prototype, DARB is programmed in Twine — an open-source interactive fiction-writing program. Future versions of DARB may be built in software that offers enhanced chatbot functionalities, but this first version leverages Twine’s minimalist, text-based, narrative-first sensibility.

Image of an individual “passage” in Twine (a screen in the DARB program) — shown here in the code are multiple arrays of possible dialogue options, each randomized.

Image of the “DARBitecture” in Twine — each “screen” is shown as a square, and the arrows indicate different possible user paths between them.

User testing + findings

DARB 1000, the initial lo-fi prototype, was evaluated through a series of user tests and interviews. Early feedback suggests that the concept is successful in motivating designers to write and in eliciting insights that might not have otherwise emerged. These findings indicate that the DARB framework might be adapted to a variety of reflective ends in the future. (More about my methodology and findings are available in my thesis site here.)

Key questions for user-testing

1. Concept: are designers open to letting a chatbot facilitate reflective writing sessions? What are users’ experiences with the program like?
2. Character: what about DARB’s character enhances or impedes the writing or reflection process?

Methods

I recruited a small number of participants via a single posting on a personal social media profile. Participants did not know in advance that they would be interacting with a chatbot — they only knew the project had something to do with reflection. I asked each participant to test DARB independently, then to complete a semi-structured interview with me about their experience.

Overview of preliminary findings

The response to DARB’s character design was unanimously positive. DARB’s overall friendliness and warmth seemed to help many participants feel comfortable in their reflections. An overwhelming majority of participants spent more time than necessary in getting to know DARB through the optional trivia series and exploring different avenues of conversation. A number of participants also chose to generate more than one writing prompt.