Steve Rand
Steve Rand
AI-Assisted-Open-Ended-Question-Coding-Main-2.jpg

Konovo: AI Assisted Question Coding

Konovo: AI Assisted Question Coding

I led the product strategy, UX direction, and research efforts for the initiative while managing and collaborating closely with Product Designer Jie Liu. Together, we explored multiple approaches to integrating AI-generated categorization into existing research workflows, balancing automation with transparency and user control. Through several rounds of concept development, stakeholder reviews, and usability testing, we evaluated different information architectures, interaction models, and editing workflows before converging on a scalable solution that fit naturally within researchers’ existing reporting processes.

Konovo: AI-Assisted Question Coding

TURNING Manual Analysis into QUICK AI-Assisted Research

Role: Director of Product Design
Timeline: December 2024 – May 2025
Designed With: Jie Liu (Product Designer)
Teams: Product, Engineering, Product Management, Research Operations
Responsibilities: Product Strategy, UX Design Direction, User Research, Usability Testing, Interaction Design, Stakeholder Alignment, Design Leadership
Impact: Reduced the time and effort required to analyze open-ended survey responses by introducing AI-assisted categorization workflows that accelerated qualitative research while preserving researcher control and customization.

Overview

Open-ended survey questions often produce deep qualitative insights, but analyzing hundreds of open ended text responses has traditionally been an expensive and time-consuming manual process. Researchers must review every response, group similar themes, create categories, and continually refine those categories as new responses arrive. While AI presented an opportunity to automate much of this work, researchers still need full transparency and control over the final output.

As Director of Product Design, I led the UX strategy and design direction for an AI-assisted Open-Ended Question Coding experience in collaboration with Product Designer Jie Liu, whom I managed throughout the project. Together we worked closely with Product, Engineering, and research stakeholders to design an AI-powered workflow that dramatically accelerated the team’s analysis while preserving researcher confidence through intuitive review, editing, and customization tools. The result established a practical framework for incorporating AI into a complex professional workflow without removing users from the decision-making process.

The Problem

Designing this experience required much more than adding AI-generated categories to survey results. Researchers needed to see how the AI reached its conclusions, quickly identify responses that required adjustment, and efficiently refine categories without starting over. The workflow also needed to support surveys in multiple states of progress, accommodate both newly generated and previously categorized data, and integrate naturally into an existing reporting platform used by experienced researchers.

The editing experience presented an equally complex design challenge. Users needed the flexibility to adjust individual responses while also performing bulk updates across a multitude of records. We explored multiple interaction models before arriving at a solution that balanced speed, discoverability, and precision. At the same time, we modernized the interface with improved accessibility, WCAG-compliant color contrast, clearer hover and interaction states, and scalable visual patterns.

Research & Design Process

The project was highly iterative, involving multiple rounds of concept exploration, stakeholder reviews, and usability testing. We evaluated several information architectures for presenting AI-generated coding results, experimented with different dashboard and editing layouts, and tested how researchers interacted with AI-generated recommendations. These sessions validated core workflows while uncovering opportunities to simplify navigation, improve editing efficiency, and strengthen user trust in the automated categorization process.

The final experience combines AI-assisted categorization with intuitive human review. Researchers can generate categories automatically, inspect AI-generated results, edit individual responses or entire groups through dedicated single and bulk editing modes, recategorize data as additional responses arrive, and organize results using sorting and filtering tools. Throughout the design process, we intentionally positioned AI as an assistant rather than a replacement for researcher expertise, allowing users to quickly reach an accurate starting point before applying their own judgment where needed.

Impact

The finished experience transformed one of the most labor-intensive steps in qualitative market research into a streamlined AI-assisted workflow. By dramatically reducing the amount of manual categorization required while preserving complete user control, researchers could spend less time organizing data and more time interpreting insights.

Beyond improving productivity, the project established design patterns for future AI-assisted workflows across the platform. It demonstrated how thoughtful interaction design, usability testing, accessibility improvements, and human-centered AI principles can work together to create experiences that increase efficiency without sacrificing transparency, confidence, or user control.