Design Tools Lab

Barnard College Computer Science
Columbia University

The Design Tools Lab (DTL) is a research group dedicated to supporting people realizing their creative potential.

We develop design software, toolkits, and learning experiences that enable people to build and share projects representing their ideas, identities, and values. We are especially interested in creative computing design tools that support beginners developing computational fluency, with prior work centered on machine learning, visual arts, electronics, and documentation.

Publications

VL/HCC 2025

paper video full arxiv paper

Keyframer: A Design Probe for Exploring LLM Assistance in 2D Animation Design

Tiffany Tseng, Ruijia Cheng, Andrew McNutt, Jeffrey Nichols

We present Keyframer, a design tool for animating static images (SVGs) with natural language. Informed by interviews with professional animation designers and engineers, Keyframer supports exploration and refinement of animations through the combination of prompting and direct editing of generated output.

machine-learningvisual-arts

arXiv preprint 2025

paper

Beyond Text: Probing K-12 Educators' Perspectives and Ideas for Learning Opportunities Leveraging Multimodal Large Language Models

Tiffany Tseng, Katelyn Lam, Tiffany Lin Fu, Alekhya Maram

We investigated K-12 educator perspectives on how multimodal large language models (MLLMs) might shape the future of learning experiences and what practical needs should be considered for successful implementation in educational contexts.

machine-learning

C&C 2025

paper

Crafting a Personal Journaling Practice: Negotiating Ecosystems of Materials, Personal Context, and Community in Analog Journaling

Katherine Lin, Juna Kawai-Yue, Adira Sklar, Lucy Hecht, Sarah Sterman, Tiffany Tseng

We report on our identification of the journaling ecosystem in which journaling practices are shaped by materials, personal context, and communities, sharing how this ecosystem plays a role in the practices and identities of journalers as they customize their routines to best suit their personal goals.

documentation

TOCE 2024

paper video

Co-ML: Collaborative machine learning model building for developing dataset design practices

Tiffany Tseng, Matt J Davidson, Luis Morales-Navarro, Jennifer King Chen, Victoria Delaney, Mark Leibowitz, Jazbo Beason, R Benjamin Shapiro

We outline a set of four data design practices (DDPs) for designing inclusive ML models and share how we designed a tablet-based application called Co-ML to foster learning of DDPs through a collaborative ML model building experience. We deployed Co-ML in a 2-week-long educational AIML Summer Camp, where youth ages 13–18 worked in groups to build custom ML-powered mobile applications.

machine-learning

IDC 2023 honorable mention

paper video

Collaborative Machine Learning Model Building with Families Using Co-ML

Tiffany Tseng, Jennifer King Chen, Mona Abdelrahman, Mary Beth Kery, Fred Hohman, Adriana Hilliard, R Benjamin Shapiro

We created Co-ML – a tablet-based app for learners to collaboratively build ML image classifiers through an end-to-end, iterative model-building process. In this paper, we illustrate the feasibility and potential richness of collaborative modeling by presenting an in-depth case study of a family using Co-ML in a facilitated introductory ML activity at home.

machine-learning

DIS 2021

paper video presentation

Circuit Assemblies: Electronic Modules for Interactive 3D-Prints

Tiffany Tseng, Yoshihiro Kawahara

This pictorial presents Circuit Assemblies, a design system for beginners to create 3D-printed interactive objects with embedded electronics. Circuit Assemblies are modules used to create objects that light up, move, or spin using basic electronic components like LEDs, batteries, and motors. To support beginners incorporating Circuit Assemblies into 3D designs, a set of virtual components were added to Tinkercad, a popular browser-based 3D CAD application with over 10 million users.

electronics

IDC 2021 best paper

paper video presentation

PlushPal: Storytelling with interactive plush toys and machine learning

Tiffany Tseng, Yumiko Murai, Natalie Freed, Deanna Gelosi, Tung D Ta, Yoshihiro Kawahara

This paper presents PlushPal, a web-based design tool for children to make plush toys interactive with machine learning (ML). With PlushPal, children attach micro:bit hardware to stuffed animals, design custom gestures for their toy, and build gesture-recognition ML models to trigger their own sounds.

machine-learningelectronics

DIS 2016

paper video

Spin: Examining the role of engagement, integration, and modularity in supporting youth creating documentation

Tiffany Tseng, Mitchel Resnick

In this paper, we contribute an evaluation of Spin, a system for youth to capture and share animations of physical design projects. We describe how educators and students used Spin in 16 different K-12 makerspaces over 6 months, ranging from 2-hour-long workshops to extended 8-week engagements. We outline the role of engagement, integration, and modularity for making documentation practice relevant and meaningful.

documentation

Fablearn 2015

paper demo

Making Make-throughs: Supporting young makers sharing design process.

Tiffany Tseng

This paper introduces the concept of make-throughs, a documentation format in which Makers share their design process throughout the development of a design project. Makethroughs capture the rich, iterative nature of a design process in which a project is constantly revised and refined over time.

documentation

DIS 2014

paper

Product versus process: Representing and appropriating DIY projects online

Tiffany Tseng, Mitchel Resnick

Through interviews and surveys with authors and readers of Instructables, we describe differences found in the practices of these two types of users in creating and applying design documentation. Based on the results, we identify design opportunities for members of the HCI community developing tools to better support people sharing creative work online.

documentation