Spring 2026
(Counter) Data Visualizations for Storytelling
Led by Priya Dhawka and Jennifer Turns
Data visualizations are often treated as neutral and objective in popular discourse and are increasingly being used as visual evidence to support political claims in the spread of problematic information. This DRG seeks to explore the creation of visualizations that counter misleading political claims and humanize underrepresented populations by leveraging existing visualization tools (Tableau, D3.js, Observable, Python libraries like Altair and Vega), visualization best practices, and experimenting with new technologies.
The short term goal of this DRG is to address the following research question and to prepare a manuscript for publication at an academic conference: How can critical, feminist traditions of resistance and refusal inform current or help imagine new visualization design practices around the creation of countervisualizations?
What you’ll do: read research papers on countervisualizations, anthropographics (human-shaped visualizations), visualization best practices, and critical, feminist epistemologies for visualization design, experiment with existing tools to design a workflow for creating countervisualizations from open federal and academic datasets.
Commitment: weekly 1-2 hour meetings, with 4 to 6 hours of commitment every week involving reading and visualization design and critique.
Credits: 2-3
Ideal for: undergraduate students with coursework background in data visualization design (HCDE 411) or graduate students with prior professional data visualization experience and/or interest in speculative design.
Participants need to be:
- Willing to read and critically engage with published scholarship
- Open to co-constructing the experience by contributing to the DRG choices of readings, visualizations, and datasets to work with.
- Aware of their own positionality and able/willing to monitor how their positionalities will influence the work.
- Are able to see visualizations from a socio-technical perspective that accounts for both the features of the visualization and the work of the designer designing the visualization.
This DRG is full for spring and no longer accepting applicants.
Questions: email Priya Dhawka: pdhawka@uw.edu
Spring 2026
Designing for AI-Enhanced Self-Management
Instructors: Joice Tang, Jennifer Turns
This DRG explores autobiographical design as a method of self-understanding, using Research through Design (RtD) and AI-assisted rapid prototyping (vibecoding) as its core practices. Participants will design, build, and live with personalized AI-enhanced self-management tools across three iterative design cycles over a 10-week quarter, treating both the design process and the experience of living with each design as data about themselves.
The group draws on Prof. Jennifer Turns' ongoing interests on autobiographical design in higher education, which reframes the central question from "how might AI support learning?" to "how might I support myself in doing x?" – centering student agency rather than prescriptive guidance. Vibecoding (AI-assisted coding requiring no prior programming expertise) makes rapid prototyping accessible and keeps the focus on design thinking and self-reflection rather than technical execution. Participants will document their process with intentionality, share emerging insights in weekly sessions, and produce a final portfolio, contributing to participants’ own self-understanding and to a growing body of knowledge about how RtD can surface insights about AI's role in personal productivity and academic life.
This DRG sits at the intersection of RtD, personal informatics, and AI-enhanced self-management, with potential to generate publishable insights about how participants developed agentic, personalized relationships with AI tools.
Enrollment information
- Meeting time: Meeting time will be determined based on student availability.
- Credits: 1 credit – ~3 hrs/week commitment
- Who should apply: This DRG will be available by invitation only.
- This DRG does meet HCDE's PhD research requirement.