<aside> <img src="attachment:14201933-b5ef-4f5e-bbe3-8a2c6b68ec78:np_marker_7814050_CA665E.png" alt="attachment:14201933-b5ef-4f5e-bbe3-8a2c6b68ec78:np_marker_7814050_CA665E.png" width="40px" />

Example Abstract Annotation from CHI 2021

</aside>

image.png

(In groups of 5 people:)

Find and highlight the following in those abstracts below

  1. Context/Territory: Why did they do it?
  2. Problem/Niche/Gap: What problem did they address?
  3. Relevance: Why should we care?
  4. Solution/Method: What did they do? How?
  5. Key findings/Results: What did they find out?
  6. Implications and contributions: What does it all mean? Is there a call to action?

What CaRS niche are they in?

What are they contributing to HCI?

1. Code Shaping: Iterative Code Editing with Free-form AI-Interpreted Sketching

Ryan Yen, Jian Zhao, and Daniel Vogel. 2025. Code Shaping: Iterative Code Editing with Free-form AI-Interpreted Sketching. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (CHI '25). Association for Computing Machinery, New York, NY, USA, Article 872, 1–17. https://doi.org/10.1145/3706598.3713822

We introduce the concept of code shaping, an interaction paradigm for editing code using free-form sketch annotations directly on top of the code and console output. To evaluate this concept, we conducted a three-stage design study with 18 different programmers to investigate how sketches can communicate intended code edits to an AI model for interpretation and execution. The results show how different sketches are used, the strategies programmers employ during iterative interactions with AI interpretations, and interaction design principles that support the reconciliation between the code editor and sketches. Finally, we demonstrate the practical application of the code shaping concept with two use case scenarios, illustrating design implications from the study.

2. Creative Writers’ Attitudes on Writing as Training Data for Large Language Models

Katy Ilonka Gero, Meera Desai, Carly Schnitzler, Nayun Eom, Jack Cushman, and Elena L. Glassman. 2025. Creative Writers' Attitudes on Writing as Training Data for Large Language Models. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (CHI '25). Association for Computing Machinery, New York, NY, USA, Article 1118, 1–16. https://doi.org/10.1145/3706598.3713287