Demo Data from SIGIR 2026 / reviewers in the wild / expert
Katherine Ziska
dblp:367/7110
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2024
0009-0002-9198-9936ORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Human-computer interaction and pervasive computing
1 paper |
Learning and educational technologies · 50% Human-AI interaction · 50% | |
| Artificial intelligence
1 paper |
Language models and text generation · 100% |
Topics — the 1 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Human-AI interaction › human-centered AI
child-AI interaction |
0.8 | 1 | 2024 | Mathemyths: Leveraging Large Language Models to Teach Mathematical Language through Child-AI Co-Creative Storytelling · CHI 2024 |
Methods — techniques the papers use, named apart from their topics
user study · 1.5prompt engineering · 1.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Mathemyths: Leveraging Large Language Models to Teach Mathematical Language through Child-AI Co-Creative StorytellingabstractMathematical language is a cornerstone of a child’s mathematical development, and children can effectively acquire this language through storytelling with a knowledgeable and engaging partner. In this study, we leverage the recent advances in large language models to conduct free-form, creative conversations with children. Consequently, we developed Mathemyths, a joint storytelling agent that takes turns co-creating stories with children while integrating mathematical terms into the evolving narrative. This paper details our development process, illustrating how prompt-engineering can optimize LLMs for educational contexts. Through a user study involving 35 children aged 4-8 years, our results suggest that when children interacted with Mathemyths, their learning of mathematical language was comparable to those who co-created stories with a human partner. However, we observed differences in how children engaged with co-creation partners of different natures. Overall, we believe that LLM applications, like Mathemyths, offer children a unique conversational experience pertaining to focused learning objectives. Chao Zhang 0082, Xuechen Liu 0003, Katherine Ziska, Chi-Lin Yu |
CHI | 3 |