Evidence map›Paper›PMID 42023015›Full record

ArticleProceedings of the ACM on human-computer interaction2025

"

Szeyi Chan, Jiachen Li, Bingsheng Yao, Amama Mahmood, Chien-Ming Huang, Holly Jimison, Elizabeth D Mynatt, Dakuo Wang

Abstract read
In one paragraph

Article in Proceedings of the ACM on human-computer interaction, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. DIS. Designing Interactive Systems (Conference) · 2025
    Article
  2. Understanding the Role of Large Language Models in Personalizing and Scaffolding Strategies to Combat Academic Procrastination.Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference · 2024
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Szeyi ChanNortheastern University, USA.
Jiachen LiNortheastern University, USA.
Bingsheng YaoNortheastern University, USA.
Amama MahmoodJohns Hopkins University, USA.
Chien-Ming HuangJohns Hopkins University, USA.
Holly JimisonNortheastern University, USA.
Elizabeth D MynattNortheastern University, USA.
Dakuo WangNortheastern University, USA.

Funding

SCH: Improving Early Prediction and Decision-Making for Sepsis with Human-AI CollaborationR01AI188576 · NIAID · OHIO STATE UNIVERSITY · PI Jeffrey M Caterino, Lace M. Padilla · 2024 to 2026
$895k
NIAID NIH HHS R01 AI188576
6 · The paper itself

Abstract

The rapid advancement of Large Language Models (LLMs) has created numerous potentials for integration with conversational assistants (CAs) assisting people in their daily tasks, particularly due to their extensive flexibility. However, users' real-world experiences interacting with these assistants remain unexplored. In this research, we chose cooking, a complex daily task, as a scenario to explore people's successful and unsatisfactory experiences while receiving assistance from an LLM-based CA,

Indexed as

exploratory studylarge language model-based conversational assistantuser study

Identifiers

PMID42023015
PMCPMC13099075

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.