Tootfinder

Embodied Natural Language Interaction (NLI): Speech Input Patterns in Immersive Analytics
Hyemi Song, Matthew Johnson, Kirsten Whitley, Eric Krokos, Amitabh Varshney
https://arxiv.org/abs/2510.12156

Embodied Natural Language Interaction (NLI): Speech Input Patterns in Immersive Analytics
Embodiment shapes how users verbally express intent when interacting with data through speech interfaces in immersive analytics. Despite growing interest in Natural Language Interaction (NLI) for visual analytics in immersive environments, users' speech patterns and their use of embodiment cues in speech remain underexplored. Understanding their interplay is crucial to bridging the gap between users' intent and an immersive analytic system. To address this, we report the results from 15 partici…

New to warm @… , you can use 'delta' to do things based on the duration of events.

Observing Without Doing: Pseudo-Apprenticeship Patterns in Student LLM Use
Jade Hak, Nathaniel Lam Johnson, Matin Amoozadeh, Amin Alipour, Souti Chattopadhyay
https://arxiv.org/abs/2510.04986

Observing Without Doing: Pseudo-Apprenticeship Patterns in Student LLM Use
Large Language Models (LLMs) such as ChatGPT have quickly become part of student programmers' toolkits, whether allowed by instructors or not. This paper examines how introductory programming (CS1) students integrate LLMs into their problem-solving processes. We conducted a mixed-methods study with 14 undergraduates completing three programming tasks while thinking aloud and permitted to access any resources they choose. The tasks varied in open-endedness and familiarity to the participants and…

"AI in the guise of Machine Learning, Deep Learning, GenerativeAI (GenAI), or Large Language Models (LLMs)... can be very useful in certain application areas such as recognising or generating patterns in large data sets. However, their key drawback is that any correctness arguments will be inherently probabilistic as they are usually based on unknown data distributions and are therefore susceptible to errors (sometimes termed “hallucinations”). "

Temporal-Aware Iterative Speech Model for Dementia Detection
Chukwuemeka Ugwu, Oluwafemi Oyeleke
https://arxiv.org/abs/2510.00030 https://arxiv.org/pdf/251…

Temporal-Aware Iterative Speech Model for Dementia Detection
Deep learning systems often struggle with processing long sequences, where computational complexity can become a bottleneck. Current methods for automated dementia detection using speech frequently rely on static, time-agnostic features or aggregated linguistic content, lacking the flexibility to model the subtle, progressive deterioration inherent in speech production. These approaches often miss the dynamic temporal patterns that are critical early indicators of cognitive decline. In this pap…

One of the rhythms BC Manjunath, Matt Davies and me will be exploring on the 8th November in Sheffield UK. It's going to be a lot of fun

Help or Hype? Students' Engagement and Perception of Using AI to Solve Physics Problems
Qurat-ul-Ann Mirza, N. Sanjay Rebello
https://arxiv.org/abs/2509.25642 https://

Help or Hype? Students' Engagement and Perception of Using AI to Solve Physics Problems
With the rise of large language models such as ChatGPT, interest has grown in understanding how these tools influence learning in STEM education, including physics. This study explores how students use ChatGPT during a physics problem-solving task embedded in a formal assessment. We analyzed patterns of AI usage and their relationship to student performance. Findings indicate that students who engaged with ChatGPT generally performed better than those who did not. Particularly, students who pro…

The Siren Song of LLMs: How Users Perceive and Respond to Dark Patterns in Large Language Models
Yike Shi (Diane), Qing Xiao (Diane), Qing (Diane), Hu, Hong Shen, Hua Shen
https://arxiv.org/abs/2509.10830

The Siren Song of LLMs: How Users Perceive and Respond to Dark Patterns in Large Language Models
Large language models can influence users through conversation, creating new forms of dark patterns that differ from traditional UX dark patterns. We define LLM dark patterns as manipulative or deceptive behaviors enacted in dialogue. Drawing on prior work and AI incident reports, we outline a diverse set of categories with real-world examples. Using them, we conducted a scenario-based study where participants (N=34) compared manipulative and neutral LLM responses. Our results reveal that recog…

Behind India's ChatGPT Conversations: A Retrospective Analysis of 238 Unedited User Prompts
Kalyani Khona
https://arxiv.org/abs/2509.13337 https://arxi…

Behind India's ChatGPT Conversations: A Retrospective Analysis of 238 Unedited User Prompts
Understanding how users authentically interact with Large Language Models (LLMs) remains a significant challenge in human-computer interaction research. Most existing studies rely on self-reported usage patterns or controlled experimental conditions, potentially missing genuine behavioral adaptations. This study presents a behavioral analysis of the use of English-speaking urban professional ChatGPT in India based on 238 authentic, unedited user prompts from 40 participants in 15+ Indian cities…

The Rhythm In Anything: Audio-Prompted Drums Generation with Masked Language Modeling
Patrick O'Reilly, Julia Barnett, Hugo Flores Garc\'ia, Annie Chu, Nathan Pruyne, Prem Seetharaman, Bryan Pardo
https://arxiv.org/abs/2509.15625

The Rhythm In Anything: Audio-Prompted Drums Generation with Masked Language Modeling
Musicians and nonmusicians alike use rhythmic sound gestures, such as tapping and beatboxing, to express drum patterns. While these gestures effectively communicate musical ideas, realizing these ideas as fully-produced drum recordings can be time-consuming, potentially disrupting many creative workflows. To bridge this gap, we present TRIA (The Rhythm In Anything), a masked transformer model for mapping rhythmic sound gestures to high-fidelity drum recordings. Given an audio prompt of the desi…