Tootfinder

Amazon, Microsoft, Google, Code.org, IBM, and other companies pledged new commitments for AI in education as part of a White House event hosted by Melania Trump (Ashley Gold/Axios)
https://www.axios.com/2025/09/04/melania-trump-ai-in-education-the-robots-ar…

Melania on AI in education: "The robots are here"
The White House sees AI in K-12 education as a key part of ensuring American competitiveness in the field.

Aesthetic Experience and Educational Value in Co-creating Art with Generative AI: Evidence from a Survey of Young Learners
Chengyuan Zhang, Suzhe Xu
https://arxiv.org/abs/2509.10576

Aesthetic Experience and Educational Value in Co-creating Art with Generative AI: Evidence from a Survey of Young Learners
This study investigates the aesthetic experience and educational value of collaborative artmaking with generative artificial intelligence (AI) among young learners and art students. Based on a survey of 112 participants, we examine how human creators renegotiate their roles, how conventional notions of originality are challenged, how the creative process is transformed, and how aesthetic judgment is formed in human--AI co-creation. Empirically, participants generally view AI as a partner that s…

AesBiasBench: Evaluating Bias and Alignment in Multimodal Language Models for Personalized Image Aesthetic Assessment
Kun Li, Lai-Man Po, Hongzheng Yang, Xuyuan Xu, Kangcheng Liu, Yuzhi Zhao
https://arxiv.org/abs/2509.11620

AesBiasBench: Evaluating Bias and Alignment in Multimodal Language Models for Personalized Image Aesthetic Assessment
Multimodal Large Language Models (MLLMs) are increasingly applied in Personalized Image Aesthetic Assessment (PIAA) as a scalable alternative to expert evaluations. However, their predictions may reflect subtle biases influenced by demographic factors such as gender, age, and education. In this work, we propose AesBiasBench, a benchmark designed to evaluate MLLMs along two complementary dimensions: (1) stereotype bias, quantified by measuring variations in aesthetic evaluations across demograph…

Exploring Gaze Dynamics in VR Film Education: Gender, Avatar, and the Shift Between Male and Female Perspectives
Zheng Wei, Jia Sun, Junxiang Liao, Lik-Hang Lee, Pan Hui, Huamin Qu, Wai Tong, Xian Xu
https://arxiv.org/abs/2509.12027

Exploring Gaze Dynamics in VR Film Education: Gender, Avatar, and the Shift Between Male and Female Perspectives
In virtual reality (VR) education, especially in creative fields like film production, avatar design and narrative style extend beyond appearance and aesthetics. This study explores how the interaction between avatar gender, the dominant narrative actor's gender, and the learner's gender influences film production learning in VR, focusing on gaze dynamics and gender perspectives. Using a 2*2*2 experimental design, 48 participants operated avatars of different genders and interacted with male or…

ArchGPT: Understanding the World's Architectures with Large Multimodal Models
Yuze Wang, Luo Yang, Junyi Wang, Yue Qi
https://arxiv.org/abs/2509.20858 https://

ArchGPT: Understanding the World's Architectures with Large Multimodal Models
Architecture embodies aesthetic, cultural, and historical values, standing as a tangible testament to human civilization. Researchers have long leveraged virtual reality (VR), mixed reality (MR), and augmented reality (AR) to enable immersive exploration and interpretation of architecture, enhancing accessibility, public understanding, and creative workflows around architecture in education, heritage preservation, and professional design practice. However, existing VR/MR/AR systems are often de…