It is demonstrated that Big Five inventories do not measure a construct equivalent to human personality in LLMs, highlighting the need for evaluation frameworks that are specifically designed and validated for LLMs, rather than transferring human psychological constructs without first establishing their validity.
Kim Zierahn, Cristina Cachero, Anna Korhonen et al.· arXiv.org· 0 citations
This study applies Archetypal Analysis to standardized acceptance ratings across eight representative AI application contexts in games to provide an exploratory empirical vocabulary for segmenting game AI audiences and offers preliminary design implications for more context-sensitive and player-sensitive AI integration...
T. Hsu, Jiang-Xu Lin, Wen-Ran Chen et al.· arXiv.org· 0 citations
An open-source dataset collected from 279 students at a public Australian university, generated through students' use of FLoRA, provides a valuable resource for advancing understanding of GenAI's role in educational IPS and informing the design of adaptive, inclusive AI-powered learning tools.
This work investigates how listener perception of AI-generated versus human-composed music affects emotional resonance and regulation and challenges the assumption that preference alone defines system success, highlighting design implications for affective and wellness technologies that foreground authenticity, transpa...
Kimaya Lecamwasam, Tishya Ray Chaudhuri· 4 citations
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It is suggested that while they may be able to approximate rank-orderings of human attractiveness, current off-the-shelf commercial MLLMs systematically overrate the beauty of human faces.
Santiago Grandas, Juan Sebastián Cely-Acosta, Mohit Mendiratta et al.· 0 citations
A privacy firewall pipeline based on a U-Net encoder-decoder, trained entirely on synthetic data, that removes speech from ambient audio while preserving environmental sounds indicative of daily activities is presented.
This Perspective argues that AI-mediated BCIs should be evaluated not only by decoding accuracy and task performance, but also by how AI assistance is deployed: when systems act autonomously, seek confirmation, preserve user effort or return control to the user.
This study presents BuildOcc, an open-source Python platform that grounds large language model agents in the ATUS, a nationally representative diary dataset covering 16,684 respondents, and provides the building energy community with a reusable, openly available implementation of the occupant behavioral layer.
A contextual engineering framework that generates eco-feedback using a self-consistency with chain-of-thought prompt, leveraging household energy analysis data, utility rate structures, and household characteristic information contributes to realizing the next level of context-aware interactions between occupants and b...
Wooyoung Jung, Prosper Babon-Ayeng· Energy and Buildings· 1 citation
The PIONEER project reports on the progress made by the PIONEER project on a privacy support tool that combines knowledge transfer and persuasive elements to increase users' privacy awareness and motivation; thus empowering them to more privacy sovereignty.
S. Althaus, Nina Gerber, Sara Hahn et al.· 0 citations
The feasibility and scalability of integrating mindfulness into ITSs through LLM-based interactions is demonstrated and LLMs are positions as an adaptive, socio-emotional layer within cognitive math tutoring.
Vera Rief, Mirella Hladký, Minju Yoo et al.· 0 citations
By aligning EEG with layer-wise neural visibility rather than fixed high-level semantics, the proposed framework improves both retrieval accuracy and image reconstruction in EEG-based visual decoding.
Students in MIT’s Concourse program delve deeply into the human condition, debate challenging questions, and learn to develop judgment about issues that can’t be quantified.
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduSep 30, 2026
Able to defeat top-ranked human players and more efficient than other models, the new system could help decision-makers in military maneuvers or business negotiations.
Computer scientist, entrepreneur, and philanthropist will collaborate with the MIT Schwarzman College of Computing to advance AI and scientific discovery.
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