Complex problem solving depends on acting effectively and understanding how a system works. AI advice may support these outcomes unequally. Two preregistered experiments compared participants managing a simulated clothing factory with and without an LLM advisor. Across studies, AI-supported participants reported greate...
While Large Language Models (LLMs) advance 3D indoor scene synthesis, current pipelines fail to retain user-specific preferences across sessions, making immersive authoring a repetitive and physically fatiguing process. We present SPHERE, an adaptive VR generation framework that transforms isolated synthesis into conti...
Hyeonmin Lee, Zheng Wei, Kyungmin Kwon et al.· 0 citations
As code generation is increasingly delegated to AI systems, the bottleneck is shifting from writing code to supervising the systems that write it --- a shift CS-education researchers have begun to name. This shift exposes a vocabulary gap: the field asks for "human oversight" without a working distinction between the t...
ABDA-NL adds a natural-language interface to ABDA, a system for argument-based discussion using ASPIC- knowledge bases under grounded semantics. Users see which conclusions are accepted, rejected, or undecided, open an interactive rendering of the grounded discussion game to learn why, explore what-if alternatives by s...
Shawn Bowers, Martin Caminada, Hao-Yang Liu et al.· 0 citations
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Eliciting detailed and actionable software requirements from end-users is a critical phase in the iterative development of a software product or application. To ensure the feedback collected is detailed and actionable, software teams can leverage the laddering interview technique. While effective for ensuring granular...
Manjushree B. Aithal, Alexander Kotz, J. Mitchell· 0 citations
The rapid development of AI-driven tools, particularly large language models (LLMs), is reshaping professional writing. Still, key aspects of their adoption such as language support, ethics, and long-term impact on writers' voice and creativity remain underexplored. In this work, we carried out a questionnaire (N = 301...
Drawing on a multimodal corpus of public accounts, commentary, and representations from 2023 to 2026, two complementary concepts are contributed: the covert triad names a structural reconfiguration: a relationship that appears dyadic but operates triadically, with AI visible only to the partner who deploys it.
Extended interaction with large language models (LLMs) has been linked to the reinforcement of delusional beliefs, attracting clinical and public concern. Yet most empirical work evaluates model safety in brief interactions, which may not reflect how harms develop through sustained dialogue. Five LLMs were tested acros...
Luke Nicholls, Robert Hutto, Zephrah Soto et al.· 0 citations
Findings show that alt text written solely by DSO professionals has lower quality than alt text written with AI assistance, while AI assistance also helped DSO professionals write alt text more quickly and with greater confidence; however, they reported inefficacy in interactions with the AI.
Muhammad Raees, Yugo Iwamoto, Konstantinos Papangelis et al.· arXiv.org· 1 citation
MORA, an AI-mediated role-playing story-based system in which target-bearing words recur as characters, locations, and props, eliciting productions under two response constraints while retaining recordings for clinician review is presented.
Su-Min Hong, Yi-Nuo Yang, Jia-Wen Li et al.· 0 citations
It is suggested that effective legible motion in social robot navigation benefits from interaction-level intent representations that support coordination, with some effects persisting even when human attention is divided.
Pranav Goyal, Andrew Stratton, Christoforos Mavrogiannis· 0 citations
Large language models (LLMs) are increasingly deployed as automated judges for AI-generated content, yet a single judge is unreliable and even a panel of judges leaves a hard residue: when judges disagree, majority voting discards the conflict instead of resolving it. We present JuryFlow, a disagreement-guided, human-i...
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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