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1,682 papers
This survey of 443 designers, among the first phase-disaggregated accounts of its kind, examined reported AI use across the four phases of the Double Diamond workflow, pointing to two distinct modes of human-AI collaboration: generating from scratch versus refining within existing software.
This exploratory qualitative study examines how students' expectations of one process-capture platform, Turnitin Clarity, compared with their experience of using it are compared with their experience of using the platform.
What shapes a model-generated inquiry when no discussion question is supplied? We introduce a bottom-up forum framework inspired by Philosophy for Children, in which language-model agents read a philosophical narrative, propose and select questions, and develop a shared conclusion without a privileged model facilitator...
Text-to-image generative AI can produce renderings from natural-language prompts in near real time, making it increasingly popular for rapidly visualizing concepts in early-stage architectural design. Meanwhile, exchanging ideas efficiently and building shared understanding have long been central challenges in architec...
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Full-duplex dialogue requires timely yet selective interruption handling, which end-of-turn prediction alone cannot achieve: complete utterances may need no response, while unfinished requests may warrant interruption. To address this challenge, we propose HiThink Turn, an intent-aware streaming turn-state predictor th...
Scanvas is presented, an AI-supported system for systematically discovering and developing synergistic design opportunities and enables users to surface and develop significantly higher-quality, synergistic concepts compared to LLM ideation baselines.
This work introduces a shared structural representation that decomposes ideas into purpose, mechanism, and implementation components and organizes related components in a multi-layer concept graph and defines measures of pairwise similarity and set-level mechanism coverage for assessing idea diversity.
As AI becomes part of how books are authored, reader response to suspected AI authorship grows more consequential, yet remains unexamined. We analyze 863 low-star reviews of 78 Amazon bestsellers across 8 categories at three levels of proximity to AI. Suspicion concentrates in Generative AI books (35.1%) but appears in...
ReVision is presented, an AI-based tool that decomposes visual and textual references into editable conceptual interpretations and visual motifs, enables their recombination across conceptual and visual spaces, and renders each direction as divergent visual-form variations, supporting more divergent exploration during...
We present preliminary empirical evidence that single-observation queries are insufficient for evaluations of LLM refusal behaviors. Using a longitudinal auditing system, we issued identical prompts 100 times each across four dates to GPT-4.1 for two socially salient topics across 20 Wikipedia sources. Refusal outcomes...
A mixed-methods investigation to characterize and mitigate memory misalignment from user perspectives, which highlights the tension between supervisory agency and interaction overhead, and advocates for friction-aware memories that balance user oversight with conversation smoothness.
On Twin-2K-500, OwnWords predicts ordinal survey answers more closely than the written memory, but does not improve exact-choice accuracy and lowers it in one of two samples.
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.
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.
A Blog post by H company on Hugging Face
The PKG Center for Social Impact expands Code.Tulsa experiential learning program.
Computer scientist, entrepreneur, and philanthropist will collaborate with the MIT Schwarzman College of Computing to advance AI and scientific discovery.
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