AI is reshaping academic research, yet its role in peer review remains polarising and contentious. Advocates see its potential to reduce reviewer burden and improve quality, while critics warn of risks to fairness, accountability, and trust. At ICLR 2025, an official AI feedback tool was deployed to provide reviewers with post-review suggestions. We studied this deployment through surveys and interviews, investigating how reviewers engaged with the tool and perceived its usability and impact. Our findings surface both opportunities and tensions when AI augments in peer review. This work contributes the first empirical evidence of such an AI tool in a live review process, documenting how reviewers respond to AI-generated feedback in a high-stakes review context. We further offer design implications for AI-assisted reviewing that aim to enhance quality while safeguarding human expertise, agency, and responsibility.

Aligning large language models (LLMs) behaviour with human intent is critical for future AI. An important yet often overlooked aspect of this alignment is the perceptual alignment. Perceptual modalities like touch are more multifaceted and nuanced compared to other sensory modalities such as vision. This study investigates how well LLMs can understand and interpret human touch experiences by focusing on their capacity to perceive the tactile qualities of everyday objects. For instance, it assesses whether LLMs can recognize that silk satin is softer and smoother than cotton denim. We developed a “Guess What Textile“ interaction using a custom AI system that enables participants to narrate their touch experiences in the “textile hand” task. Participants were given two textile samples–a target and a reference–to handle. Without seeing them, participants described the differences between them to the LLM. Using these descriptions, the LLM attempted to identify the target textile by assessing similarity within its high-dimensional embedding space, where its perceptual representations are encoded. Our results suggest that a degree of perceptual alignment exists; however, it varies significantly among different textile samples. For example, LLM predictions are well aligned for silk satin, but not for cotton denim. Moreover, participants felt that their textile experiences were not closely matched by the LLM predictions. This study is the first exploration into perceptual alignment around touch using LLM encoders, exemplified through textile hand task. We discuss possible sources of this alignment variance, and how better human-AI perceptual alignment can benefit future everyday tasks. • We address the gap in understanding perception alignment between human touch and AI. • First study on alignment between human touch experiences and LLMs in embeddings. • A novel interactive task probes LLMs’ learned representations for human alignment. • LLMs show perceptual biases, aligning better with certain textiles than others.

With perpetually stressful lives, people are prompted to consider slowing down through mindfulness meditation. We extend HCI research beyond its pre-existing focus on interaction-intensive meditation technologies, to focus simply on whether one’s sense of touch can facilitate meditation. We capitalise on the touchless nature of mid-air haptics by rendering subtle sensations on the palm during meditation, using one design derived from pilot testing. In a novel and exploratory between-subjects study, we compare audio versus audio-haptic guidance in adult novices’ mindfulness meditation, through a mixed methods approach which combines standardised questionnaires with micro-phenomenological interviews ( n =10 per group). Interestingly, mid-air haptics quantitatively increased both hindrance and relaxation, which appears conflicting but is actually complementary when considered together with qualitative analysis. Specifically, mid-air haptics initially distracted participants but eventually facilitated mindfulness through three processes (embodied metaphor, breath alignment, and meditative grounding), though this requires further validation. We reflect on the need for future work exploring an integrated, multimodal meditation experience.

The rise of Machine Learning (ML) is gradually digitalizing and reshaping the fashion industry, which is under pressure to achieve Net Zero. However, the integration of ML/AI for sustainable and circular practices remains limited due to a lack of domain-specific knowledge and data. My doctoral research aims to bridge this gap by designing digital multisensory textile experiences that enhance the understanding of the textile domain for both AI systems and humans. To this end, I develop TextileNet, the first fashion dataset using textile taxonomies for textile materials identification and classification via computer vision, and TextileBot, a domain-specific conversational agent. TextileBot integrates textile taxonomies with large language models (LLMs) to engage consumers in sustainable practices. Additionally, my research explores how multisensory experiences can improve user understanding and how AI perceives textiles. The overarching goal is to embed human expertise into machines, design immersive multisensory experiences, and facilitate natural human-AI interactions that promote sustainable practices.

Human-artificial intelligence (AI) alignment ensures that AI systems align with human goals and behaviors. This paper introduces perceptual alignment as a critical aspect of this alignment, focusing on the concurrence between human judgments and AI evaluations across sensory modalities. We particularly explore how Multimodal Large Language Models (MLLMs), which process both visual and textual data, interpret the tactile qualities of textiles—a significant challenge in online shopping environments. Our research analyzes six vision-based MLLMs to see how they describe the tactile experience of textiles and compares these AI-generated descriptions with human assessments. Through semantic similarity measures and in-person evaluations, we investigate the extent of alignment between human perceptions and AI descriptions. Our findings indicate significant variability in the AI’s ability to interpret different textiles, highlighting both the potential and limitations of current AI models in achieving perceptual alignment. This work contributes to understanding the complexities of aligning AI capabilities with human touch sensory experiences.

As we constantly seek to improve and expand upon the capabilities of technology, we frequently wonder whether we use technology to its fullest extent. Studies indicate that increasing our awareness and mindfulness of our senses may lead to a journey of unexplored experiences. In this paper, we focus on the perception of mid-air haptics stimuli and whether it can be improved through mindfulness meditation. We have conducted an experiment with 22 participants given the task to recognize digits 0 to 9 drawn on their palms using a mid-air haptic device under two conditions - with and without prior mindfulness meditation. Results show that for frequencies targeting both Meissner (40 Hz) and Pacinian (200 Hz) receptors, meditation significantly improves performance of the participants, as well as increases their confidence. This suggests that including a short meditation step in haptic user interfaces could lead to improved system performance and user satisfaction.

Millimetre-wave (mmWave) radar has emerged as an attractive and cost-effective alternative for human activity sensing compared to traditional camera-based systems. mmWave radars are also non-intrusive, providing better protection for user privacy. However, as a Radio Frequency based technology, mmWave radars rely on capturing reflected signals from objects, making them more prone to noise compared to cameras. This raises an intriguing question for the deep learning community: Can we develop more effective point set-based deep learning methods for such attractive sensors? To answer this question, our work, termed MiliPoint, delves into this idea by providing a large-scale, open dataset for the community to explore how mmWave radars can be utilised for human activity recognition. Moreover, MiliPoint stands out as it is larger in size than existing datasets, has more diverse human actions represented, and encompasses all three key tasks in human activity recognition. We have also established a range of point-based deep neural networks such as DGCNN, PointNet++ and PointTransformer, on MiliPoint, which can serve to set the ground baseline for further development.