Ann Li

Hand-written name, "Ann Li"

Ann Li

is a multidisciplinary designer
driven by
storytelling, sense-making, and curiosity, exploring experiential narratives and creative tech

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Ann Li

Neurobiologist-turned designer. Currently pursuing a Masters in Interaction Design at Carnegie Mellon University.

Work spans the digital and physical, including multisensory exhibits, creative direction and strategy, interactive installations, and user interfaces.

Augmenting Embodied Learning, Manufacturing Futures Institute

Integrating XR and tinyML to train skilled workers for future opportunities in creative arts and manufacturing


Design Research  
Workshop Facilitation
Feature Development
Interface Design
XR Prototyping




Dina El-Zanfaly
Daragh Byrne
Zhenfang Chen
Andrew Knowles
Tate Johnson
Semina Yi
Yumeng Zhuang


Design Research
UX Design
Conversation Design
Motion Design

Despite the prevalence of welding in construction, manufacturing, and creative arts, training novice welders is a time-intensive and challenging process.

Metal welding is a craft manufacturing skill that can be unusually difficult to externalize and represent to novices. Building competency requires an apprentice to iteratively practice embodied skills and sensitize themselves to a sensorially complex practice. Learners must build deep embodied knowledge such as hand-eye coordination, muscle memory, and sound identification.

To explore these challenges, a series of co-design workshops was conducted with a youth program in welding and fabrication. Opportunities for mixed reality, sensing, and tinyML processes to augment welding training and practice were identified, leading to the design of an extended reality welding helmet and torch.
We apply machine learning to a lightly-modified off-the-shelf XR and welding setup to enable in-situ welding training, enhancing the embodied learning of welding in three key ways.

An XR system assists with limitations of traditional welding education, by providing realtime, in-helmet, and in-situ feedback and response to augment the educational experience

Modified welding helmet and gun

By enabling users to encounter real behaviors, forces, sounds, and overall experience of an active weld, we provide a novel in-situ experience with spatial and biometric analysis

The designed system allows us to explore other aspects of embodied learning in welding practice, including the analysis of biometric data, performance analysis, and the inclusion of meditation to further augment the educational experience.

Visual XR Guides and Integrated Motion Sensing

Combined motion-sensing and visual XR feedback helps improve proprioceptive and embodied learning

Sensing Sonic Cues During Welding Practice

Acoustic sensing focuses learner attention on non-visual cues of weld performance

Mediated Meditation and Regulation

Biometric sensing enhances mindfulness and stress management in sensorially challenging environments

System diagram
Experiential co-design workshops conducted with community collaborators at the partner site

Focus groups with instructors and leadership
Participatory co-design of HUD interface
Accessible prototyping for XR visual interfaces
Weldning training immersion

Prior work in the areas of extended reality and welding create isolated, fully immersive simulations of welding, rather than an augmentation of actual welding in context.

Existing products and research include full VR welding experiences (training without a functional helmet or torch, but a visual augmentation with hand tracking) and AR/computer vision-based simulations using tracking markers.

The novelty of our work includes the ability for it to be used as an in-situ, functioning augmentation (implemented to work with a live spark) as well as our holistic augmentation (beyond training muscle memory, our system also uses additional sensors and the Quest's onboard sensors to promote good practice through feedback on mindful breathing, weld performance, and other attributes).

Publications and press for more details on our co-design process and hardware development, system setup and training specifics, related works, and citations

Augmenting Embodied Learning in Welding Training: The Co-Design of an XR- and tinyML-Enabled Welding System for Creative Arts and Manufacturing Training
ACM Tangible Embedded and Embodied Interaction 2024
Honorable Mention

Augmenting Welding Training: An XR Platform to Foster Muscle Memory and Mindfulness for Skills Development
ACM Interactive Surfaces and Spaces 2023
Best Demo

Machine learning and extended reality used to train welders
Feature: Carnegie Mellon University College of Engineering

Build Back Better Grant

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© 2023 Ann Li
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