Design at Scale (D@S) Lab    

Research Themes: software tech & intelligent systems


A TRL is a measure to indicate the matureness of a developing technology. When an innovative idea is discovered it is often not directly suitable for application. Usually such novel idea is subjected to further experimentation, testing and prototyping before it can be implemented. The image below shows how to read TRL’s to categorise the innovative ideas.

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Summary of the project


Designers often use qualitative research methods to gather data in order to understand the needs of the target group for which they want to design a product, service or intervention. The beauty of these methods is that they produce rich types of data – detailed experiences captured in long text, a multitude of photographs and days of videos. When adequately processed, this data can yield deep insights. The downside of such methods is that they are time-consuming and labour-intensive and the collected data typically originates from small sample sizes. The researcher is developing new AI-powered design methods and techniques that utilize Natural Language Processing (NLP) and Computer Vision (CV) to automate the collection and analysis of rich data. These methods are expected to not only upscale sample size and reduce analytical labor, but also enable designers and user researchers to focus on tasks that require human intuition and creativity (e.g., designing a new prototype).
Using and refining these AI techniques can help designers to collect and produce more insights from humans, fostering a synergistic relationship between the designer and AI. Consequently, being able to gather and process more data can also help to train self-learning algorithms in dealing with more complex data and generating richer responses in conversation agents or onboarding systems for new workers. Overall, his research focuses on designing and developing AI-powered systems that augment human perception and cognition. Ultimately, he is interested in utilising AI for extending human abilities beyond the humanly possible.

What's next?


The next step is validating methods and protypes in different contexts, while moving them from the lab into the wild. He will be working on a smart-glasses prototype that combines data from different physiological sensors to measure one’s cognitive state and streamline the interaction with surrounding intelligent products, services, and systems. The aim is to inaugurate the era of cognition-aware systems---systems that consider the user’s cognitive and perceptual capacities.

With or Into AI?


Both

Dr. Evangelos Niforatos

Prof. Dr. Gerd Kortuem

Dr. Ujwal Gadiraju

Dr. Jie Yang

Faculties involved

  • IDE
  • EEMCS