Work Sample 3: Z Method: A Computational Design Approach for UX & AI

Motivation:

Over the last four years, I developed a methodology for providing promising results to machine learning through the use of human-centric datasets. As a UX Researcher, I developed assistive interactive interfaces for therapists and patients in the realm of stroke rehabilitation. The design and development of my interfaces led me to create a methodology to understand the larger context of how to design assistive technology, no matter the domain. My research focused on taking a mixed-methods approach to understanding how to increase human intelligence computationally.

Artificial intelligence (AI) is increasingly considered a critical computational design material in developing innovative products, systems, and services. The framing of AI as a possible key constituent in the design process necessitates rethinking the end-goal function and use of computational design solutions in an era of “evolving complementary capabilities and doings” AI. In particular, machine learning has the potential to radically reorient the researcher and designer’s approach to crafting high-quality user experience as a human-centered design stance might now also have to accommodate the needs of machines.

Education, retail, and healthcare are three significant global areas of inquiry that could greatly benefit from ethical innovations in artificial intelligence. The worldwide COVID-19 pandemic brings these issues even more to the fore as recurring lockdowns keep people at home, necessitating the transfer of services to virtual domains. 

I took a collaborative and human-centric computational design approach toward understanding the application of Human-Computer-Interaction (HCI) for complex Machine Learning (ML) in Embodied Learning (EL) scenarios. The work described has focused on applying HCI methodologies to complex ML contexts to capture and assess how humans move, think, and learn in embodied spaces. HCI and ML can be considered existentially different in their primary objectives, methodologies, and evaluation processes within embodied spaces. HCI typically takes a human-centered approach, focusing on using human intelligence to best design solutions. In contrast, ML processes typically focus on using highly standardized, quantifiable datasets as input for extremely fast processors to provide generalizable outputs.

Human learning in embodied learning spaces is often considered tacit and challenging to uncover. I took a three-pronged approach to designing a solution to reveal and augment this form of human knowledge. The primary objective of my talk is to describe the methodology (Z Methodology)  that integrated an HCI approach to synergize human intelligence with computational intelligence. This methodology comprises three highly interrelated and iterative processes: First, it transforms tacit human knowledge into explicit knowledge. Next, it converts explicit human knowledge into a computable model. Finally, it uses computational power to empower humans. W

Research question(s):

  1. .How can we develop an HCI design methodology that integrates human intelligence with computational intelligence, making tacit human knowledge explicit, making that explicit human knowledge computable, and using that computational power to empower humans?

  2. How can we use this methodology to empower then the human vs. replace the human to incentivize and reward humans in this context?

Research goals:

  • Identifying and understanding the needs of the different (and emergent) stakeholders encountered throughout the development process

  • Revealing expert human knowledge for human movement performance

  • Determining the optimal functionality for the digital support tools required to address stakeholder needs

  • Making explicit human knowledge computable

  • Optimizing the data capture and storage approach for data analysis

  • Capturing high-quality data to assist in the development of a computer vision algorithm


Research & Design Methods:

  • Focus Groups, 1:1 Interviews, healthcare design, iterative design process, service design model, usability evaluations, interactive interfaces, user feedback, statistical analysis, participatory design, and user-flow models


Number of Users:

  • 100+ stroke survivors and 20+ occupational/physical therapists

Outcomes of my work and project impact: 

  • Research trials for stroke survivors across the United States using this Framework and the Apps I have developed

  • The methodology currently being used in the fashion retail space to understand the mental models of store associates

  • MIT Technology Review Innovator Under 35

  • Virginia Tech Computer Science HCI Researcher of the Year

  • Speaker - Innovation Week - Rio De Janeiro Brazil

  • Speaker - EmTech

    Speaker - Design Drives Innovation Week

Z-Method: Computational Design Approach to Building A.I. Systems

Implicit to Explicit Human Knowledge Framework

Implicit to Explicit Knowledge Created through the lens of research and design