Human–Computer Interaction (HCI) for AI Systems Design

Learn how to practically model and design human-AI systems, mastering automation, user experience and risk management.

  • Duration

    8 weeks

  • Commitment

    6-8 hours per week

  • Price

    US$2,400

  • Study mode

    Tutor guided

  • Certificate of Achievement

    Evidence your learning with a Certificate of Achievement from the University of Cambridge on successful completion.

    • Start

      03 July 2023

    • Finish

      28 August 2023

    • Enrol by

      26 June 2023

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Course overview

AI, or artificial intelligence, has tremendous potential to reduce operational costs, increase productivity and automate processes in user experience (UX), product design and development. However, AI is notoriously challenging to integrate into systems design, especially systems which involve human–computer interaction (HCI). This course will take a practical, systems thinking perspective, using functional modelling frameworks to break interactive systems down and teach professionals how to successfully, safely and meaningfully infuse their own systems with AI. 

Led by Professor Per Ola Kristensson, this 8-week course will teach methods, principles and approaches for designing systems, products and services that leverage AI to assist users in achieving their goals and improving user experience. Through a series of case-based exercises (applicable to a wide range of industries), learners will gradually create a report that functionally models their own AI system design, including strategies for managing risk, governance, verification and validation. 

The course will provide professionals at a senior or managerial level with practical and transferable knowledge on how to effectively use AI in a system and enable them to present this knowledge in a clear and compelling fashion to internal stakeholders, external clients or venture capitalists. 

What will I learn?

By the end of the course, you will be able to:

  • derive a solution-neutral problem statement that motivates a human-AI system and arrive at a requirements specification that can be used to test the system 
  • design a functional model of a human-AI system and analyse the types and levels of automation that can be used to address the solution-neutral problem statement 
  • perform a risk analysis and determine the types of risks that are inherent in the human-AI system and propose mitigation activities 
  • create a verification cross-reference matrix that can be used to verify that system requirements have been met for all deployment contexts relevant to the human-AI system 
  • develop a strategy for managing the risks and governance issues of a human-AI system 
  • create a validation strategy to ensure the human-AI system is fit for purpose and addresses the overall function it is intended to perform. 

Who is this course for?

Due to the high-level systems-thinking approach this course will take, it is recommended for professionals operating at a senior or managerial level, such as Senior Product Developers, Product Design Managers, Lead System Designers, and directors of UI or UX.  This course is applicable to a wide range of industries, and does not require deep technical expertise to participate in. Professionals managing product design, development and user experience in the following industries, with an interest in applying AI to improve products or processes will find this course valuable:  

  • manufacturing 
  • technology 
  • financial services (including insurance and banking) 
  • healthcare (especially the production of medical devices) 
  • consumer/commercial products. 

What will I get on completion?

Evidence your learning with a Certificate of Achievement from the University of Cambridge on successful completion.

Course dates

  • 2023

    03 Jul - 28 Aug

    Places available

    Enrol by 26 Jun

Requirements

Level of knowledge

  • The course does not require a university-level mathematics background, or prior experience with artificial intelligence, but some experience with quantitative reasoning or critical thinking is highly recommended. 

Materials & equipment

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