University of Cambridge
HCI (Human Computer Interaction) for AI Systems Design
AI-assisted Error Monitoring & Detection for Industrial Production Machinery
Participation
Individual work
University Staff
Prof. Per Ola Kristensson
Alva Markelius
Elizabeth Barsotti
Duration
8 weeks (Part-time)
Year
Oct – Dec 2025
Tools
Figma
Miro
Microsoft Excel
Tableau
Apple Keynote
Real-world scenario
Design of an AI-assisted real-time error and fault monitoring and detection system for industrial production machinery within a factory, to:
- minimise production downtime
- monitor machine metrics
- identfiy breaches of operating tolerance levels
- identify errors and faults
- idenitfy and recommend suitable replacement machines and skilled workers
- allow human supervisors to dismiss or snooze system notifications and manually override AI recommendations
- enable human-in-the-loop interactions, — such as dismissing, snoozing, and manual overrides (with rationale capture) — to create feedback loops that continuously informs and improves system behaviour.
Tasks
- 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 function 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 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.
Course scoring
Task Discussions
100%
Participation
95%
Final Project
86.7%
Final Course Grade
94%
Pass
Submitted assignment
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