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This Position reports to:
R&D Team Lead

In recent years, the rapid development of AI solutions has significantly impacted various industries, including process industries, where AI is expected to assist operators in decision-making. However, several challenges remain in making AI truly beneficial for operators, where one major issue is the lack of diversified and high-quality data for AI training. Relying solely on manuals and documents is often insufficient and assimilating complex operational data is a time consuming and requires vast data science competence. Even with the advancements of data engineering it is apparent that there are deficiencies of industrial information exist, due to limited sensing and digitalization.

To tackle the problem, some research suggests that incorporating tacit knowledge from human experts into AI systems could improve decision-making by making AI more adaptable and better at handling ambiguity and uncertainty.

Tacit knowledge is spontaneous, intuitive, experimental, everyday knowledge, and it includes a range of conceptual and sensory information as well as images which can be exercised as an attempt to make something meaningful. However, the process of capturing tacit knowledge and converting it into explicit information suitable for AI training remains underexplored.

In addition, heavy industries will face a big transformation in the future when experienced workers retire, and new generation will become the major workforce. Experienced human workers often rely heavily on tacit knowledge to help them make decisions in operations. Those precious tacit knowledge experienced workers have gained throughout the years will be at a risk of getting lost. How to maintain the knowledge and transfer them to younger workforce will be a key issue for many companies in the industries.

This thesis will focus on exploring UX design or framework for capturing tacit knowledge of domain experts through interacting with AI agents, to help AI/ML generate best practices or standardized workflows.

Recommended research questions are:

• What are users' requirements and needs for using AI agents to capture operators' tacit knowledge during their daily tasks?
• How to design human AI interaction to enable and motivate knowledge sharing for expert users during their daily interaction with AI systems?
  • How does an ideal UX flow look like?
  • What are design implications for designing such AI agents?
  • What are the potential risks and challenges faced by users when interacting with AI systems designed to capture and utilize tacit knowledge?

Details

• Period: Spring 2026, typically between January and July
• Number of credits: 30 ECTS
• Number of students: 1 - 2
• Location: Västerås, hybrid work possible

Your responsibilities

• Literature review on tacit knowledge elicitation, and human AI interaction for knowledge elicitation
• Conduct user studies to understand potential user needs, using methods such as expert or user interviews (ABB internal experts or operators in plants)
• Create concepts based on learning and assumptions
• Implement prototypes (fidelity can be flexible)
• Conduct user evaluation (optional)
• Summarize results and identify design implications on using AI agent to conduct tacit knowledge elicitation

Your background
We are looking for motivated master's students with a strong interest in human-centered design and automation.

Candidates could have:

• Academic Background in one or more of the following fields:
  • Human-Computer Interaction (HCI)
  • Interaction Design
  • Human Factors Engineering
  • Cognitive Psychology or Behavioral Science
  • Systems Design or Ergonomics
  • Socio-Technical Systems
• Ability to conduct user research and translate insights into concrete concepts
• Ability to make quick prototype (ideally have skills to create high fidelity prototypes), using Figma or other tools to create interactive prototype
• Able to approach challenges proactively and creatively, integrating user-centered approaches with technological advancements
• Ideally to have interests or previous experience in the topic of human AI interaction and knowledge conversion between human users and AI
• Big plus if you are familiar with SQL / NoSQL or other languages for storing and querying knowledge databases (not mandatory)

More about us

ABB is a global technology leader in electrification and automation. We see our purpose as being to enable a more sustainable and resource-efficient future. By connecting our engineering and digitalization expertise, we help industries run at high performance, while becoming more efficient, productive and sustainable so they outperform. We call this: 'Engineered to Outrun.'

Recruiting Manager Dawid Ziobro, dawid.ziobro@se.abb.com, will answer your questions.

Apply with your CV, academic transcripts and a cover letter in English. We look forward to receiving your application!

Join us. Be part of the team where progress happens, industries transform, and your work shapes the world. Run What Runs the World.

A Future Opportunity
Please note that this position is part of our talent pipeline and not an active job opening at this time. By applying, you express your interest in future career opportunities with ABB.

We value people from different backgrounds. Could this be your story? Apply today or visit www.abb.com to learn more about us and see the impact of our work across the globe.