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About this opportunity:

Ericsson Research is at the forefront of exploring how AI and knowledge graph technologies can enhance the way large-scale research organizations work. As research grows in complexity and scale, there is increasing interest in how intelligent systems can help researchers discover relevant prior work, understand connections between research artefacts, and make better-informed decisions throughout the research lifecycle.

The objective of this thesis is to study, redesign, prototype, and evaluate the user experience and organizational value of a multi-stakeholder AI- and knowledge graph-based workflow for scientific review and approval. The work will explore how AI-driven discovery and recommendation capabilities can support researchers, reviewers, approvers, and other stakeholders, with a strong focus on trust, transparency, explainability, and responsible use of AI in professional decision-making.

What you will do:

• Conduct a literature review covering AI-assisted review and approval systems, human–AI interaction, explainable AI, decision support, knowledge graph-based discovery, trust in automation, and responsible AI.
• Map a real-world scientific review and approval workflow, including stakeholder roles, information needs, decision points, handovers, and dependencies.
• Investigate the possibilities and limitations of data sources such as publication metadata, citation networks, research artefact repositories, and knowledge graph relationships for supporting review and approval workflows.
• Conduct qualitative user research with researchers, reviewers, approvers, and other stakeholders to understand requirements, expectations, and trust-related concerns.
• Define design principles and requirements for an AI-supported workflow that balances automation with appropriate human oversight and follows corporate guidelines for responsible AI.
• Develop or adapt a prototype workflow, interface, or system component that demonstrates how AI-generated suggestions, knowledge graph links, provenance information, and explanations can improve the review and approval process.
• Evaluate the prototype through user studies, usability testing, and stakeholder feedback.
• Analyse strengths, limitations, and organizational implications, including risks related to transparency, trust, accountability, and adoption.
• Present the thesis with recommendations for integrating AI-supported capabilities into research workflows.

The skills you bring:

You are a Master's student in interaction design or human–computer interaction; computer science; information systems; machine learning, data science, or information retrieval; business and management; law with an information-technology specialisation; or a related field.

You have strong analytical, research, problem-solving, writing, and communication skills.

You are interested in one or more of: human–AI interaction, user-centred design, AI governance, knowledge management, or research information systems.

You are comfortable working with multiple stakeholder groups and translating diverse needs into design requirements.