DBIS

The healthcare and long-term care (LTC) sector is experiencing severe workforce challenges. Nurses and caregivers are frequently confronted with excessive workloads, mandatory overtime, and the constraints of complex union agreements. These pressures contribute to burnout, high turnover rates, and rising labour costs, while simultaneously undermining care quality and patient satisfaction.

Conventional workforce management (WFM) tools are largely focused on record-keeping and compliance auditing, which lack predictive and optimization capabilities, making them inadequate for the highly dynamic and complex nature of healthcare scheduling. In this thesis, we aim to develop a next-generation, AI-driven, compliance-aware, and human-centered intelligent scheduling platform, that can satisfy regulatory and union requirements while also respecting staff preferences and improving organizational outcomes.

The goal of this thesis is to design and prototype a serious game that simulates stakeholder engagement challenges in MLOps lifecycles. The game will make use of LLM-based agents to represent typical stakeholder roles (e.g., data scientists, ML engineers, domain experts, operations managers) and allow players to interact with them in different lifecycle stages.

The aim is to both explore the dynamics of stakeholder collaboration in MLOps and provide an educational tool for students, practitioners, and researchers to better understand the complexities of human and organizational factors in MLOps.

This thesis aims to explore and prototype a system for participatory governance and transparent decision-making in the context of modernizing legacy software systems. Using Large Language Models (LLMs), the system will analyze and extract decisions from various project artifacts (e.g., commit messages, meeting transcripts), represent them in a structured decision domain model, and create an audit trail that supports stakeholder engagement, accountability, and traceability throughout the modernization process.

The overall goal is to support more inclusive, explainable, and well-documented modernization strategies by making both decisions and their rationales visible and verifiable to all relevant stakeholders.

With rapid advances in artificial intelligence, especially large language models (e.g., OpenAI GPT, Anthropic Claude, Google Gemini), AI has demonstrated great potential in natural language processing, code generation, and automated testing. Programming, traditionally a highly specialized and creative task, is increasingly supported and partially automated by AI. Tools such as GitHub Copilot and OpenAI Codex already generate code snippets based on natural language prompts, boosting developer productivity.

However, current AI code generation systems face challenges such as limited context understanding, inconsistent code quality, lack of adaptation to complex software engineering workflows, and limited capacity for continuous interaction and collaboration. Therefore, building an intelligent coding agent system capable of autonomous planning, requirement comprehension, code writing, debugging, and optimization has become a cutting-edge research topic in academia and industry.