Professional Doctorate in Applied Artificial Intelligence

This course provides a practical foundation in applied research methodology enhanced by AI tools across the full research lifecycle. Students learn to design research for industry-focused problems, define clear questions and impact metrics, and integrate AI into literature review, data collection, and exploratory analysis.

The course covers quantitative, qualitative, and mixed-methods approaches supported by technologies such as large language models (LLMs), automated search, and knowledge graphs. It also addresses ethical, reliability, and bias considerations to ensure responsible and rigorous AI-assisted research.

This course explores how organizations navigate technological evolution and make strategic decisions in an AI-driven world. Students examine technology life cycles, diffusion of innovation, and market adoption dynamics to understand how new technologies create competitive advantage.

The course focuses on aligning AI initiatives with business and societal objectives, while managing technological change through effective governance, risk management, and compliance frameworks. Students also develop foresight and scenario-planning skills to anticipate emerging AI technologies and potential disruptions.

This course offers a deep, cross-sector analysis of current and emerging AI use cases, with a strong focus on business integration and return on investment. Students explore AI applications across healthcare, finance, manufacturing, education, the public sector, and sustainability.

The course examines AI-driven business models, including value creation, monetization strategies, and sources of competitive advantage. Through case studies of both successful and failed AI deployments, students identify practical lessons and common pitfalls. Participants also learn how to measure, evaluate, and effectively communicate AI’s impact to diverse stakeholders.

This course provides an accessible, high-level overview of AI’s major verticals, equipping students to collaborate effectively with technical teams and make informed strategic decisions.

Students explore key domains including Natural Language Processing (NLP), Computer Vision, and Predictive Analytics, with a focus on practical integration and business applications. The course also introduces frontier areas such as generative AI, multimodal models, and reinforcement learning, highlighting their emerging applied potential.

Emphasis is placed on understanding capabilities, limitations, and real-world deployment considerations rather than technical implementation details.

This course guides students in developing a research proposal on technology topics aligned with OPIT’s core research areas. Students prepare a detailed write-up summarizing the state of the art and outlining their proposed project.

The proposal is planned and discussed with a Supervisor, who evaluates the work. Final approval involves the Doctoral Committee, ensuring the project meets rigorous academic and strategic standards.

After approval of the Research Proposal, students conduct a Preliminary Research Investigation to assess the feasibility of their project and gather initial evidence or data.

This phase allows refinement of the proposal based on insights from the preliminary analysis. The investigation is evaluated by the Supervisor and the Examining Committee to ensure academic rigor and practical viability.

The Capstone Research Project and Doctoral Dissertation represent the final stage of the programme. After successfully completing all taught modules and the approved research proposal and preliminary investigation, doctoral candidates undertake an independent research project under academic supervision.

The final assessment consists of a written doctoral dissertation (approximately 60,000–80,000 words or equivalent applied research output) and a formal oral defence before an examining committee.