Doctor of Technology in Artificial Intelligence

This course is designed to equip doctoral students with a robust foundation in research methodologies specifically suited for the rapidly evolving domains of Artificial Intelligence (AI) and technology management. Recognising the interdisciplinary nature of these fields, the course guides learners through the entire research process—from conceptualisation to execution— emphasising methodological integrity, ethical responsibility, and relevance to professional practice.

The course begins by situating research within the broader context of technological innovation and digital transformation. Learners will explore key research philosophies and paradigms, and how these influence methodological choices in AI and technology-related studies. A balanced focus on both quantitative and qualitative research methods enables learners to critically compare different designs such as experimental studies, case studies, ethnography, and mixed-methods approaches. Special attention is paid to secondary research methodologies, including scoping reviews and systematic reviews, which are essential for synthesising existing literature and identifying research gaps in AI and technology management.

Students will gain practical skills in research planning, data collection, and analysis, with an emphasis on the appropriate use of analytical tools and software. Ethical principles such as data privacy, responsible AI, and research integrity are embedded throughout the course to foster responsible and context-aware research practices. The course also introduces advanced methods in evaluating research quality—such as the application of validity, reliability, and trustworthiness across different methodological traditions.

By the end of the course, learners will be prepared to critically appraise existing research, design and execute original studies, and translate research findings into actionable insights for both academic and professional settings. This course lays a strong methodological and ethical foundation for doctoral-level research projects and future contributions to the fields of AI and technology management.

In today’s dynamic and technology-driven global environment, organizations face constant disruption from emerging technologies that reshape industries and redefine strategic priorities. This course, Disruptive Technology Management, Innovation, and Leadership, is designed to prepare current and aspiring leaders to effectively navigate, harness, and lead through technological transformation.

Students will explore the foundational theories of disruptive innovation and the diffusion of technological change, enabling them to critically assess how emerging tools—such as artificial intelligence, blockchain, Internet of Things (IoT), augmented and virtual reality (AR/VR), and quantum computing—impact organizational structures, customer behavior, and competitive landscapes. With a strong grounding in real-world case studies, learners will examine historical and contemporary examples of disruption, drawing insights that inform strategic decision-making and innovation leadership.

In addition to theoretical models, the course emphasizes practical frameworks such as design thinking and agile methodologies (e.g., Scrum and Kanban) as mechanisms for fostering innovation, responding to rapid change, and co-creating value. Students will learn how to identify and interpret technological trends, evaluate sustainability and ethical implications, and develop data-driven strategies that promote long-term organizational resilience and growth.

This course also delves into the leadership competencies needed to guide complex, diverse teams in environments characterized by uncertainty and rapid innovation. Students will build the capacity to lead strategic foresight efforts, champion sustainable practices, and cultivate a culture of experimentation and adaptability.

By the end of the course, students will possess the critical knowledge and applied leadership skills required to manage technological disruption effectively and shape innovation agendas that drive sustainable and responsible transformation in their respective fields.

In an age defined by exponential technological growth, artificial intelligence (AI) plays a pivotal role in reshaping how knowledge is created, managed, and applied within organizations. This course, AI Technology and Knowledge Practices Based Research, offers learners a comprehensive exploration of the intersection between AI technologies, research methodologies, and organizational knowledge practices. It is specifically designed for students seeking to critically examine how AI transforms strategic decision-making, operational efficiency, and sustainable development.

Students will begin by investigating the theoretical foundations and evolving paradigms of AI, focusing on their influence on knowledge systems and the broader organizational ecosystem. Through the application of advanced research methodologies, learners will analyze frameworks for AI-driven knowledge generation, storage, retrieval, and synthesis, paying close attention to ethical considerations and responsible integration. The course places a strong emphasis on applied research and data interpretation to solve real-world problems. Learners will review case studies showcasing innovative AI applications and explore tools and strategies for leveraging AI to address complex organizational challenges. Furthermore, students will critically assess the socioeconomic implications of AI technologies and engage with principles of sustainable, responsible AI adoption.

By the end of the course, students will be equipped with the theoretical insights, methodological expertise, and applied research capabilities necessary to lead AI-centric knowledge initiatives and contribute meaningfully to strategic innovation within a range of professional settings.

As artificial intelligence (AI) and emerging technologies continue to evolve and integrate into core societal and organizational systems, the need for robust ethical, regulatory, and governance frameworks becomes increasingly vital. This course, AI and Technology Management Research: Governance, Ethics, and Regulation, offers a rigorous exploration of the multifaceted challenges and responsibilities associated with overseeing the deployment of intelligent technologies in a rapidly digitizing world.

Students will engage with a wide range of theoretical and applied topics, including the ethical dimensions of AI—such as transparency, bias, accountability, and fairness—as well as the implications of data privacy and cybersecurity. The course provides an in-depth examination of global regulatory landscapes, enabling learners to critically assess various governance models and international compliance standards.

Through comparative analyses and case-based inquiry, learners will evaluate the effectiveness of current regulatory frameworks while developing tools to assess ethical risks and policy gaps. The course emphasizes proactive strategies for designing governance structures and ethical protocols that align with legal mandates and social values, without stifling innovation.

By the end of the course, students will be equipped to shape responsible AI practices, advocate for transparent and accountable technology policies, and lead initiatives that uphold ethical standards in technology management across diverse organizational contexts.

This course provides doctoral candidates with a robust foundation in research design, academic writing, and scholarly inquiry, specifically within the realm of Artificial Intelligence and Technology Management. Emphasizing both theoretical and practical dimensions of research development, the course equips students with the methodological tools and conceptual frameworks necessary to conceive, plan, and articulate a rigorous doctoral research proposal.

Students will be guided through each critical stage of research design—from identifying a research- worthy knowledge gap to formulating research questions, constructing a literature review, selecting appropriate methodologies, and ensuring adherence to ethical research practices. Emphasis is placed on scholarly reasoning, analytical clarity, and the synthesis of complex information into coherent academic arguments.

Through hands-on learning, doctoral candidates will develop a 3,000-word research proposal that integrates evidence-based reasoning, robust methodological choices, and realistic timelines. The course also introduces students to advanced literature searching, citation management tools (such as Mendeley, Zotero, and EndNote), and critical appraisal strategies essential for both primary and secondary research, including systematic and scoping reviews. By the end of the course, students will have a well-structured and ethically sound research proposal ready to be presented as the foundation for their doctoral dissertation.

Advanced Research Progress and Progress Review helps a student who has just completed the ‘Research Plan’ to undertake a cadence of supervised, original research leading to a substantial portion of their research thesis being completed (2–4 chapters or equivalent).

Although students may request twice-weekly meetings early in the writing process, it is expected (and students typically prefer) not to meet more than twice per month thereafter. This allows the student time to develop their independent research and writing. As the student advances in independence and confidence in their research, and under the discretion of their supervisor, they may reduce their supervisory meetings to 1 time per month, though they must continue to participate in ‘Work in Progress’ seminars each month.

Under the supervision of their supervisor, and through regular submissions and synchronous feedback sessions, students hone and strengthen their ability to conduct innovative, original research at the very forefront of a technological discipline or professional field.

While the focus of year one was on preparing the ‘Research Proposal’, the focus of year two is on the actual work of research, whether that be in the field, in applied settings, working with datasets, conducting technical design work, or undertaking practice-led inquiry and in-depth analysis of current technology-related developments—producing extensive notes and questions, and rough drafts of chapters or thesis sections.

In addition, supervisors in year two will facilitate the student’s participation in the broader professional and scholarly community, whether through presenting in the ‘Work in Progress’ seminar for graduate students and practitioners, or through recommended and invited academic or industry conferences.

Entering the third year of doctoral studies, students will have a well-defined research topic, a clear structure to organise their proposed research, a firm grasp of the relevant literature, a practical timeline in which to conduct their research, and a substantial body of drafted thesis chapters or sections (equivalent to 2–4 chapters).

In this module, the aims of the methodology module are fulfilled and the research of the past two years is brought into one overarching argument. The exact length of the thesis will vary by method and discipline, but ordinarily will not exceed 80,000–100,000 words, exclusive of any appendices.

The thesis will constitute a substantial, original, independent piece of research, which is clearly articulated in relation to the primary evidence and secondary literature, and which is organised in relation to the plan first envisaged in the methodology module.

Students may select the Integrated Thesis option for their dissertation, which results in a shorter document drawing directly on publishable or published material. The Integrated Thesis option will typically be 20–40,000 words (not longer than 80,000 words without express permission from Woolf), exclusive of appendices and data sets. A doctoral thesis on the Integrated Thesis option may be accepted for examination if it consists of a minimum of three papers of publishable quality, framed by an introduction, a literature survey (either written as a stand-alone chapter or divided among the constituent chapters), and a conclusion. The thesis must represent a contextualised and broadly coherent body of work, justified in the introduction and conclusion. At least one paper must be authored solely by the candidate. Any co- authored papers must include a statement describing the candidate’s contribution to the paper. Where the co-author is another student at Woolf who also intends to include the paper in their own thesis, permission to include the paper must be approved, prior to submission for examination, by the Academic Board of the college, with written notice to Woolf’s Quality Assurance, Enhancement, and Alignment Committee (QAETAC).

Regular supervision meetings keep the student on- course with the timeline agreed in the methodology module. Supervisory meetings concentrate on a pre-submitted piece of research in a pattern that continues until the first draft of the thesis is complete.

Although students may request twice-weekly meetings early in the writing process in module two, it is expected (and students typically prefer) not to meet more than once or twice per month by this stage of the thesis. This allows the student time to develop their independent research and writing. All full-time students must meet with their supervisor at least once per month. Students meeting only once a month must also participate each month in Works-in-Progress seminars. After the completion of the first draft, meetings focus on the harmonisation of the parts, adjustments to the overall argument, and the supervisor seeks to ensure that the student guides the thesis with a single, coherent line of enquiry. The final meetings with the student focus on polishing the editorial aspects of the thesis, and helping the student prepare for examination. While there is not a formal requirement that a piece of the thesis will have been published already, the thesis should contain publishable work, and the student should graduate with a clear plan of revision toward publication (e.g., a series of articles, direct publication, or recasting the argument as a book for a more general audience).