Doctor of Technology in Artificial Intelligence

Introduction

Doctor of Technology in Artificial Intelligence

The Doctor of Technology degree is designed for experienced professionals and senior practitioners in fields involving advanced technology, innovation, and digital transformation. It is suited for those aiming to address complex, real-world challenges by integrating applied research into their practice. The programme supports individuals who aspire to bridge the gap between technological theory and practice, producing knowledge that can influence policy, business operations, and industrial strategy. It provides training for those seeking advanced professional expertise, particularly in leadership roles that require strategic decision-making grounded in research and evidence-based practice.

This Doctor of Technology qualification is aimed at professionals seeking to advance within leadership, strategic innovation, or policy advisory roles in technology-driven organizations. Graduates will be positioned for high-level roles such as Chief Technology Officer (CTO), Director of Innovation, or Heads of R&D, where research-informed decision-making and strategic foresight are essential.

1. A doctorate program has no grades, and no credits – but it does have a three-part structure with checkpoints and a final viva voce examination.
2. Academic Staff are called Faculty members at Woolf and must possess a doctorate meeting the EQF Level 8 qualification. They are expected to have a record of peer-reviewed research. All teaching and instructional design at Woolf is under the authority and oversight of an academic staff member. In cases where pre-recorded lectures or podcasts are provided that contain content from outside of Woolf, any such content is to be produced by lecturers who are experts with a research doctorate in the relevant domain, or where relevant, with at least 5 years of industry-specific experience.
3. The programme is 4500 contact hours delivered online, designed for full-time study.

Key Patterns

1. Meetings: 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.
2. Learning Methods: Synchronous sessions with instructor; other contact hours by activities under the direction and supervision of an instructor; scholarly articles and study materials in the VLE; asynchronous lectures; communication by chat forum.
3. Deliverables: Students complete a comprehensive Research Plan in Module 1, progress through structured research activities in Module 2, complete and defend their thesis in Module 3, and complete thematic coursework modules in Years 1-3.
4. Duration of Courses: Module 1 (2000 hours), Module 2 (1000 hours), Module 3 (1500 hours), AI Coursework Modules 4-8 (400 hours each).
5. Overall Structure and Evidence Required for Progression

The Doctor of Technology comprises three core doctoral research modules (Advanced Research Planning, Advanced Research Progress, and Thesis Completion), which form the foundation of the doctorate. These are complemented by five specialized AI coursework modules (Research Methods, Disruptive Technology, AI Technology and Knowledge Practices, Governance and Ethics, and Doctoral Thesis Writing) that extend professional expertise in technology leadership and innovation.

Year One: Research Methods in AI and Technology Management

This course is designed to equip doctoral students with a robust foundation in research methodologies specifically suited for the rapidly evolving domain of Artificial Intelligence and Technology Management. The module emphasizes both the theoretical frameworks and practical application of research design in the context of emerging technologies, enabling students to develop sophisticated analytical approaches to address complex, real-world technological challenges while maintaining rigorous scholarly standards.

Assets to log evidence:

• Completion of scenario-based assignments exploring research paradigms in AI
• Development of a research methodology proposal aligned with the student's dissertation focus
• Submission of critical evaluations of different methodological approaches to AI research

Learning Outcomes

1. Develop an applied research-based approach to AI and technology management, integrating knowledge from emerging practice-based and scholarly research
2. Apply advanced research methodologies specifically suited to artificial intelligence and technology domains, demonstrating competence in both quantitative and qualitative approaches
3. Design rigorous research protocols that adhere to standards of research ethics, compliance, and responsible AI governance
4. Evaluate emerging methodological paradigms relevant to AI, making informed choices about research strategy

Year Two: Disruptive Technology Management, Innovation and Leadership

In today’s dynamic and technology-driven global environment, organizations face constant disruption from emerging technologies that reshape industries, market structures, and competitive landscapes. This module prepares doctoral researchers and technology leaders to analyze, strategize, and lead through periods of significant technological change. It develops critical competencies in identifying disruptive forces, evaluating innovation opportunities, and leading organizational transformation in the context of rapid technological advancement.

Assets to log evidence:

• Scenario-based assignments analyzing technological disruption in specific sectors
• Innovation planning exercises addressing real-world technology challenges
• Strategic case studies on leading through technological change

Learning Outcomes

1. Analyze and evaluate disruptive technologies and their impact on organizational strategy, market dynamics, and industry evolution
2. Develop strategic frameworks for anticipating, evaluating, and managing technological disruption in technology-driven organizations
3. Design innovation strategies that balance technological opportunity with organizational capability and risk mitigation
4. Demonstrate advanced leadership competencies in guiding organizational transformation through technological change

Year Three: AI Technology and Knowledge Practices Based Research

In an age defined by exponential technological growth, artificial intelligence plays a pivotal role in reshaping how knowledge is created, managed, and applied across sectors. This module engages doctoral researchers with the epistemological and methodological dimensions of knowledge creation in AI-enabled environments. Students develop sophisticated understanding of how AI technologies influence research practices, knowledge generation, and professional expertise across domains, positioning them to lead evidence-based practice in AI-intensive fields.

Assets to log evidence:

• Exploratory assignments on epistemological frameworks in AI research
• Reflective critiques on knowledge creation in AI-enabled systems
• Analysis of AI’s impact on professional practice and expertise
• Scholarly essays integrating epistemology with practical AI application

Learning Outcomes

1. Critically examine how AI and emerging technologies reshape knowledge creation, validation, and application within professional domains
2. Develop sophisticated understanding of epistemological frameworks suited to research conducted in AI-enabled environments
3. Design knowledge practices that integrate AI capabilities with human expertise and scholarly rigor
4. Evaluate the implications of AI-driven knowledge systems for professional judgment, decision-making, and practice

Full Details of Doctor of Technology in Artificial Intelligence

Entry Requirements

1. Education Requirements

An EQF Level 7 qualification (e.g., MSc, MTech, or equivalent) in a relevant field such as Engineering, Computer Science, Information Technology, Management, or other technology-related disciplines. Alternatively, applicants with an EQF Level 7 qualification in any discipline may be considered if they demonstrate significant professional experience in technology innovation, implementation, or leadership, together with evidence of professional practice aligned with technology management or strategic implementation and a clear statement of motivation and research interest relevant to applied technological inquiry.

For the Doctor of Technology in Artificial Intelligence specialisation, applicants must additionally demonstrate familiarity with AI tools or practices through academic coursework in artificial intelligence, machine learning, data science, or related areas, or through professional experience involving AI systems, predictive analytics, algorithmic tools, or data governance. A proposed research interest aligned with strategic deployment of AI in enterprises, governance and ethics of AI implementation, or industry-specific AI use cases (e.g., supply chain, CRM, smart manufacturing) is required.

1. Language Requirements

The language of instruction is English or Spanish. English-language proficiency must be demonstrated at C1 level, evidenced by completion of an EQF Level 7 degree taught in English; IELTS overall 6.5 (with at least 6.0 in each component); TOEFL; Cambridge English Advanced (CAE); or equivalent. For Spanish-medium study, a DELE score of C1 or higher is required.

1. Transfer of Progress from Other Programmes

Applicants transferring from an accredited doctoral programme (e.g., D.Tech, Doctor in Artificial Intelligence, PhD in Natural or Mathematical Sciences, or related discipline) may be eligible for credit transfer and a reduced study duration. This will be assessed on a case-by-case basis and formally included in any offer of admission.

1. Duration of Programme

Full-time students complete the programme in a minimum of 3 years, with the possibility of extending by petition to the Dean of College up to a maximum of 6 years. Part-time students may take up to 8 years, engaging one module every two years. All individual schedules are determined in consultation with the student's supervisor.

Assessment

The Doctor of Technology is assessed on a pass/fail basis. There are no letter grades or credit points. Each module is assessed through a combination of regular assignments (contributing 20% of the module mark) and a final summative assignment (contributing 80%). Regular assignments consist of iterative tasks that progressively build competence within the module's domain. The final assignment is a major piece of work specific to the module — such as a research proposal, strategic innovation plan, or conceptual framework paper — expected to be of a high standard, well-structured, well-crafted, and supported by extensive and appropriate citations. Module 1 requires at least four regular assignments and one final assignment; Modules 4–8 require at least two regular assignments and one final assignment. Failed modules may be re-sat within a timeframe determined by Woolf Academic Staff after individual evaluation.

The Viva Voce Examination

Upon submission of the completed doctoral dissertation, students are required to undergo a viva voce examination. The dissertation is submitted for assessment by at least two external examiners, including at least one from outside Woolf, both of whom must hold research doctorates in the relevant field. The viva voce is scheduled following the examiners' preliminary review, and the student presents and defends the dissertation orally before the examination panel.

Possible outcomes are: award of the Doctor of Technology in Artificial Intelligence (EQF 8); award of the degree with minor or major corrections required; referral for further work; or, in cases where the student does not progress to the final dissertation phase, an exit award of Master of Philosophy (M.Phil, EQF 7). Students may dispute examination outcomes via Woolf's standard Red Flag and appeal procedures.

Internships policy

Internships must be a genuine extension of the student’s academic programme, providing opportunity to apply theoretical knowledge to substantive projects directly related to their field of study. Internships consisting primarily of administrative or routine tasks will not be approved.

Every internship must have a defined start date, end date, and formal learning plan with objectives agreed in advance by the student, the host organisation, and the relevant college. Responsibilities and task complexity should increase over time. Each student must be assigned a named supervisor within the host organisation who holds relevant expertise and is responsible for providing regular guidance and feedback.

Woolf prioritises paid internships to ensure equitable access regardless of socioeconomic background. Unpaid internships may only be approved where they constitute a genuine learning opportunity and do not displace the work of a paid employee.

Programmatic standards

Day-to-day management sits with the relevant college. Each college must have a designated Woolf contact responsible for vetting and approving all host organisations and placements before any internship may proceed. Colleges are responsible for matching students to approved positions.

Students must complete pre-internship preparation before commencing a placement, which may include CV writing, interview support, and other instruction as necessary. Virtual internships are encouraged to widen access beyond geographical constraints; support systems must address the challenges of remote work, including cross-timezone communication and fostering professional belonging.

Programme effectiveness must be evaluated on an ongoing basis. Formal evaluations will be collected from students, host supervisors, and academic advisors, and will inform curriculum design and programme improvement.

Grading Scheme

General Marking Criteria and Classification

Marking of student work keeps in view the scale of work that the student can reasonably be expected to have undertaken in order to complete the task.

The assessment of work for the course is defined according to the following rubric of general criteria:

1. Engagement:
   • Directness of engagement with the question or task
   • Range of issues addressed or problems solved
   • Depth, complexity, and sophistication of comprehension of issues and implications of the questions or task
   • Effective and appropriate use of imagination and intellectual curiosity
2. Argument or solution:
   • Coherence, mastery, control, and independence of work
   • Conceptual and analytical precision
   • Flexibility, i.e., discussion of a variety of views, ability to navigate through challenges in creative ways
   • Completion leading to a conclusion or outcome
   • Performance and success of the solution, where relevant
3. Evidence (as relevant):
   • Depth, precision, detail, range and relevance of evidence cited
   • Accuracy of facts
   • Knowledge of first principles and demonstrated ability to reason from them
   • Understanding of theoretical principles and/or historical debate
   • Critical engagement with primary and/or secondary sources
4. Organisation & Presentation:
   • Clarity and coherence of structure
   • Clarity and fluency of writing, code, prose, or presentation (as relevant)
   • Correctness of conformity to conventions (code, grammar, spelling, punctuation, or similar relevant conventions)

Definition of marks

Indicative equivalence table

Synchronous Adjustments Template

Synch discussions may affect the mark on submitted assignments: written work is submitted in advance, and a discussion follows. This provides students an opportunity to clarify and explain their written claims, and it also tests whether the work is a product of the student’s own research or has been plagiarised.

The synchronous discussion acts to shift the recorded mark on the submitted assignment according to the following rubric:

+3

Up to three points are added for excellent performance; the student displays a high degree of competence across a range of questions, and excels in at least one group of criteria. Relative weaknesses in some areas may be compensated by conspicuous strengths in others.

+/- 0

The marked assignment is unchanged for fair performance. Answers to questions must show evidence of some solid competence in expounding evidence and analysis. But they will be marred by weakness under one or more criteria: failure to discuss the question directly; appeal to irrelevant information; factual error; narrowness in the range of issues addressed or evidence adduced; shortage of detailed evidence; or poor organisation and presentation, including consistently incorrect grammar. Answers may be characterised by unsubstantiated assertion rather than argument, or by unresolved contradictions in the argument.

• 3 (up to three points)

Up to three are subtracted points for an inability to answer multiple basic questions about themes in the written work. Answers to questions will fall down on a number of criteria, but will exhibit some vestiges of the qualities required, such as the ability to see the point of the question, to deploy information, or to offer some coherent analysis towards an argument. Such qualities will not be displayed at a high level or consistently, and will be marred by irrelevance, incoherence, error and poor organisation and presentation.

0 (fail)

Written work and the oral examination will both be failed if the oral examination clearly demonstrates that the work was plagiarised. The student is unfamiliar with the arguments of the assignment or the sources used for those arguments.

Plagiarism

Plagiarism is the use of someone else’s work without correct referencing. The consequence of plagiarism is the presentation of someone else’s work as your own work. Plagiarism violates Woolf policy and will result in disciplinary action, but the context and seriousness of plagiarism varies widely. Intentional or reckless plagiarism will result in a penalty grade of zero, and may also entail disciplinary penalties.

Plagiarism can be avoided by citing the works that inform or that are quoted in a written submission. Many students find that it is essential to keep their notes organised in relation to the sources which they summarise or quote. Course instructors will help you to cultivate professional scholarly habits in your academic writing.

Depending on the course, short assignment essays may not require students to submit a bibliography or to use extensive footnotes, and students are encouraged to write their assignments entirely in their own words. However, all essays must acknowledge the sources on which they rely and must provide quotation marks and citation information for verbatim quotes.

There are several forms of plagiarism. They all result in the presentation of someone’s prior work as your new creation. Examples include:

• Cutting and pasting (verbatim copying)
• Paraphrasing or rewording
• Unauthorised Collaboration
• Collaboration with other students can result in pervasive similarities – it is important to determine in advance whether group collaboration is allowed, and to acknowledge the contributions or influence of the group members.
• False Authorship (Essay Mills, Friends, and Language Help)
• Paying an essay writing service, or allowing a generous friend to compose your essay, is cheating. Assistance that contributes substantially to the ideas or content of your work must be acknowledged.

Complaints and appeals

Students and faculty should always seek an amicable resolution to matters arising by addressing the issue with the person immediately related to the issue. Students should handle minor misunderstandings or disagreements within a regular teaching session or by direct message, or with their College. If a simple resolution is not possible, or the matter remains unresolved for one party, the steps outlined in this section apply to all groups, colleges, and units of Woolf.

The Red Flag system

An issue with a red flag should be submitted in the case that a member of Woolf seeks to make an allegation of serious misconduct about another member, including matters of cheating, plagiarism, and unfair discrimination or intolerance.

Any member of Woolf, seeking to raise a matter of serious concern, should submit a red flag by emailing redflag@woolf.education. Provide a short, clear description of the issue.

If a student submits an issue with a red flag, or if a faculty member submits an issue about a student, it will trigger a meeting with the student’s College Advisor. If the issue is not resolved, the matter will be escalated to the College Dean, or to a committee designated by the College Dean, which will have the power to clear the flag.

If an issue is submitted with a red flag by a faculty member about another faculty member, then the issue is reported directly to the College Dean.

For both students and faculty members, after the Dean’s decision, the one who submits the complaint is provided the opportunity to accept or appeal the decision; if the one submitting the issue appeals the decision, it will be assigned to the Quality Assurance, Enhancement, and Technology Alignment Committee, which is a subcommittee of the Faculty Council.

Mitigating circumstances

When serious circumstances (‘Mitigating Circumstances’), beyond the control of a student or faculty member, adversely affect academic performance or teaching support, a Mitigating Circumstances report must be submitted using Woolf’s red flagging system. Mitigating Circumstances may include but are not limited to serious medical problems, domestic and personal circumstances, major accidents or interruptions of public services, disturbances during examination, or serious administrative or procedural errors with a material effect on outcomes.

Mitigating circumstances do not normally include a member’s personal technology problems, including software, hardware, or personal internet connection failures; employment obligations or changes in employment obligations; permanent or sustained medical conditions (unless there is a sudden change of condition); or circumstances where no official evidence has been submitted.

Mitigating circumstances are normally only considered when a red flag has been submitted for the issue before the deadline of an affected written project or assignment, or within one week of a cumulative examination. Proof of mitigating circumstances may result in an extended deadline or examination period, or the possibility to retake an examination; it will not result in any regrading of existing submissions or exams.

Grade appeals

Students who dissent from the grades they have received should follow the normal procedure for submitting a red flag.