Day 1 | Pre-Event: Doctoral-level work in practice | Fri. 10 May | 09:00–13:00
Learn from those that went through the doctoral journey and use the moment to discuss research ideas for your doctoral project
A live, unscripted demonstration of doctoral-level reasoning led by senior DBA graduates and faculty, coupled with reflective feedback on presented research ideas.
Day 1 | Scene Setting | Fri. 10 May | 13:00–18:00
Shared Diagnosis: What is Inhibiting Scale in Doctoral Education?
Objective: Establish a shared, evidence-based understanding of the systemic, institutional, and individual shortcomings of current doctoral education, as a foundation for innovation and transformation.
13:00–13:45 | Opening Plenary
Speaker: Prof. Dr. Kevin Boeh, DoctorateHub Vice Chancellor, United States
Why Innovation and Transformation in Doctoral Education Are Needed
- The evolving role of doctoral education in research, innovation, and society
- Increasing complexity: interdisciplinarity, professional doctorates, global cohorts, AI
- Why legacy doctoral models no longer work, and what graduate schools still lack
- Positioning this residency as a constructive, forward-looking design space
Outcome: Participants align on purpose, scope, and a shared vocabulary for the days ahead.
14:00–15:30 | Plenary Session
System-Level Shortcomings in Doctoral Education
Focus areas:
- The doctorate as a Black-Box where only the start and end point are clearly defined
- Lack of commonly articulated progression points and milestones
- Tension between claims of research uniqueness and implicit structural expectations
- Consequences for student experience, supervision, and quality assurance
Outcome: A shared map of systemic limitations that constrain progress and innovation.
15:30–15:45 | Break
15:45–17:15 | Plenary Discussion
Institutional and Individual Shortcomings
- Institutional incentive structures and competing priorities
- Supervision as an under-designed academic responsibility
- Variability in supervisory preparation and pedagogical confidence
- How individual practices are shaped by institutional context
Outcome: Clear distinction between individual effort and institutional responsibility.
17:15–18:00 | Synthesis Session
What Needs to Change to Enable Transformation in Doctoral Education
- What are the different barriers that must be overcome?
- Where institutions can realistically intervene
- Which assumptions about doctoral education deserve re-examination
Outcome: A shared agenda for innovation that informs Days 2–4.
Day 2 | Designing Alternatives | Sat. 09 May | 09:00–15:00
Future-Ready Doctoral Education: Models and Frameworks
Objective: Demonstrate that alternatives exist and explore how DoctorateHub / GSBM designs doctoral systems differently.
09:00–09:15 | Review Day 1 and Open Day 2
Speaker: Prof. Dr. Kevin Boeh, DoctorateHub Vice Chancellor, United States
09:15–10:45 | System-Level Design
Moderator: Dr. Andreas Meiszner, DoctorateHub PVC for Learning, Germany
Structured, Modular, Scalable Doctoral Education
This workshop starts with a short scene setting showcasing existing or emergent solutions around the following:
- The 9-stage DBA model as a worked example
- Planned extensions vs. silent drift
- Modular, granular, and stackable systems
Outcome: Participants can sketch future-ready doctoral architectures.
10:45–11:00 | Break
11:00–12:30 | Individual-Level Design
Feedback that works
This workshop starts with a short scene setting showcasing existing or emergent solutions around the following:
- Predictable supervision, assessment, and feedback loads
- The role of language and communication
- Theoretical and abstract – versus – concrete and applicable
Outcome: Supervision understood as a system, not a personality.
12:30–13:30 | Lunch
13:30–15:00 | Institutional-Level Design
From Lone Supervisor to Supervision Village
This workshop starts with the results from the system- and individual-level design points and how these can be applied at the institutional level, considering inter-alia:
- Opening the black box: towards transparent doctoral journeys
- The Village Model of doctoral research support and training
- Accountability, conflict resolution, and quality control
Outcome: Explicit design criteria for modern doctorates.
15:00–21:00 | Social Event
Sightseeing Tour, informal networking, and partnership conversations
Day 3 | Competence Development | Sun. 10 May | 09:00–18:00
Hands-on supervisory training in times of AI-mediated research and research education
Objective: Build concrete supervisory and methodological capability across the full research lifecycle, with AI as a central—but controlled—tool. This is not a lecture about AI. This is a working day where you will use AI, critique AI, and leave with both sharper skills and a shared institutional stance on what responsible AI-mediated supervision looks like.
09:00–09:15 | Review of Day 2 and Opening of Day 3
Moderator: Dr. Jim Wagstaff, GSBM Faculty, Educator & AI Innovator, Singapore
Calibration: Where Are We Starting From?
AI experience in the room will vary. Before we can build together, we need to know what we’re working with.
- Hands-on pulse check: Everyone completes the same task in ChatGPT, Claude, etc.
- What did you notice? What surprised you?
- Framing the day: You will experience AI as your candidates will, then judge it through the lens and experience that a supervisor must
Outcome: A shared baseline of experience. No assumptions. No one left behind or held back.
09:15–10:45 | Applied Lab I
AI Across the Research Lifecycle
Moderator: Dr. Jim Wagstaff, GSBM Faculty, Educator & AI Innovator, Singapore
Your doctoral candidates will use AI. The question is not whether but how—and whether they will use it well or badly. This lab puts you in the shoes of the doctoral candidates.
Station A: AI and the Literature (30 min)
- Tools: NotebookLM, Claude, ChatGPT, etc.
- Upload papers. Generate synthesis. Query across sources. Create an audio overview.
- The hard question: What does AI surface that you might have missed—and what does it flatten, oversimplify, gloss over, or get wrong?
Station B: AI and Qualitative Analysis (30 min)
- Tools: Claude, ChatGPT, NotebookLM
- Feed in interview transcripts. Ask for thematic coding. Request pattern identification.
- The hard question: How does AI’s coding compare to your instincts? What would you accept, revise, or reject?
Station C: AI and Quantitative Analysis (30 min)
- Tools: Claude, ChatGPT, Code Interpreter
- Work with real data outputs—regression tables, survey results
- The hard question: Could a candidate use this to understand their data better—or to avoid understanding it altogether?
Outcome: Direct experience with AI’s capabilities and limitations. Initial instincts about where AI helps and where it misleads.
10:45–11:00 | Break
11:00–12:00 | Applied Lab II
Moderator: Dr. Jim Wagstaff, GSBM Faculty, Educator & AI Innovator, Singapore
AI, Mixed Methods, and the Integration Problem
Mixed methods research is where AI gets interesting—and dangerous. Integration requires judgement that AI can simulate but not possess.
- Scenario: A candidate has qualitative themes AND quantitative survey data on the same phenomenon
- Task: Use AI to explore integration—joint displays, convergence and divergence, narrative synthesis
- The hard question: Where might AI manufacture false coherence? What must a supervisor probe?
Outcome: Understanding of AI’s potential and pitfalls in the most complex methodological territory your candidates will navigate.
12:00–12:30 | Supervisor Reflection
Moderator: Dr. Jim Wagstaff, GSBM Faculty, Educator & AI Innovator, Singapore
When to Say: “Read the F***ing Papers”
Now put on your supervisor hat. Review outputs from the morning—yours or a colleague’s—as if a candidate submitted them.
- What would you want to know about how this was produced?
- What capability must the candidate still demonstrate themselves?
- What’s your green light, yellow light, red light?
Human judgement is the non-automatable core of supervision. This session is about locating exactly where that judgement must be applied.
Outcome: The shift from experiencing AI to supervising its use. Surfaced tensions that will inform the afternoon.
12:30–13:30 | Lunch
13:30–14:15 | Data Clinic
Moderator: Dr. Jim Wagstaff, GSBM Faculty, Educator & AI Innovator, Singapore
Ethics, Integrity, and Real-World Data Problems
AI does not solve the fundamental problems of research integrity—it complicates them. This session addresses what supervisors must enforce regardless of the tools candidates use.
- Data management failures: What goes wrong, and why it matters
- Auditability and reproducibility: Can you trace how the results were produced?
- AI-assisted data collection: Code generation, scraping, survey automation—where are the risks?
- Institutional responsibility: What must the institution guarantee, and what falls to the supervisor?
Outcome: Clear understanding of the ethics and integrity issues that AI amplifies but does not create.
14:15–15:00 | Writing & Integrity Lab
Moderator: Dr. Jim Wagstaff, GSBM Faculty, Educator & AI Innovator, Singapore
AI-Supported Writing Without Academic Fraud
The difference between editing and authoring matters. So does the difference between using AI to clarify your thinking and using it to avoid thinking altogether.
- Editing vs. authoring: Where is the line?
- Detecting over-automation: What does AI-written prose look like, and why does it matter?
- Making authorship defensible: What documentation and transparency should candidates provide?
Hands-on exercise:
- Use AI to draft, summarise, or translate findings for non-academic audiences
- Compare: What happens when AI drafts versus when AI refines your draft?
- Pair critique: Swap outputs. Score on accuracy, insight, originality, voice.
Outcome: Practical capability in AI-supported research communication. A critical eye for detecting over-automation and defending authorship.
15:00–15:15 | Break
15:15–16:30 | Synthesis Workshop
Moderator: Dr. Jim Wagstaff, GSBM Faculty, Educator & AI Innovator, Singapore
What Supervision Must Now Enforce
AI will be used regardless of policy. The question is whether your institution has a defensible, transparent stance—or leaves every supervisor to figure it out alone.
Gallery walk (15 min): Review the tensions and insights surfaced throughout the day.
Sorting exercise (35 min): Working in groups, categorise AI use across the research lifecycle:
Research Phase | Encouraged | Permitted with Transparency | Discouraged or Prohibited |
Literature synthesis | | | |
Qualitative coding | | | |
Quantitative interpretation | | | |
Mixed methods integration | | | |
Data collection & management | | | |
Writing & communication | | | |
Draft guidance (25 min): Together, we will produce a working draft of AI guidance for supervision: what candidates should be told at the start of their journey, and what supervisors must enforce throughout.
Outcome: A co-created institutional asset. Not imposed from above—built from shared experience.
16:30–17:30 | Presentation of Findings
From Working Groups to Shared Perspective
Moderator: Dr. Jim Wagstaff, GSBM Faculty, Educator & AI Innovator, Singapore
Each group presents their draft guidance to the full room.
- Where do we align?
- Where do we differ—and why?
- What tensions remain unresolved?
This is not about forcing consensus. It is about making differences visible so faculty can make deliberate choices rather than drift into inconsistency.
Outcome: A consolidated draft of AI-related guidance for doctoral supervision: co-created, debated, and owned by the people who will use it.
17:30–18:00 | Closing
Moderator: Dr. Jim Wagstaff, GSBM Faculty, Educator & AI Innovator, Singapore
Commitments: What Will You Do Differently?
- Which tools and use cases were most valuable? Least?
- One thing you will try with your next supervisee
- One thing you will watch out for
- Who owns the draft guidance? When do we revisit it?
Outcome: Personal accountability. Institutional ownership. A path forward.
Exit Condition
Participants leave Day 3 with:
- Experience using AI tools across the full doctoral research lifecycle—not watched someone else use them;
- Sharpened judgement about when AI helps candidates learn and when it lets them avoid learning;
- Clear understanding of ethics, integrity, and auditability requirements in AI-mediated research;
- A co-created guidance document that makes guidance explicit and useful;
- Personal commitments to supervise differently in an AI-mediated world.
What to Bring
- A laptop with internet access
- A Google account (for NotebookLM and Gemini)
- Access to ChatGPT, Gemini, Perplexity, Claude, etc. (free tiers work; paid tiers unlock more capabilities)
- Willingness to experiment, make mistakes, and think critically
Day 4 – Action Planning | Mon. 11 May | 09:00–13:00
Implementation and Partnerships
Objective: Translate insight into concrete institutional and partnership actions.
09:00–09:15 | Review Day 2 and opening of Day 3
Speaker: Prof. Dr. Kevin Boeh, DoctorateHub Vice Chancellor, United States
09:15–10:45 | Strategy Session
Recruiting Strategies
- Doctoral Student Recruitment: Quick wins and recruitment via the network
- Doctoral Faculty Training Programme: How to take this further in scale and scope? Training supervisors at scale
- What else should we be doing?
Outcome: Realistic implementation and growth pathways.
10:45–11:00 | Break
11:00–12:30 | Partnership Lab
Building Doctoral Education Ecosystems
- Universities, industry, regulators, NGOs
- What partners desire and require
- Governance, risk, and power dynamics
Outcome: Partnership ideas grounded in reality.
12:30–13:00 | Closing Plenary
What Will You Change Next Week?
- Key lessons
- Individual and institutional commitments
- Formal close
13:00–onwards | Networking Session
Present what you do and what you look for – for prospective doctoral students, academic partners, or organisations interested in doctoral level education
- Participants connect around projects and subject of interest
- Brainstorming on future projects
- Partnership Building
Exit Condition
Participants leave not just inspired, but with a sense of obligation:
- with clearer responsibility as supervisors and institutions;
- with concrete design and training tools;
- and with partnerships to pursue.
This residency exists to stop the reproduction of known doctoral failures and to replace them with deliberate, transparent, future-ready doctoral education.
