University Digital Twin
A connected view of people, processes, competencies, resources and projects.
The digital twin brings together the state of participants, individual learning pathways, tasks, projects, spaces, equipment and shared resources. Relationships between these elements reveal dependencies that would otherwise remain separated across systems.
This research architecture makes it possible to examine a change, compare its consequences and prepare a decision. EduOS Lad uses the resulting context for analytics, agent support, scheduling and authorised actions.
This article describes a proposed architecture and demonstrations, not a live university deployment.
People
Role · availability · tasks · learning pathway
Current example: A team member working as a data analyst on a modelling task
Relationship: A participant performs work and creates a result
The university as a connected system of states
A student has roles, tasks and learning results. Projects have stages and milestones. Buildings and laboratories have capacity, equipment and availability. Programmes and research processes produce observations and changes.
The digital twin connects these states through relationships and events, making it possible to identify constraints and examine the consequences of a proposed change.
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Object
A person, task, project, programme, resource, space or process.
- 02
Properties
Characteristics describing the object.
- 03
Relationships
How the object connects to other elements.
- 04
Current state
Observed values, statuses and constraints.
- 05
Event
A change affecting the object or its links.
- 06
New state
The updated configuration following an action or external change.
Explore relationships between university objects
The separate University 5.0 environment illustrates objects, events, resources and state changes.
Open digital-twin model ↗Six levels of the digital twin
Objects, relationships, states, events, rules and role-based views form one linked architecture. They must retain clear definitions and traceable relationships.
Objects
A common inventory of university entities.
Result: Common inventory of entities
Objects gain meaning through their connections
The state of one object alone cannot describe its significance. A competence connects to an action, result, context and expert verification. A laboratory connects to timetables, equipment, people, tasks and access rules.
Participates in
A person or team contributes to a task, project or process.
Is accountable for
A role is responsible for a result or decision.
Uses
A process requires time, space, equipment or data.
Depends on
An element needs the output of another.
Creates
An action produces a result, data or new state.
Supports evidence for
An observable result supports a claim of competence.
Constrains
A rule, resource or condition limits permitted actions.
Updates
An event or result changes the state of another object.
Campus energy-reduction project
Select an object to see its state, incoming and outgoing relationships, a possible event and its consequences.
Engineering laboratory
One university across physical and digital layers
Physical, educational, research, project and organisational contexts describe the same institution from different perspectives. The digital twin connects them through objects, events and processes.
Physical
Educational
Research
Projects
Organisational and digital
One laboratory across five layers
Physical layer
A single physical space has different meanings for different people and processes, depending on their tasks and the current state.
How a change travels through the system
Events change an object and may affect connected roles, equipment, learning activities and project decisions. Understanding the cascade helps compare feasible responses before an authorised change.
Resource
Equipment temporarily becomes unavailable.
Educational
Assessment criteria for a project change.
Staffing
An expert joins a project for a limited period.
Project
A client clarifies the desired outcome.
Research
New data challenge the original hypothesis.
Infrastructure
A classroom closes for maintenance.
Regulatory
Rules for handling data change.
External
A new technological capability becomes available.
Example: the engineering laboratory is unavailable on the planned day
Affected object
Engineering laboratory; its availability status changes.
These are illustrative alternatives, not approved changes to an actual timetable.
A digital twin of an individual learning pathway
An individual pathway combines verified outcomes, roles, current tasks, goals, evidence, experience and available next actions. Its state connects to team work and institutional resources.
Example: a participant in an energy modelling project
Current state
Choose an event
The scenario model is successfully defended
The example shows possible changes to a pathway; competence confirmation remains subject to evidence and review.
When three processes need one laboratory
Time, space, equipment, expert availability and access form a connected resource model. The priorities used to allocate them change the resulting timetable.
State of a resource
Resource types
Learning session
18 students · this week's deadline · basic equipment · linked to module completion
Research experiment
Four researchers · two available days · specialised rig · linked to publication work
Project testing
Seven team members · before partner meeting · instruments and sensors · feeds pilot decision
Select an allocation priority
Suggested order
- Learning session
- Project testing
- Research experiment
The allocation is a proposal. An authorised person checks and approves the change before any resource or schedule is modified.
A map of research and project dependencies
Research produces methods and data; educational modules develop competencies; project prototypes test solutions. Their practical results can inform new research questions and decisions.
Project state
Research state
Campus energy-reduction system
Research
Modelling the factors behind building energy consumption
One institution, different views of its state
A student's information needs differ from those of a lecturer, programme leader or university management. Permissions must be based on roles and tasks, not merely on whether data exist.
Visible to: Student
Illustrative visibility only; actual access also requires authorisation under the applicable rules.
Where state descriptions and decisions come from
Each element should have a documented source, owner, update time, purpose and permitted scope. To verify a conclusion it must be possible to trace the evidence and decisions on which it depends.
Provenance record
Sources
Trace an illustrative conclusion
Laboratory timetable
Equipment condition
Owner approval
Access rules
Project booking
Derived availability
Selected: Laboratory timetable. These descriptions are examples; no live operational timestamp is asserted.
Data quality must be assessed for currency, completeness, consistency, verifiability, fitness for purpose and provenance.
How agents use an authorised digital-twin view
A specialist AI agent uses only the data and authority needed for its assigned function. Its output is a suggestion, observation or prepared action passed to a responsible participant.
Navigator
Uses:
Produces: Options for the next action
Tutor
Uses:
Produces: Contextual learning support
Analyst
Uses:
Produces: Observations and possible interpretations
Coordinator
Uses:
Produces: Coordinated work plan
Resource agent
Uses:
Produces: Alternative resource allocations
Governance agent
Uses:
Produces: Approval route and permitted actions
Creating an interdisciplinary research laboratory
A hypothetical university considers a new laboratory for the study of complex socio-technological systems. The digital twin supports planning and the decisions of authorised participants.
Stage 1 of 12
Initiative
A research group and an educational programme propose a laboratory for studying complex socio-technological systems.
What is required for a university digital twin
The twin requires compatible entity definitions, evidence and source history, event-based updates and explicit authorisation for access and decisions.
- 01
Shared entity model
People, tasks, competencies, projects, resources and processes have consistent definitions.
- 02
Stable identifiers
Each object and result can be linked to its history and data sources.
- 03
Defined relationships
Participation, responsibility, dependency, use and evidence are specified.
- 04
Event-based updates
Changes to objects and processes are recorded as events and produce updated states.
- 05
Data governance
Each element has a source, purpose, access boundary and responsible role.
- 06
Role-based views
Participants see information relevant to their tasks and authority.
- 07
Observable decisions
The grounds, participants, actions and results of significant decisions remain traceable.
A connected and observable model of University 5.0
- 01
Connected model
People, tasks, projects, competencies, spaces and resources use compatible entities and relationships.
- 02
Current state
The model represents status, availability, load, results and constraints.
- 03
Change history
Events, decisions and new states connect to previous versions.
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Dependency analysis
A change in one object can be followed through related processes and resources.
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Scenario-based decisions
People compare permitted alternatives and their implications before changing a process.
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Role-based views
Each participant sees information appropriate to their responsibilities and authority.
- 07
Agent support
AI agents use authorised current state for navigation, analysis, coordination and resource proposals.
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Renewal through results
Completed actions update the model and create context for the next cycle.
The proposed digital twin connects the condition of education, research, projects and resources to observable actions and accountable decisions. Results change the twin's state and provide context for subsequent work.
About the model
The University Digital Twin is being developed by Sergey Shatokhin within the IWXA laboratory as part of University 5.0 and EduOS Lad. It connects event-driven processes, individual pathways, competencies, resources, projects, AI agents and governed decisions.