capability patterns for higher education & research institutions
Joe Corneli
These aren't peeragogy patterns per se but maybe useful!
1. Subtle narrative patterns
IF your institution hosts scholars who analyse cultural narratives
HOWEVER they rarely collaborate with researchers trained in formal narratology
THEN strengthen your capacity to recognise subtle narrative patterns
BY jointly investigating how cross-rank conversations shape institutional sensemaking
BECAUSE cultural and structural narrative lenses together reveal hidden institutional dynamics.
2. Shared meaning through dialogue
IF leaders seek to understand research culture
HOWEVER they lack deep access to student and staff meaning-making
THEN enhance your capacity for collaborative meaning construction
BY studying how staff–student narratives co-produce culture
BECAUSE meaning is a relational artefact, not a managerial asset.
3. Institutions as dynamic systems
IF your clinical or practice-based teams generate rich interactional data
HOWEVER the institution treats these as local operational moments
THEN expand your capacity to sense institutional dynamics
BY analysing conversational breakdowns in real work settings
BECAUSE frontline practices often reveal system behaviour earlier than policy does.
4. Situating practices historically
IF institutional decisions feel abrupt or ungrounded
HOWEVER their historical lineage remains unexamined
THEN cultivate capacity for historical situatedness
BY filming and analysing reflective conversations as documentary memory
BECAUSE institutions think in temporal arcs even when acting in the present.
5. Attending to learning journeys
IF doctoral pedagogy is redesigned top-down
HOWEVER students’ own accounts of change are poorly attended to
THEN build capacity to track learning narratives
BY studying doctoral self-narration across disciplines
BECAUSE learners often see capabilities long before frameworks do.
6. Understanding behaviour through empirical analysis
IF evidence is discussed abstractly
HOWEVER real behavioural patterns remain unanalysed
THEN strengthen capacity for empirical understanding
BY comparing statistical patterns with researchers’ epistemic norms
BECAUSE evidence is only meaningful when aligned with lived reasoning.
7. Identifying structural conditions
IF you deploy AI or data tools
HOWEVER you cannot articulate the structural constraints they embed
THEN build capacity to identify enabling conditions
BY investigating explainability requirements across technical and legal domains
BECAUSE structure determines what freedoms institutions actually have.
8. Synthesising complex information
IF automation narratives proliferate
HOWEVER institutions process them only rhetorically
THEN improve capacity for synthesis
BY analysing how automation narratives shape actual uptake
BECAUSE complexity becomes actionable only when stories and systems meet.
9. Evidence-informed evaluation of professional practice
IF professional development is described qualitatively
HOWEVER the actual digital practices remain unmeasured
THEN deepen capacity for evidence-informed evaluation
BY analysing AI tool-use patterns and wellbeing indicators
BECAUSE practice improves when reflection and data meet.
10. Envisioning alternative futures
IF digital adoption is seen as incremental
HOWEVER future infrastructures remain unimagined
THEN expand capacity to envision futures
BY mapping propagation of digital tools across space and groups
BECAUSE futures become real when models make them visible.
11. Designing systemic interventions
IF sustainability efforts remain siloed
HOWEVER data-driven modelling and ethics remain disconnected
THEN develop capacity for systemic design
BY co-designing resilient sustainability transitions with AI insight
BECAUSE resilience emerges when systems and values co-evolve.
12. Shaping shared wellbeing environments
IF campus or spatial planning is functionalist
HOWEVER wellbeing insights from health sciences stay peripheral
THEN enhance capacity to design wellbeing environments
BY bringing planning and health research into participatory design
BECAUSE spaces shape flourishing.
13. Creating new organisational arrangements
IF ageing populations are treated as service users
HOWEVER they possess unique design insight
THEN grow capacity for organisational co-experimentation
BY co-creating digital interventions for ageing communities
BECAUSE institutions thrive when all contributors shape their environments.
14. Exploring regulatory pathways
IF innovation outruns governance
HOWEVER legal insight gets bolted on retroactively
THEN build capacity for regulatory exploration
BY mapping regulatory options for AI-enabled research
BECAUSE responsible innovation begins before rules are written.
15. Communicating change through narrative design
IF organisational change is poorly communicated
HOWEVER narrative design expertise exists in pockets
THEN expand capacity for change communication
BY designing narratives that align researchers with institutional shifts
BECAUSE change lands when people can story it.
16. Cultivating shared epistemic norms
IF cross-disciplinary teams collaborate
HOWEVER they lack common epistemic standards
THEN evolve capacity for shared epistemic practice
BY studying how evidence reshapes reasoning in mixed-method teams
BECAUSE epistemic integrity is a collective achievement.
17. Evolving supervisory practice
IF supervision models ossify
HOWEVER reflective practice is unevenly distributed
THEN strengthen capacity for supervisory evolution
BY integrating professional-doctorate reflective models into PhD supervision
BECAUSE supervisors shape the institutional future one student at a time.
18. Integrating personal and collective development
IF research careers change rapidly
HOWEVER institutions neglect developmental coherence
THEN expand capacity for integrated career learning
BY studying how AI augmentation reshapes career pathways
BECAUSE institutions grow when people grow coherently.
19. Transforming practice into organisational learning
(Your Nigel–Ioanna example)
IF research leaders steer technological change
HOWEVER they rarely engage in reflective dialogue with those using the tools
THEN strengthen capacity to transform practice into learning
BY exploring how reflective dialogues shape ethical AI adoption
BECAUSE organisational learning emerges when leadership and practice meet.
These 19 micro-patterns naturally collapse into four larger forces of institutional evolution.
🔵 SUPER-CAPABILITY 1: Collective Sensemaking
The power to perceive institutions clearly by listening deeply, sharing meaning, and understanding lived experience.
🟣 SUPER-CAPABILITY 2: Collective Legibility
The power to make research life intelligible through evidence, modelling, and structural insight.
🟢 SUPER-CAPABILITY 3: Collective Shaping
The power to create new institutional possibilities through co-design, experimentation, and narrative ingenuity.
🔴 SUPER-CAPABILITY 4: Collective Self-Transformation
The power to evolve as a research community—aligning norms, supervision, leadership, and professional growth through reflective dialogue.