THE DISHA INTELLIGENCE ENGINE → LEARNING INTELLIGENCE
Know What to Learn Next — and Why.
Learning decisions traced to goals and gaps — with practice, evidence and progress, not just content.
What learning intervention would most meaningfully close a capability gap?
Signature: GOAL → GAP → OPTIONS → PATH → PRACTICE → EVIDENCE → PROGRESS
One system, six questions — you are here:
The Learning Decision Canvas
Change the goal — watch everything re-prioritize
Goal, current capability, target capability, evidence gap, constraints, learning options, practice opportunities and the evidence outcome. The canvas is the decision, not a form.
Current capability
Senior engineer — strong build evidence, thin leadership evidence
Target capability
Team lead — leadership, cost governance, streaming depth
Evidence gap
Leadership evidence (none verified) · streaming architecture (stated only) · cost governance (absent)
Constraints
Full-time role · 5 h/week budget · prefers work-integrated practice
What this goal prioritizes right now
- 1. Evidence-building leadership assignment
- 2. Streaming project at work
- 3. Cost-governance shadowing
Evidence outcome to aim for: A shipped leadership artifact + updated skill evidence — not a completion certificate
Route Simulator — try it
Four routes to the same capability, compared honestly
Fast-track, deep-learning, work-integrated, credential-led — by time, effort, prerequisites, evidence produced and dependencies.
1. Deep learningbest fit for this constraint
Structured course sequence with assessments.
2. Fast-track
Short intensive sprint; practice embedded in current work.
3. Work-integrated
Learn by doing a real, scoped piece of work with feedback.
4. Credential-led
Provider programme ending in a verified credential.
The evidence loop
Completion is not competence — the loop closes in evidence
The loop ends where the Skills page begins again: updated skill evidence re-evaluates readiness — and the next gap decision is better informed than the last.
Organization view — within privacy boundaries
Aggregate capability gaps by role, team or critical skill — never individual surveillance. Where a gap is structural, the honest options widen: build, buy, borrow or automate the work. Each option carries different time, dependency and evidence profiles — compared in the Workforce and Decision pages, not decided here.
Calculators
Run the numbers — as scenarios, never verdicts
Calculator 1
Learning Priority
Target, current evidence, time, proficiency goal and constraints → a priority with rationale, prerequisites and evidence-building activities.
Calculator 2
Route Comparison
Fast-track vs deep-learning vs work-integrated vs credential-led — by time, effort and evidence produced.
Calculator 3
Build / Buy / Borrow / Automate
For organizations: develop, hire, partner or redesign the work — where appropriate.
Every recommendation traces to a goal and a gap, and ends in an actionable next step. Completion is never silently treated as competence.
Ask the AI Learning Advisor
Traces every suggestion back to your goal and gap — and never invents providers or prices.
- What should I learn next?
- Can I learn this through a project instead?
- Which gap matters most?
- What should my team learn?
Grounding rules
- Answers cite the authorized records behind them — or say the evidence does not exist.
- Uncertainty is shown, never smoothed away.
- Recommendations are options for a human to judge — never silent decisions.
- Every answer stays within your purpose and access level.
In the live product these conversations stream from your authorized data. Here, the questions show exactly what the analyst is built to answer.
Interconnections
Where learning intelligence hands off next
Conceptual navigation across one intelligence system — links carry your context; none of this implies a causal or predictive pipeline.
Technical architecture — how this page is built
Connected systems
Goal graph, skill graph, learner profile, evidence ledger, content metadata, pathway engine, recommendation engine, assessment & progress services, provider integrations.
Core objects
Goal, gap, learning option, route, practice opportunity, evidence outcome, constraint, prerequisite, progress event.
Governance — identical on every Intelligence page
How this system stays honest
These five statement types never blend: everywhere in the engine, you can tell what is measured, what is derived, what is projected and what is advised.
Statement types
Observed, inference, forecast, scenario and recommendation are visually distinguished — never blended.
Illustrative by default
All data, people and outcomes on these pages are labeled illustrative composites.
No fabrication
Never fabricate people, employers, credentials, outcomes or statistics.
Explainable AI
AI explanations expose the evidence and the uncertainty behind them.
Authorized use
Data is used only within its authorized purpose and access level.
Human authority
AI supports judgement; it never replaces human decision authority.
Learning Intelligence adds: Never fabricate provider availability, price, credentials or outcomes. Content recommendation is kept separate from credential validity.
The engine behind the pages
Six pages, one interaction contract
Rule 1
Context handoff
Moving between the six pages carries your question and context with you — you never restart from zero.
Rule 2
Shared vocabulary
Skill, evidence, proficiency, readiness and scenario mean the same thing on every page.
Rule 3
Common evidence layer
Every claim carries source, timestamp, provenance, confidence and access level — everywhere.
Rule 4
AI layer boundaries
The AI summarizes, explains, compares and simulates — without inventing facts or silently converting recommendations into decisions.
Rule 5
Progressive disclosure
Overview first; evidence, assumptions and audit trails open on demand.
Journey · Recruit
Journey · Develop
Journey · Move
Journey · Transform
Journey · Institution
Illustrative journeys — conceptual paths across the engine, not claims about how any decision is made.