0%
The world does not arrive as prompts.
Real life arrives as events, changes, conversations, relationships, obligations, opportunities, risks and signals. Before an intelligent system can reason about what to do, it must determine what is happening.
A brief introduction to Situational AI and why it matters.
History, documents, roles, relationships, constraints, memories, tools, and current signals. Indispensable, and not enough.
For a particular observer, relative to goals, responsibilities, relationships, time, and the responses actually available.
Events happen. States persist. Context informs. Intents aim. Situations mean.
A payment failed. A message arrived. A build broke.
The invoice is overdue. The customer is inactive. The branch is blocked.
History, documents, roles, relationships, constraints, and current signals.
Recover payment. Preserve trust. Ship safely. Learn the answer.
The observer-relative interpretation that establishes what matters and what response is appropriate.
Modern AI may recognize situations implicitly, inside a prompt, and forget them when the inference ends. Situational AI asks what changes when situations become explicit, representable computational objects that a system can:
Detect which situation is emerging, evolving, overlapping, or ending, from changing evidence.
Hold observer, roles, events, goals, stage, urgency, uncertainty, and available responses as one object.
Find structurally analogous situations across vocabularies, workflows, and domains.
Keep what was recognized, what was chosen, why, and what followed, as experience rather than text.
Follow a situation as it intensifies, weakens, merges, divides, or resolves over time.
Revise interpretations and responses from outcomes, including the interpretations that were wrong.
How can a system recognize which situation is emerging from changing evidence?
Can an intelligent system remember experience as situations, responses and outcomes rather than merely information?
Can structurally analogous situations be discovered across different domains?
How do situations emerge, develop, intensify, weaken, merge, divide and resolve?
These questions are the work.
Founder, academic collaborators and student researchers exploring, building, testing and challenging the idea.
Situational AI is developing through conversations, experiments and encounters across disciplines.
October 22, 2026 · 5 PM · Crow Museum of Asian Art, Edith and Peter O’Donnell Jr. Athenaeum, UT Dallas
Details & RSVP →August 5, 2026 · Naveen Jindal School of Management, UT Dallas
What happened →May 4, 2026 · Naveen Jindal School of Management, UT Dallas
What happened →Help define, build, test, challenge, and extend situation-centric intelligence. Researchers, engineers, cognitive scientists, designers, operators, and critics are welcome.
BotsWork.ai
Mindspace AI