A behavioural intelligence framework for understanding human action in AI systems.
When a student pauses for 30 seconds before a math problem, the system records the pause and moves on.
It cannot tell confusion from careful thinking, anxiety from strategic deliberation.
It tracks what. It misses why.
Clicks capture automatically. Emotions don't show up in server logs.
Psychologists and computer scientists rarely cite each other.
Engagement KPIs reward time-on-platform over user goal achievement.
Explains why: autonomy, competence, relatedness. Lacks real-time detection.
Decodes feelings from signals. Disconnected from motivation theory.
Detects vulnerability & receptivity windows. Limited beyond health.
Captures what users do. Skips why.
Each layer generates the one above; AI must work in reverse.
Most AI sits at Layer 1. ABSD is the roadmap up.
What the user does. Discrete events tracked with high fidelity by almost every system today — and where most stop.
How the user does it. Patterns emerging across actions — not single events. The bridge between what AI sees and what it needs to understand.
What the user feels. Inferred — not directly observable. Confusion can resolve into engagement or spiral into frustration depending on system response inside that window.
Why the user does it. The fundamental psychological needs that shape how users respond when states are challenged.
A user with high competence drive persists through confusion. One with frustrated competence disengages.
Across SDT, affective computing, JITAI, learning analytics.
Semi-structured, 15–20 min. CX primary, EdTech secondary.
Descriptive breadth on signal recognition and framework utility.
Illustrative ABSD applications. Public docs.