Evaluation Framework v2.60

Architectural
Heuristics

A multi-layered evaluation of cognitive friction in AI-driven interfaces. We analyze the invisible boundary where machine logic meets human behavior.

View Pillars

01. Heuristic Mapping

We begin by mapping existing AI interaction flows against ten core HCI principles. Our research identifies exactly where the "black box" nature of AI causes a loss of human agency or semantic confusion.

User agency verification
Predictability benchmarks
System-status transparency

02. Friction Point Analysis

A systematic audit of waiting states, error recovery, and hallucination UI indicators. We measure the cognitive load required to verify machine-generated output before it enters the user's workflow.

Technical focus

"Minimizing the 'uncanny valley' of interface lag during high-token generation phases."

03. Cognitive Load Testing

Our final stage uses observable user behavior patterns to quantify mental effort. We utilize eye-tracking and response-latency metrics to determine if an AI interface is truly assisting the user or merely adding another layer of management.

Visual Noise Reduction

Assessing the density of information and decision-nodes per screen.

Latency Tolerance

Developing UI cues that maintain user trust during long-form processing.

Trade-off Metrics

Every AI interface is a balance of competing priorities. Our methodology weighs these specifically for each use case.

Metric Axis	Primary Objective	Risk Factor
Agency vs. Automation	User steering and override capabilities.	Passive system dependency.
Transparency vs. Speed	Clear sourcing of model data origins.	Black-box decision making.
Forgiveness vs. Accuracy	Robust error handling for hallucinations.	Erosion of user trust.