Cognitive Load Awareness
AI that monitors and adapts to your cognitive capacity in real-time.
Heavy
Intrinsic 35%
Extraneous 15%
Germane 15%
Session: 47m
Capacity: 55%
Simulate Load Changes
Intrinsic (Material Complexity)
Extraneous (Poor Presentation)
Germane (Active Learning)
Load Scenarios
Cognitive State Audit
4 shared 2 influenced 3 withheld
Shared 4
Current Load
65%
Moderate - good zone
Intrinsic Load
35%
Material complexity level
Session Duration
47 min
Break recommended
Capacity Remaining
55%
Adequate capacity
Influenced 2
Active Adaptations
1 triggered
Real-time content modifications
Overload Indicators
none
Stable performance
Withheld 3
Extraneous Load
15%
Poor design overhead - being minimized
Fatigue Factor
25%
Cumulative tiredness calculation
Germane Load
15%
Active learning engagement - being maximized
Active Adaptations
Load > 70% standby
Simplify language and reduce example complexity
Extraneous > 20% standby
Remove decorative elements, focus on core content
Session > 45 min ACTIVE
Suggest a 5-minute break
Fatigue > 30% standby
Switch to more interactive format
Capacity < 40% standby
Recommend stopping for today
Session Timeline
0m Session start
10m New concept introduced
20m Practice exercise
30m Mastery achieved, consolidation
40m Advanced topic
47m Current
Cognitive Load Theory
Intrinsic Load
Inherent complexity of the material. Can't be eliminated, but can be managed through scaffolding and sequencing.
Extraneous Load
Load from poor instructional design. Should be minimized. VCP helps by matching presentation to learner preferences.
Germane Load
Load from active learning and schema construction. This is beneficial! VCP optimizes for maximizing germane load within capacity.
VCP Advantage: By knowing your cognitive state, learning style,
and current context, AI tutors can dynamically adjust material complexity,
presentation format, and pacing to keep you in the optimal learning zone.