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Three levels at which the user's cognition can be represented in artificial intelligence.

Baptist Liefooghe¹, Leendert van Maanen¹

¹Department of Psychology, Utrecht University, Utrecht, Netherlands.

Frontiers in Artificial Intelligence

|January 30, 2023

Summary

Optimizing artificial intelligence (AI) user models by integrating human cognition insights enhances AI adaptability. Deeper cognitive integration in AI user models leads to more accurate, personalized user experiences and advances explainable AI.

Keywords:

cognitive modeling explainable AI human behavior human cognition user model

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Area of Science:

Cognitive Science
Artificial Intelligence
Human-Computer Interaction

Background:

Artificial intelligence (AI) applications are increasingly integrated into daily life, often utilizing user models for personalization.
User models enable AI to adapt to individual users, but their effectiveness can be limited.
Current user models may not fully leverage insights from human cognitive processes.

Purpose of the Study:

To investigate how integrating human cognition models can optimize AI user models.
To explore different levels of cognitive integration within AI user models.
To demonstrate the benefits of cognition-informed user models, particularly in educational AI applications.

Main Methods:

Review and categorization of existing approaches to integrating human cognition into AI user models.
Case study analysis of AI-based educational applications.
Evaluation of user model validity and adaptation granularity based on cognitive integration depth.

Main Results:

Identified three distinct levels of integrating human cognition into AI user models, ranging from loose inspiration to tight implementation of cognitive processes.
Demonstrated that user models with deeper cognitive integration provide more valid and fine-grained adaptations.
Educational AI case studies showed enhanced personalization with cognition-informed user models.

Conclusions:

AI user models benefit significantly from closer alignment with models of human cognition.
Deeper cognitive integration leads to superior AI personalization and adaptation.
Cognition-informed user models represent a promising avenue for advancing explainable AI (XAI).