Cognitive Processing System
Supports perception, interpretation, judgment, and evaluation of AI-generated outputs under bounded rationality and limited attentional capacity.
About HAIPS
HAIPS proposes that high-quality decision-making in intelligent environments depends not only on system transparency, but also on the human capacity to cognitively process explanations and emotionally regulate under uncertainty, pressure, and responsibility.
HAIPS shifts the focus from human–AI interaction as interface design to human–AI integration as a psychological system.
Explainable AI
Decision-Making
Cognitive Load
Emotional Regulation
High-Stakes Systems
Framework Components
Emotional Regulation System
Captures the management of trust, uncertainty, stress, and confidence when humans engage with AI in consequential environments.
Explainable AI Layer
Provides interpretable information that can guide action, but only becomes effective when aligned with human psychological capacity.
Inner AI
Functions as the integrative core of HAIPS, coordinating cognition, affect, and interpretive response to produce adaptive decisions.
Founder
Nguyen Thi Thu Van
Independent researcher in Psychology and AI, lecturer in applied psychology, and professional skills trainer with more than 15 years of experience in higher education and corporate training.
Her work focuses on human cognition, emotional adaptation, and human–AI psychological integration in education, leadership, and high-stakes environments.
Academic Positioning
The HAIPS framework is developed as a foundational conceptual contribution at the intersection of psychology, cognitive science, decision theory, and human-centered AI.
This research direction supports future journal publications, preprints, academic visibility, and doctoral applications.
Current Working Paper
The HAIPS Model: An Explainable Human–AI Integrated Psychological Framework for Cognitive–Emotional Decision-Making in High-Stakes Environments
This working paper develops HAIPS as a conceptual model integrating cognition, emotional regulation, and explainable AI through the construct of Inner AI.
- Explains the paradox of explainability in human–AI systems
- Develops theoretical propositions linking explainability, cognitive load, emotion, and decision quality
- Positions HAIPS as a psychologically grounded framework for adaptive AI design
Contact
Name: Nguyen Thi Thu Van
Email: vannguyen09091982@gmail.com
Research Area: Psychology, Human–AI Interaction, Cognitive Adaptation, Explainable AI
This site is intended as an academic profile and research presentation page for the HAIPS framework.