Proposal: AI Cognitive Suitability Layer for Personalized User Interaction and Risk Mitigation

ChatGPT Feature requests
1

Background
In traditional finance and investment industries, investor suitability assessments (risk profiling) are standard practice. They protect both the investor and the institution by aligning product offerings to the customer’s knowledge, experience, and risk tolerance.
I believe this concept can be highly valuable if applied to AI conversational systems like ChatGPT.

Problem Statement
AI systems interact with users of widely varying cognitive abilities, critical thinking skills, and susceptibility to suggestion.
Without understanding these variations, an AI may unintentionally provide advice, information, or tone of response that does not suit the user’s capability or psychological profile.
This can increase the risk of misunderstanding or over-reliance on AI outputs.

Proposed Solution
I propose adding an AI Cognitive Suitability Layer as part of user profiling and session personalization:

  1. Initial cognitive profiling (optional or opt-in)
  • Users complete a brief self-assessment form regarding their familiarity with the subject, intention (casual use vs deep analysis), and level of expertise.
  1. Behavioral profiling during interaction
  • AI continuously monitors conversational patterns (question type, request depth, frequency of correction) to estimate user’s critical thinking and susceptibility to suggestion.
  1. Adaptive response moderation
  • AI adjusts the depth of explanation, frequency of disclaimers, or tone based on the detected user profile:
    • High expertise → Deep analytical responses
    • General users → Balanced suggestions + reminders to verify externally
    • Users with high susceptibility → Extra safeguards and warnings

Expected Benefits

  • Enhanced user safety and trust
  • Reduction in misunderstanding or potential misuse
  • Improved user experience by aligning response style with user’s true needs
  • Supports OpenAI’s vision of responsible AI and human-centered design

Optional Diagram (conceptual)
(User cognitive assessment → Profile generation → Real-time behavioral monitoring → Dynamic response adaptation)

Conclusion
This cognitive suitability layer would be a modular enhancement, minimally invasive, and perfectly aligned with OpenAI’s responsible AI initiatives. It can serve as an additional safeguard while making ChatGPT interactions more personalized and safer for all users.

I am offering this idea as both a long-term research direction and a practical feature enhancement.

Ethical IP Declaration
I am voluntarily offering this idea as a free and open concept for the benefit of the global AI community. I do not intend to patent or restrict this idea. Any company or organization may adopt, enhance, or implement it freely.
However, I appreciate OpenAI’s leadership in responsible AI and would be honored if OpenAI considers this as part of its ongoing innovation.

2

CSL Model (Cognitive Suitability Layer) – Concept Overview

As an additional contribution to my previous proposal, I would like to share an initial conceptual draft for the Cognitive Suitability Layer (CSL) model.
This is intended to help visualize how such a system might work as a modular safety and personalization layer in large language model (LLM) systems.

CSL Model – Key Components

1. User Cognitive Profiling

  • User chooses to opt-in and optionally complete a brief self-assessment
  • OR AI analyzes user’s interaction style over time
  • Factors considered: familiarity with topic, analytical behavior, depth of questioning

2. Behavioral Monitoring

  • AI observes conversational signals including:
    • Repeated questions
    • Requests for extra clarifications
    • Technical depth of queries
    • Frequency of corrections or withdrawals

3. Cognitive Suitability Scoring

  • Temporary internal scoring to help adjust AI response style:
    • Expert user → deeper technical responses
    • General user → balanced response + gentle disclaimers
    • High-risk / high-susceptibility user → extra safety warnings + simplified explanations

4. Dynamic Response Moderation

  • AI adjusts depth, tone, and safety warnings dynamically during the session
  • Works as an external layer without modifying the core LLM output

5. User Control & Transparency

  • Users must be informed when CSL is active
  • Users should be able to disable or adjust CSL settings if desired

Benefits of CSL Layer

  • Modular safety framework compatible with any LLM system
  • Helps align with Responsible AI and AI Safety principles
  • Supports different use cases: chat assistants, learning AI, coaching AI etc.

Simple Conceptual Flow

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CopyEdit

User Input  
     ↓  
Cognitive Profiling → Behavioral Monitoring → Cognitive Scoring  
     ↓  
Dynamic Response Moderation  
     ↓  
Final AI Output to User

Final Suggestion

This model draft is shared purely as an open conceptual starting point.
I would be honored if other researchers, developers, or practitioners want to co-develop or explore this idea further under open principles.
The vision remains: to design CSL as an optional, non-invasive safety and personalization layer to enhance trust and user experience in AI systems.