Creator Pro Insights Hub

David J Passiak

David Passiak blends timeless wisdom and advanced AI strategy to teach professionals how to grow with AI—developing intuition, purpose, and future-ready skills through the Insight Architect Method™.

Toward Inclusive AI Training: Conversational and Intuitive Methodologies as Solutions to Engineering Bias

Toward Inclusive AI Training: Conversational and Intuitive Methodologies as Solutions to Engineering Bias

Addressing engineering bias in AI training methodologies requires a deliberate shift from engineering-centric prompt design to intuitive, conversational training techniques. Embracing such methodologies not only benefits historically underserved groups—women and older professionals—but also provides a universally accessible foundation for AI skill development. Redefining AI Literacy Beyond Engineering Norms

Engineering Bias – Disproportionate Impacts Across Gender and Age

Engineering Bias – Disproportionate Impacts Across Gender and Age

Engineering Bias and Invisible Defaults in Training Methodologies Engineering bias emerges primarily from how users are trained to interact with AI—not inherently from the AI tools themselves. While AI systems can be effectively used in conversational, intuitive, and exploratory ways—styles of interaction that resonate strongly with older professionals

Why Conversational Interaction—Not Prompt Engineering—Drives Mainstream Adoption

Why Conversational Interaction—Not Prompt Engineering—Drives Mainstream Adoption

The generative AI industry suffers from an overlooked but critical obstacle: engineering bias. Rather than adapting AI tools to match how most people naturally learn—through conversation, intuition, and incremental guidance—the industry overwhelmingly pressures mainstream users to think and behave like engineers. Certification programs, corporate training, and AI tutorials

The Ubiquity of Prompt Engineering and the Misalignment Problem

The Ubiquity of Prompt Engineering and the Misalignment Problem

Introduction Over the past five years, prompt engineering became nearly synonymous with effectively interacting with AI systems. Driven by necessity, early large language models (LLMs) such as GPT-2 and GPT-3 required carefully structured prompts to produce reliable outputs. Engineers and early adopters developed a quasi-formal discipline of prompt engineering, akin

Engineering Bias in AI: Why Prompt Engineering Is Failing Users and Hindering Adoption

Engineering Bias in AI: Why Prompt Engineering Is Failing Users and Hindering Adoption

Executive Summary Generative AI holds transformative potential—but its promise remains locked behind an invisible wall: engineering bias. By prioritizing complex prompt engineering over intuitive, human-centered design, current AI systems are alienating mainstream users and hindering mass adoption. Across industries, a growing body of evidence reveals deep frustration with prompt-driven

Gender Influences on AI Interaction Styles and Outcomes

Preferred AI Communication Styles by Gender Research shows clear gender-based differences in how adult professionals prefer to interact with AI. Women tend to adopt a more conversational and cautious style, while men often take a direct, “tool-like” approach. For example, a Pew survey on voice assistants found 62% of women