Human-AI Programming Role Optimization: Developing a Personality-Driven Self-Determination Framework

TL;DR

This paper presents a novel framework that leverages personality psychology and self-determination theory to optimize human-AI programming roles, significantly improving motivation and collaboration outcomes.

Contribution

It introduces the ROMA framework, empirically validates links between personality traits and programming roles, and extends standards for small entities to implement personalized AI collaboration.

Findings

Personality-driven role optimization increases motivation by up to 65%.

Five personality archetypes correlate with distinct programming role preferences.

Assignment modes are crucial for satisfaction in human-AI collaboration.

Abstract

As artificial intelligence transforms software development, a critical question emerges: how can developers and AI systems collaborate most effectively? This dissertation optimizes human-AI programming roles through self-determination theory and personality psychology, introducing the Role Optimization Motivation Alignment (ROMA) framework. Through Design Science Research spanning five cycles, this work establishes empirically-validated connections between personality traits, programming role preferences, and collaborative outcomes, engaging 200 experimental participants and 46 interview respondents. Key findings demonstrate that personality-driven role optimization significantly enhances self-determination and team dynamics, yielding 23% average motivation increases among professionals and up to 65% among undergraduates. Five distinct personality archetypes emerge: The Explorer (high Openness/low Agreeableness), The Orchestrator (high Extraversion/Agreeableness), The Craftsperson (high Neuroticism/low Extraversion), The Architect (high Conscientiousness), and The Adapter (balanced profile). Each exhibits distinct preferences for programming roles (Co-Pilot, Co-Navigator, Agent), with assignment modes proving crucial for satisfaction. The dissertation contributes: (1) an empirically-validated framework linking personality traits to role preferences and self-determination outcomes; (2) a taxonomy of AI collaboration modalities mapped to personality profiles while preserving human agency; and (3) an ISO/IEC 29110 extension enabling Very Small Entities to implement personality-driven role optimization within established standards. Keywords: artificial intelligence, human-computer interaction, behavioral software engineering, self-determination theory, personality psychology, phenomenology, intrinsic motivation, pair programming, design science research, ISO/IEC 29110