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