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

In 1967, Marvin Minsky envisioned minds—biological and artificial—collaborating beyond their individual limitations. That same year, Brian Randell captured software engineering’s irreducibly social nature as the “multi-person construction of multi-version programs.” Today, the prophecy becomes reality. Artificial intelligence, that ‘man-made yet alien’ presence, transforms the very nature of collaborative programming itself. This dissertation optimizes human-AI programming roles through self-determination theory and personality psychology. As developers encounter AI collaboration—experiencing a technological thrownness into unprecedented relational dynamics—frameworks must emerge that harmonize human agency with algorithmic augmentation. The research employs Design Science Research across five systematic cycles: Cycle 1 explores personality-role causal relationships through behavioral science methods. Cycles 2 & 3 adopt pragmatist axiology to design and extend the Role Optimization Motivation Alignment (ROMA) framework with AI integration. Cycle 4 develops an ISO/IEC 29110 Software Basic Profile & Agile Guidelines extension for Very Small Entities, while Cycle 5 validates through in-situ test artifacts and empirical triangulation, engaging 200 experimental participants and 46 interview respondents. Results demonstrate that ROMA significantly enhances self-determination and team dynamics through personality-driven role optimization in both human-human and human-AI (“Human ⧉ AI”) collaborative contexts. Five distinct personality archetypes emerge: The Explorer (high Openness), The Orchestrator (high Extraversion/Agreeableness), The Craftsperson (high Neuroticism/low Extraversion), The Architect (high Conscientiousness), and The Adapter (balanced profile). Personality-aligned role assignments increase intrinsic motivation by an average of 23% among professionals and up to 65% among undergraduates, with AI mode selection (Co-Pilot, Co-Navigator, Agent) proving crucial for self-determination need satisfaction. The dissertation contributes: (1) an empirically-validated framework linking personality traits to programming role preferences and self-determination outcomes; (2) a taxonomy of AI modalities mapped to personality profiles while preserving human agency; and (3) an ISO/IEC 29110 extension enabling VSEs to implement personality-driven role optimization within established standards.