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

💡 Research Summary

This dissertation tackles the emerging challenge of optimizing collaboration between human programmers and artificial intelligence (AI) systems in software development. By integrating Self‑Determination Theory (SDT) with the Big Five personality framework, the author proposes the Role Optimization Motivation Alignment (ROMA) framework, which matches developers’ personality profiles to specific AI collaboration modalities—Co‑Pilot, Co‑Navigator, and Agent—to maximize autonomy, competence, and relatedness, the three basic psychological needs identified by SDT.

The research follows a Design Science Research (DSR) methodology across five iterative cycles. Cycle 1 conducts a systematic literature review to map existing AI‑assisted programming roles to human responsibilities and to formulate the conceptual ROMA model. Cycle 2 implements an online experiment with 200 participants, collecting Big Five Inventory scores and role‑preference ratings, revealing statistically significant correlations between personality traits and preferred AI roles. Cycle 3 adds depth through 46 semi‑structured interviews, uncovering how the mode of role assignment (automatic, semi‑automatic, manual) influences perceived autonomy. Cycle 4 develops a functional ROMA prototype that integrates personality‑based role recommendation, real‑time motivation feedback, and CI/CD‑aware role allocation; pilot testing in real development teams shows an average 23 % increase in intrinsic motivation among professionals and up to 65 % among undergraduate students, alongside measurable improvements in team dynamics (31 % higher communication efficiency) and engineering outcomes (22 % faster code‑review cycles, reduced defect rates). Cycle 5 extends ISO/IEC 29110, the international standard for Very Small Entities (VSE), by adding a “Personality‑Driven Role Mapping Guideline” and an “Automated Role Assignment Module,” enabling small organizations to adopt ROMA within a recognized process framework.

Five personality archetypes emerge from the data:

1. The Explorer (high Openness, low Agreeableness) – prefers exploratory tasks and the Co‑Pilot mode to experiment with new APIs.
2. The Orchestrator (high Extraversion, high Agreeableness) – thrives in coordination, favoring Co‑Navigator to guide team interactions.
3. The Craftsperson (high Neuroticism, low Extraversion) – seeks reliability and delegates repetitive testing to the Agent.
4. The Architect (high Conscientiousness) – values systematic design verification, using Co‑Pilot for structured code generation.
5. The Adapter (balanced profile) – flexibly switches among modalities based on context.

When role assignments align with these archetypes, SDT’s three needs are satisfied, leading to heightened intrinsic motivation, better perceived competence, and stronger relatedness among team members. The ROMA framework operationalizes this alignment through three core components:

• Personality‑Role Mapping Engine – converts Big Five scores into archetype classification and recommends the optimal AI modality using a pre‑validated preference matrix.
• Motivation Feedback Loop – captures post‑task surveys and telemetry to monitor autonomy, competence, and relatedness, automatically triggering role reassignment if deficits are detected.
• Automated Assignment Module – integrates with CI/CD pipelines to provision the appropriate AI toolset for each developer, while offering a UI for manual overrides.

The study acknowledges several limitations. The participant pool is predominantly from Korean and Anglo‑American academic and corporate settings, potentially limiting cross‑cultural generalizability. Self‑report personality measures may be subject to social desirability bias. Future work should explore longitudinal effects in diverse cultural contexts, incorporate additional individual differences (e.g., expertise, domain knowledge), and refine the ISO/IEC 29110 extension for broader industry adoption.

In summary, the dissertation delivers an empirically validated, theory‑grounded framework that links personality traits to AI‑assisted programming roles, demonstrates substantial motivation and performance gains, and provides a concrete pathway for standard‑compliant implementation in small‑scale software enterprises. This contribution advances both behavioral software engineering research and practical guidance for the next generation of human‑AI collaborative development environments.