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

• URL: http://arxiv.org/abs/2511.00417v1
• Date: Sat, 01 Nov 2025 06:00:14 GMT
• Title: Human-AI Programming Role Optimization: Developing a Personality-Driven Self-Determination Framework
• Authors: Marcel Valovy,
• Abstract summary: dissertation optimize human-AI programming roles through self-determination theory and personality psychology.<n>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)
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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

Related papers

• Enhancing Persona Following at Decoding Time via Dynamic Importance Estimation for Role-Playing Agents [13.003892350610947]
  The utility of Role-Playing Language Agents in sociological research is growing alongside the adoption of Large Language Models.<n>For realism in social simulation, Role-Playing Language Agents must adhere to their personas defined by character profiles.<n>We propose a novel, theory-driven method that dynamically estimates context-dependent persona importance and integrates it into weighted reward-guided decoding.
  arXiv  Detail & Related papers  (2026-03-02T04:37:16Z)
• HumanLLM: Towards Personalized Understanding and Simulation of Human Nature [72.55730315685837]
  HumanLLM is a foundation model designed for personalized understanding and simulation of individuals.<n>We first construct the Cognitive Genome, a large-scale corpus curated from real-world user data on platforms like Reddit, Twitter, Blogger, and Amazon.<n>We then formulate diverse learning tasks and perform supervised fine-tuning to empower the model to predict a wide range of individualized human behaviors, thoughts, and experiences.
  arXiv  Detail & Related papers  (2026-01-22T09:27:27Z)
• Structured Personality Control and Adaptation for LLM Agents [11.050618253938126]
  Large Language Models (LLMs) are increasingly shaping human-computer interaction (HCI)<n>We present a framework that models LLM personality via Jungian psychological types.<n>This design allows the agent to maintain nuanced traits while dynamically adjusting to interaction demands.
  arXiv  Detail & Related papers  (2026-01-15T03:15:24Z)
• Exploring a Gamified Personality Assessment Method through Interaction with LLM Agents Embodying Different Personalities [45.56431615835303]
  This study explores an interactive approach for personality assessment, focusing on the multiplicity of personality representation.<n>We propose a framework of Gamified Personality Assessment through Multi-Personality Representations (Multi-PR GPA)
  arXiv  Detail & Related papers  (2025-07-05T11:17:20Z)
• Evaluating AI Alignment in Eleven LLMs through Output-Based Analysis and Human Benchmarking [0.0]
  Large language models (LLMs) are increasingly used in psychological research and practice, yet traditional benchmarks reveal little about the values they express in real interaction.<n>We introduce PAPERS, output-based evaluation of the values LLMs express.
  arXiv  Detail & Related papers  (2025-06-14T20:14:02Z)
• Deterministic AI Agent Personality Expression through Standard Psychological Diagnostics [0.0]
  We show that AI models can express deterministic and consistent personalities when instructed using established psychological frameworks.<n>More advanced models like GPT-4o and o1 demonstrate the highest accuracy in expressing specified personalities.<n>These findings establish a foundation for creating AI agents with diverse and consistent personalities.
  arXiv  Detail & Related papers  (2025-03-21T12:12:05Z)
• MindGYM: What Matters in Question Synthesis for Thinking-Centric Fine-Tuning? [51.85759493254735]
  MindGYM is a structured and scalable framework for question synthesis.<n>It infuses high-level reasoning objectives to shape the model's synthesis behavior.<n>It composes more complex multi-hop questions based on QA seeds for deeper reasoning.
  arXiv  Detail & Related papers  (2025-03-12T16:03:03Z)
• Kolb-Based Experiential Learning for Generalist Agents with Human-Level Kaggle Data Science Performance [81.05882480184587]
  We propose a computational framework of Kolb's learning cycle with Vygotsky's ZPD for autonomous agents.<n>Agent K is the 1st AI system to successfully integrate Kolb- and Vygotsky-inspired human cognitive learning.<n>With 9 gold, 8 silver, and 12 bronze medals level performance - including 4 gold and 4 silver on prize-awarding competitions - Agent K is the 1st AI system to successfully integrate Kolb- and Vygotsky-inspired human cognitive learning.
  arXiv  Detail & Related papers  (2024-11-05T23:55:23Z)
• PersLLM: A Personified Training Approach for Large Language Models [66.16513246245401]
  We propose PersLLM, a framework for better data construction and model tuning.<n>For insufficient data usage, we incorporate strategies such as Chain-of-Thought prompting and anti-induction.<n>For rigid behavior patterns, we design the tuning process and introduce automated DPO to enhance the specificity and dynamism of the models' personalities.
  arXiv  Detail & Related papers  (2024-07-17T08:13:22Z)
• Personality of AI [0.0]
  This research paper delves into the evolving landscape of fine-tuning large language models to align with human users. Acknowledging the impact of training methods on the formation of undefined personality traits in AI models, the study draws parallels with human fitting processes using personality tests. The paper serves as a starting point for discussions and developments in the burgeoning field of AI personality alignment.
  arXiv  Detail & Related papers  (2023-12-03T18:23:45Z)
• PsyCoT: Psychological Questionnaire as Powerful Chain-of-Thought for Personality Detection [50.66968526809069]
  We propose a novel personality detection method, called PsyCoT, which mimics the way individuals complete psychological questionnaires in a multi-turn dialogue manner. Our experiments demonstrate that PsyCoT significantly improves the performance and robustness of GPT-3.5 in personality detection.
  arXiv  Detail & Related papers  (2023-10-31T08:23:33Z)
• Psychological Aspects of Pair Programming [0.0]
  This study aims to gain quantitative and qualitative insights into pair programming. The research's goal is to use the findings to design further studies on pairing with artificial intelligence.
  arXiv  Detail & Related papers  (2023-06-12T20:57:50Z)
• AGENT: A Benchmark for Core Psychological Reasoning [60.35621718321559]
  Intuitive psychology is the ability to reason about hidden mental variables that drive observable actions. Despite recent interest in machine agents that reason about other agents, it is not clear if such agents learn or hold the core psychology principles that drive human reasoning. We present a benchmark consisting of procedurally generated 3D animations, AGENT, structured around four scenarios.
  arXiv  Detail & Related papers  (2021-02-24T14:58:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.