User-Adaptive Generation of Generative AI Systems: A Framework for Generating Personalized Generative Artificial Intelligence from Individual User Requirements
Abstract
Generative artificial intelligence (GenAI) systems are commonly developed as general-purpose products that serve many users through shared model architectures, predefined capabilities, configurable settings, and standardized interaction interfaces. Although existing systems support personalization through prompts, memory, fine-tuning, retrieval, and tool integration, the underlying system is often designed before the individual user's requirements are fully known. This paper proposes User-Adaptive Generation of Generative AI Systems (UGGAI), a conceptual framework in which generative artificial intelligence is used to construct, configure, evaluate, and adapt generative AI systems according to the specific requirements of individual users.
The central idea is to transform user requirements into explicit system-design specifications and then generate an appropriate AI system rather than merely generate content through an existing system. The generated system may differ in model selection, capability composition, interaction mechanisms, memory configuration, inference strategy, available tools, computational requirements, and adaptation policies. Depending on the user's needs, the result may be a personalized configuration of an existing model, a composed multi-model system, a parameter-adapted model, or a newly trained model when sufficient resources and evidence justify that approach. The framework further considers continuous adaptation, whereby changes in user requirements can trigger controlled regeneration or modification of the AI system.
A formal problem formulation, reference architecture, user-requirement representation, generation procedure, evaluation methodology, and research hypotheses are presented. The framework is distinguished from conventional personalization, prompt optimization, recommender systems, automated machine learning, and user-specific model adaptation by its proposed focus on generating the AI system itself as an explicit design artifact. This distinction does not imply that the underlying techniques are individually new. Rather, the paper proposes a unified framework for investigating whether user-conditioned system generation can improve task satisfaction, usability, resource efficiency, and adaptability compared with fixed or manually configured alternatives. The framework remains a research proposal requiring implementation and empirical validation.
Related
- Meta-Agent Generation: A User-Adaptive Framework for Generating Personalized Artificial Intelligence Agents
- Meta-Generation: A General Framework for Generating Generators, Generative Mechanisms, and the Rules That Construct Them
- Generative artificial intelligence in the preparation of scientific medical articles on surgery: principles of responsible use and current requirements of editorial organisations
- Generative AI-powered multimodal physiological sensors for personalized edge health monitoring
- Multi-dimensional piano performance technique assessment and personalized teaching strategy generation based on deep learning
BibTeX
@misc{zhang2026useradaptive,
title = {{User-Adaptive Generation of Generative AI Systems: A Framework for Generating Personalized Generative Artificial Intelligence from Individual User Requirements}},
author = {Jincheng Zhang},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23499716},
url = {https://zenodo.org/records/23499716},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - User-Adaptive Generation of Generative AI Systems: A Framework for Generating Personalized Generative Artificial Intelligence from Individual User Requirements
AU - Jincheng Zhang
PY - 2026
DA - 2026/10/20
PB - Zenodo
DO - 10.5281/zenodo.23499716
UR - https://zenodo.org/records/23499716
AB - Generative artificial intelligence (GenAI) systems are commonly developed as general-purpose products that serve many users through shared model architectures, predefined capabilities, configurable settings, and standardized interaction interfaces. Although existing systems support personalization through prompts, memory, fine-tuning, retrieval, and tool integration, the underlying system is often designed before the individual user's requirements are fully known. This paper proposes User-Adaptive Generation of Generative AI Systems (UGGAI), a conceptual framework in which generative artificial intelligence is used to construct, configure, evaluate, and adapt generative AI systems according to the specific requirements of individual users. The central idea is to transform user requirements into explicit system-design specifications and then generate an appropriate AI system rather than merely generate content through an existing system. The generated system may differ in model selection, capability composition, interaction mechanisms, memory configuration, inference strategy, available tools, computational requirements, and adaptation policies. Depending on the user's needs, the result may be a personalized configuration of an existing model, a composed multi-model system, a parameter-adapted model, or a newly trained model when sufficient resources and evidence justify that approach. The framework further considers continuous adaptation, whereby changes in user requirements can trigger controlled regeneration or modification of the AI system. A formal problem formulation, reference architecture, user-requirement representation, generation procedure, evaluation methodology, and research hypotheses are presented. The framework is distinguished from conventional personalization, prompt optimization, recommender systems, automated machine learning, and user-specific model adaptation by its proposed focus on generating the AI system itself as an explicit design artifact. This distinction does not imply that the underlying techniques are individually new. Rather, the paper proposes a unified framework for investigating whether user-conditioned system generation can improve task satisfaction, usability, resource efficiency, and adaptability compared with fixed or manually configured alternatives. The framework remains a research proposal requiring implementation and empirical validation.
ER -