Description

This Master Prompt establishes a comprehensive framework for applying Design Science Research to the conceptualization, development, evaluation and governance of Artificial Intelligence-enabled artifacts. It examines the integration of machine learning, deep learning, generative Artificial Intelligence, large language models, natural language processing, predictive analytics, intelligent decision-support systems, automated knowledge extraction, recommendation systems and human–AI interaction within artifact-oriented research. Particular emphasis is placed on transforming practical decision problems into theoretically grounded AI artifact requirements, selecting appropriate computational approaches, designing intelligent system architectures, integrating domain knowledge and evaluating the relationships among algorithmic performance, human interpretation, operational usefulness and scientific contributions. The framework incorporates machine learning development methodologies, data preprocessing, feature engineering, predictive modeling, optimization, explainable Artificial Intelligence, human-centered AI design, hybrid intelligence, knowledge-based systems, prompt engineering, rule-integrated language models and responsible AI governance. Researchers are guided in developing AI-enabled conceptual frameworks, constructing intelligent artifacts, designing computational pipelines, selecting training and evaluation strategies, establishing baseline models, conducting comparative experiments and evaluating the effectiveness of human–AI collaboration. The Master Prompt addresses predictive accuracy, precision, recall, F1-score, calibration, robustness, generalization, interpretability, computational efficiency, usability, fairness, privacy, transparency, accountability and ethical deployment. It supports the development of AI system architecture diagrams, machine learning workflow models, algorithmic flowcharts, human–AI interaction frameworks, confusion matrices, performance comparison graphs, explainability visualizations and responsible AI governance matrices. Particular attention is given to data leakage prevention, reproducibility, model uncertainty, algorithmic bias, hallucination risks, human oversight, contextual appropriateness and the distinction between computational performance and validated real-world benefits. The framework ensures that AI-enabled artifacts are evaluated not only as technical systems but also as purposeful research contributions situated within authentic organizational, social and technological environments.