BEYOND PREDICTION MODELS: DEVELOPING ARTIFICIAL INTELLIGENCE SYSTEMS CAPABLE OF REASONING, SELF-ASSESSMENT, AND ADAPTIVE DECISION-MAKING

Abstract

Artificial intelligence systems evolved from traditional predictive models toward more advanced architectures capable of reasoning, self-assessment, and adaptive decision-making. This study examined how these three core capabilities influenced intelligent system performance in complex and dynamic environments. A quantitative research design was employed, and data were collected from a sample of 320 respondents working in AI-related domains, including data science, software engineering, and intelligent system development. The study analyzed relationships among AI Reasoning, Self-Assessment, Adaptive Decision-Making, and Intelligent System Performance using correlation and regression techniques. The results indicated that AI Reasoning significantly influenced Self-Assessment (β = 0.58, p < 0.001) and Adaptive Decision-Making (β = 0.61, p < 0.001). Self-Assessment showed a positive effect on Intelligent System Performance (β = 0.36, p < 0.001), while Adaptive Decision-Making demonstrated the strongest impact on performance (β = 0.49, p < 0.001). Correlation analysis confirmed strong positive relationships among all variables, with the highest correlation between Adaptive Decision-Making and Intelligent System Performance (r = 0.67). The model explained a substantial proportion of variance in system performance, indicating strong predictive power. The findings confirmed that integrating reasoning, self-assessment, and adaptive mechanisms significantly enhanced the effectiveness, reliability, and autonomy of AI systems. The study contributed to the development of next-generation artificial intelligence frameworks capable of operating in uncertain and rapidly changing environments.