Outcome-Aware AI Copilot for Collaborative Decision Making: An Adaptive Learning Framework

作者：S. Thombre, V. Doifode, S. Kokardekar, D. Khushalani, D. Bhoyar, P. R. Selokar, S. K. Mohod, V. Bhoyar · 发表于：African Journal Of Applied Research · 年份：2026 · DOI：10.26437/qv5h2n18

Purpose: Artificial Intelligence (AI) systems are being rapidly deployed to support decision-making tasks; however, the AI-only approaches usually suffer from static learning methods, limited context understanding, and error propagation. Human decision-making, while context-aware, is prone to inconsistency and cognitive bias. Design/Methodology/Approach: This paper proposes an Outcome-Aware AI Copilot framework that integrates human judgment with AI predictions and continuously learns from final task outcomes to improve decision accuracy over time. Using an exam-answer validation use case, we compare three different approaches: human-only, AI-only, and AI Copilot-based decision-making. Research Limitation: This study highlights the importance of collaborative learning in achieving more reliable and adaptive AI-assisted decision systems. Findings: Experimental results depict that the proposed AI Copilot consistently outperforms both the human-only and AI-only systems by adapting its decision strategy based on past outcomes. Practical Implication: The proposed Outcome-Aware AI Copilot framework can be applied in educational assessment systems and other decision-support environments to improve accuracy, reduce human error, and enhance the reliability of AI-assisted evaluations through continuous human–AI collaboration and feedback-driven learning. Social Implication: The proposed AI Copilot framework can support fairer, more accurate, and trustworthy decision-making systems by p...