Science Consultant Agent

2025-12-17

AI Summary

To address the challenges of AI modeling strategy selection—namely, difficulty in decision-making, slow deployment, and weak cross-role collaboration—this paper proposes and implements Web Intelligence Assistant, an integrated AI modeling decision-support framework. Methodologically, it unifies three core capabilities: structured requirement elicitation, literature-driven verifiable recommendation, and executable prototype generation. These are realized through four coordinated modules: interactive questionnaire-based input, intelligent auto-filling, evidence-based scientific literature retrieval, and template-driven code generation. Technically, the framework integrates natural language understanding, domain-specific knowledge graphs, and an interactive web frontend to enable collaborative decision-making among product managers, developers, and researchers. Empirical validation across diverse real-world scenarios demonstrates substantial reduction in modeling design cycle time, with recommendation accuracy and prototype usability reaching practical thresholds. Its key contribution lies in being the first framework to deeply couple requirement modeling, evidence-based recommendation, and executable prototype generation—thereby bridging a critical gap in the AI engineering decision-support toolchain.

Technology Category

• Humans and AI: Intelligent User Interfaces
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Intelligent Robots: Embodied AI

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

The Science Consultant Agent is a web-based Artificial Intelligence (AI) tool that helps practitioners select and implement the most effective modeling strategy for AI-based solutions. It operates through four core components: Questionnaire, Smart Fill, Research-Guided Recommendation, and Prototype Builder. By combining structured questionnaires, literature-backed solution recommendations, and prototype generation, the Science Consultant Agent accelerates development for everyone from Product Managers and Software Developers to Researchers. The full pipeline is illustrated in Figure 1.

Problem

• Helps select effective AI modeling strategies
• Accelerates development with literature-backed recommendations
• Generates prototypes for diverse practitioner needs

Innovation

• Web-based AI tool for modeling strategy selection
• Four components: questionnaire, smart fill, recommendation, prototype
• Accelerates development with literature-backed recommendations and prototypes