AI-Powered Research Analysis
Just-in-Time News: An AI Chatbot for the Modern Information Age
Author: Fahim Sufi
Publication: AI 2025, 6, 22
This research introduces an AI chatbot designed for real-time news delivery and global event analysis. It leverages generative AI (Google Gemini LLM), Robotic Process Automation (RPA), and a comprehensive news database (Microsoft Dataverse) to provide personalized, summarized news. Tested with 35 users and 321 queries, it achieved high performance (F1-score 0.97, recall 0.99, precision 0.96) across 53,916,650 potential news categorizations, demonstrating robust handling of complex news data. Deployed on Microsoft Teams and as a web app, it revolutionizes news consumption and promotes an informed citizenry.
Executive Impact & Key Findings
This analysis extracts critical performance indicators and strategic insights, highlighting the transformative potential for enterprise applications.
989,432
News Reports Analyzed
2,342
Sources Integrated
0.97
F1-Score
53,916,650
Unique News Classifications Handled
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
The study introduces a novel architecture combining conversational AI (Microsoft Copilot) with generative AI (Google Gemini LLM), RPA (Microsoft Power Automate), and a massive news database (Microsoft Dataverse). This enables dynamic query generation and LLM-based summarization, addressing the limitations of static information retrieval.
This innovative design allows the chatbot to understand user intent and provide tailored news updates in real-time, setting it apart from previous AI chatbot solutions.
A detailed mathematical complexity analysis demonstrates the system's efficiency, with overall complexity approximated as O(V + E + D + G + (P × T) + (N × R)).
The chatbot robustly handles a vast combinatorial space of 53,916,650 potential news categorizations, based on country, sub-category, and significance level. Empirical testing with 321 diverse queries validated its ability to navigate this complexity effectively.
Achieved an impressive F1-score of 0.97, recall of 0.99, and precision of 0.96. The average response time is 9 seconds, extending to 25 seconds for complex political events. User feedback averaged 4.3 out of 5 for summarization accuracy.
Deployed on Microsoft Teams and as a standalone web application, showcasing practical usability and accessibility for real-world scenarios.
53,916,650
Unique News Classifications Handled
AI Chatbot News Retrieval & Summarization Process
User Query (Natural Language)
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AI Chatbot Identifies Intent
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RPA Activates & Generates FetchXML Query (via LLM)
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Query Dataverse News Database
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Retrieve Relevant News Articles
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LLM Summarizes News (Contextual Understanding)
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Summarized Tailored News to User
| Feature | Traditional News (Dashboard/App) | AI Chatbot (Proposed System) |
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| Information Delivery |
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| User Interaction |
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| Real-time Analysis |
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| Bias Mitigation |
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| Information Overload |
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| Accessibility |
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Addressing Information Overload with AI
Traditional news consumption methods often overwhelm users with irrelevant or unreliable content, leading to information overload and a distorted perception of reality. The proposed AI Chatbot tackles this by providing users with a personalized and efficient way to stay informed about news that matters most. By combining generative AI, topic-based interaction models, real-time news data integration, and a feedback mechanism, it offers a valuable tool for news consumption and analysis, enhancing comprehension and engagement.
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Estimated Annual Savings
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Annual Hours Reclaimed
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Your AI Implementation Roadmap
A phased approach ensures seamless integration and maximum impact, drawing from the architectural and deployment strategies discussed in the research.
Phase 1: Knowledge Base & Database Foundation
Development of a robust knowledge base (knowledge graph) and creation of a dynamic news database in Microsoft Dataverse, constantly refreshed with news from 2342 diverse sources.
Phase 2: RPA Engine & LLM Integration
Development of Microsoft Power Automate flows to orchestrate information exchange, dynamically generate FetchXML queries using Google Gemini LLM, and retrieve relevant news articles.
Phase 3: LLM Summarization & Ethical AI
Integration of Google Gemini LLM for concise, informative summarization, incorporating ethical AI practices, safety settings, and bias mitigation strategies for sensitive content.
Phase 4: Multi-Platform Deployment & Feedback Loop
Deployment of the AI-Chatbot on Microsoft Teams and as a standalone web application, with a structured feedback mechanism (CSAT rating, adaptive dialog) to continuously improve accuracy and user experience.
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