Just-in-Time News: An AI Chatbot for the Modern Information Age
Abstract
:1. Introduction
- New Architecture: Introduces a new architecture combining conversational AI with generative AI capability, RPA, a news database, and an LLM for dynamic news understanding and summarization.
- Complexity Analysis: Provides a detailed mathematical complexity analysis, offering insights into system performance and optimization.
- Robustness of Solution: Demonstrates the chatbot’s robustness in handling a vast combinatorial space of potential news classifications (53,916,650 unique combinations).
- Real-world Implementation: Implemented and evaluated with a massive news database (989,432 reports from 2342 sources), distinguishing it from previous studies with smaller datasets.
- Multi-Platform Deployment: Deployed on Microsoft Teams and as a standalone web application, showcasing practical usability in various contexts.
- High Performance: Achieved an F1-score of 0.97, recall of 0.99, and precision of 0.96, demonstrating superior performance in delivering accurate news summaries.
2. Background & Literature Review
3. Materials and Methods
3.1. Mathematical Model
3.1.1. Transformer Architecture
3.1.2. Statistical Language Modeling
3.1.3. Hypothetical Equations
- Calculate QKT = [[19, 22], [43, 50]].
- Divide by dk: [[9.5, 11], [21.5, 25]].
- Apply softmax: [[0.1192, 0.2689], [0.8808, 0.7311]].
- Multiply by V: [[3.4417, 3.7568], [10.5583, 11.2432]].
- Calculate Wh ht = [0.52, 0.48].
- Apply softmax: [0.5498, 0.4502].
3.1.4. AI Studio Playground Parameters
- For T = 0.5 (low temperature): [0.9643, 0.0354, 0.0003]. The model becomes highly focused on the most likely word.
- For T = 1.0 (normal temperature): [0.7, 0.2, 0.1]. The probabilities remain unchanged.
- For T = 2.0 (high temperature): [0.5946, 0.2649, 0.1405]. The model’s output becomes more diverse, considering less likely words.
- Summary 1: “The President addressed the nation, outlining a new economic plan”.
- Summary 2: “Amidst growing concerns, the President announced a controversial economic strategy”.
- Summary 3: “The President’s speech sparked outrage, with critics denouncing the proposed economic measures”.
- These summaries differ in their tone and emphasis. Summary 1 is neutral, Summary 2 highlights controversy, and Summary 3 focuses on negative reactions.
- Low Temperature (e.g., T = 0.5): The LLM will likely select the most probable and neutral summary, which is often the safest option. In this case, it would likely choose Summary 1.
- Normal Temperature (e.g., T = 1.0): The LLM has more flexibility in its selection, potentially choosing a slightly more nuanced summary like Summary 2.
- High Temperature (e.g., T = 2.0): The LLM is encouraged to explore less probable and potentially more dramatic summaries, possibly selecting Summary 3, which highlights the controversy and strong reactions.
3.2. Architecture
3.3. News Aggregation Process
- “The LLM can identify and remove biases in news reporting by comparing and contrasting different sources”.
- “By using multiple sources, the LLM can generate a more comprehensive and objective summary of the news event”.
- “Having more news media reporting the same event can help to mitigate the impact of bias on the summarization process”.
3.4. Implementation Strategy
3.5. Agent Communication
- User Initiation: The user initiates the interaction by requesting specific news analytics.
- Intent Identification: The AI-Chatbot, leveraging its knowledge base and natural language understanding capabilities, identifies and reconfirms the user’s intent.
- Information Gathering: The chatbot engages in a brief dialogue with the user to gather essential information, such as the type of event and location of interest.
- RPA Activation: The chatbot relays the gathered information to the Power Automate RPA, initiating the news retrieval process.
- FetchXML Query Generation: The RPA, utilizing the Google Gemini LLM, dynamically generates a FetchXML query tailored to the user’s request.
- News Retrieval: The FetchXML query is executed against the Dataverse News Database, retrieving relevant news articles.
- News Summarization: The retrieved news articles are processed by the Google Gemini LLM to generate a concise summary.
- Response Delivery: The summarized news content is returned to the chatbot, which presents it to the user in a clear and user-friendly format.
3.6. Complexity Analysis
3.6.1. Conversational AI
3.6.2. RPA Engine
3.6.3. LLM Operations
3.6.4. News Database
3.6.5. Overall System Complexity
4. Results
Code 1: Fetch XML Generated by LLM for Querying the News Database | |||||
1. | <fetch version=“1.0” output-format=“xml-platform” mapping=“logical” distinct=“false”> | ||||
2. | <entity name=“news_database”> | ||||
3. | <attribute name=“dfs_newsdatabaseid”/> | ||||
4. | <attribute name=“dfs_title”/> | ||||
5. | <attribute name=“dfs_description”/> | ||||
6. | <attribute name=“dfs_eventcode”/> | ||||
7. | <attribute name=“dfs_sourceurl”/> | ||||
8 | <attribute name=“dfs_firsteventcountry”/> | ||||
9. | <attribute name=“dfs_secondeventcountry”/> | ||||
10. | <attribute name=“dfs_rating”/> | ||||
11. | <order attribute=“dfs_rating” descending=“true”/> | ||||
12. | <filter type=“and”> | ||||
13. | <condition attribute=“dfs_eventcode” operator=“in”> | ||||
14. | <value>Cybersecurity News</value> | ||||
15. | <value>Nation State Hacking</value> | ||||
16. | <value>Globally Disruptive Cyber Attack</value> | ||||
17. | <value>Ransomware Attack News</value> | ||||
18. | </condition> | ||||
19. | </filter> | ||||
20. | </entity> | ||||
21. | </fetch> |
5. Discussion
5.1. Performance Evaluation
5.2. Robustness of Solution
5.3. Ethical AI and Handling of Bias
6. Conclusions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A. Code Snippet for the News AI-Chatbot
1. Recognizing Intent kind: AdaptiveDialog beginDialog: kind: OnRecognizedIntent id: main intent: triggerQueries: - latest news - news update - what’s happening in the world - top headlines - current events - breaking news - news summary - recent news - news - global events - events - More News actions: - kind: Question id: question_nVBxtb interruptionPolicy: allowInterruption: true |
variable: init:Topic.vRecentNewsYesNo prompt: Do you want recent news update on any topic? entity: BooleanPrebuiltEntity - kind: ConditionGroup id: conditionGroup_8 × 9ql9 conditions: - id: conditionItem_KPq53v condition: =Topic.vRecentNewsYesNo = true actions: - kind: Question id: question_G5U72d interruptionPolicy: allowInterruption: true |
alwaysPrompt: true variable: init:Topic.vNewTopic prompt: Which topic are you interested in now (e.g., politics, military, sports, UFO anything)? entity: StringPrebuiltEntity - kind: Question id: question_xDXPaD interruptionPolicy: allowInterruption: true |
repeatCount: 1 alwaysPrompt: true variable: init:Topic.vCountry prompt: Do you want global news or are interested in News applicable to a specific country (e.g., USA, UK, Canada, Australia, Congo etc.)? defaultValueResponse: Couldn’t find the required country. Hence, setting it as global. defaultValue: Global entity: CountryOrRegionPrebuiltEntity - kind: SendActivity id: sendActivity_UWtZQT activity: text: - Please stay with me while, I curate {Topic.vNewTopic} News for you. - I am going to obtain relevant events on {Topic.vNewTopic} now. Please wait for few seconds. - kind: InvokeFlowAction id: invokeFlowAction_UJW82A input: binding: text: =Topic.vNewTopic text_1: =Topic.vCountry output: binding: vfetchxml: Topic.vFetchXML flowId: 883880cd-597a-ef11-a671-7c1e521a13f0 - kind: SendActivity id: sendActivity_rFUSCP activity: “{Topic.vFetchXML}” - kind: SendActivity id: SendActivity_Oimz0N activity: text: - | Any more news updates? quickReplies: - kind: MessageBack text: More News |
- kind: MessageBack text: Thats All 2. System Redirect & Feedback kind: AdaptiveDialog startBehavior: CancelOtherTopics beginDialog: kind: OnSystemRedirect id: main actions: - kind: Question id: 41d42054-d4cb-4e90-b922-2b16b37fe379 conversationOutcome: ResolvedImplied alwaysPrompt: true variable: init:Topic.SurveyResponse prompt: Did that answer your question? entity: BooleanPrebuiltEntity |
- kind: ConditionGroup id: condition-0 conditions: - id: condition-0-item-0 condition: =Topic.SurveyResponse = true actions: - kind: CSATQuestion id: csat_1 conversationOutcome: ResolvedConfirmed |
- kind: SendActivity id: sendMessage_8r29O0 activity: Thanks for your feedback. |
- kind: Question id: question_1 alwaysPrompt: true variable: init:Topic.Continue prompt: Can I help with anything else? entity: BooleanPrebuiltEntity |
- kind: ConditionGroup id: condition-1 conditions: - id: condition-1-item-0 condition: =Topic.Continue = true actions: - kind: SendActivity id: sendMessage_4eOE6h activity: Go ahead. I’m listening. |
elseActions: - kind: SendActivity id: yHBz55 activity: Ok, goodbye. |
- kind: EndConversation id: jh1GMT |
elseActions: - kind: Question id: PM68ot alwaysPrompt: true variable: init:Topic.TryAgain prompt: Sorry I wasn’t able to help better. Would you like to try again? entity: BooleanPrebuiltEntity |
- kind: ConditionGroup id: KNxYBf conditions: - id: DPveFP condition: =Topic.TryAgain = false actions: - kind: BeginDialog id: cngqi4 dialog: crd69_copilot.topic.Escalate |
elseActions: - kind: SendActivity id: GrVHEW activity: Go ahead. I’m listening. |
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Main Area | Sub-Area | Paper |
---|---|---|
Business | Customer Relationship Management | [21] |
Business | Customer Service | [9,10] |
Business | Marketing | [11] |
Business | Business Process Automation | [12] |
Business | Restaurant Management | [22] |
Business | Online Shopping Assistance | [32] |
Business | Knowledge Industries | [33] |
Chemistry | Organic Chemistry | [30] |
Computer Science | Code Generation | [34] |
Computer Science | Natural Language Processing | [35,36,37,38] |
Computer Science | Software Development | [39] |
Computer Science | Chatbot Error Correction | [40] |
Computer Science | Chatbot Evaluation | [41] |
Computer Science | Human-Computer Interaction | [42] |
Education | Assessment and Evaluation | [43] |
Education | Educational Chatbots | [23] |
Education | Educational Data Mining | [44] |
Education | Educational Technology | [45] |
Education | Language Learning | [6,27,28,46,47,48] |
Education | Learning and Teaching | [49] |
Education | Learning Management System | [50] |
Education | Personalized Learning | [5] |
Education | Student Engagement | [7] |
Education | Student Feedback & Support | [8,51] |
Education | Technology Adoption | [52,53,54] |
Education | Surgical Education | [53] |
Healthcare | Health Information | [24] |
Healthcare | Immunization Information | [55] |
Healthcare | Medical Diagnosis | [2] |
Healthcare | Medical Information Retrieval | [3,4] |
Healthcare | Sexual and Reproductive Health | [26] |
Healthcare | Pathology | [56] |
Healthcare | Radiotherapy | [57] |
Linguistics | Language Learning | [6,27,28,46,47,48] |
Mental Health | Depression Interventions | [29] |
Mental Health | Emotion Recognition | [1] |
Mental Health | Empathy and Well-being | [58] |
Mental Health | Mental Health Care | [31] |
Mental Health | Psychotherapy | [59] |
Psychology | Mental Health | [29,31,58,59] |
Marketing | Ethical Use of Chatbots | [60] |
Paper | News/Event Analysis | Real-Time Updates | Technical Insights | Connection with Live Database | Deployed Standalone Custom Chatbot | Detailed Performance Evaluation |
---|---|---|---|---|---|---|
[1] | No | No | Yes | No | No | Yes |
[50] | No | No | Yes | No | Yes | Yes |
[8] | No | No | No | No | No | Yes |
[35] | No | No | No | Yes | Yes | No |
[2] | No | No | Yes | Yes | Yes | Yes |
[23] | No | No | No | No | No | Yes |
[36] | No | No | Yes | No | No | No |
[3] | No | No | Yes | No | Yes | Yes |
[9] | No | No | Yes | No | Yes | No |
[49] | No | No | No | No | Yes | Yes |
[34] | No | No | Yes | No | No | Yes |
[51] | No | No | Yes | No | No | Yes |
[44] | No | No | No | No | No | Yes |
[5] | No | No | No | No | No | Yes |
[46] | No | No | No | No | Yes | Yes |
[11] | No | No | Yes | No | Yes | Yes |
[21] | No | No | Yes | No | Yes | Yes |
[43] | No | No | No | No | No | Yes |
[45] | No | No | No | No | No | Yes |
[7] | No | No | No | No | No | Yes |
[22] | No | No | Yes | No | Yes | Yes |
[37] | No | No | Yes | No | No | Yes |
[30] | No | No | Yes | No | No | Yes |
[59] | No | No | No | No | No | Yes |
[58] | No | No | Yes | No | Yes | Yes |
[47] | No | No | Yes | No | No | Yes |
[24] | No | No | Yes | No | No | No |
[6] | No | No | No | No | Yes | Yes |
[39] | No | No | Yes | Yes | Yes | Yes |
[61] | No | No | No | No | Yes | Yes |
[26] | No | No | Yes | No | Yes | Yes |
[29] | No | No | Yes | No | Yes | Yes |
[4] | No | No | Yes | No | No | Yes |
[31] | No | No | Yes | No | Yes | Yes |
[48] | No | No | Yes | No | No | Yes |
[33] | No | No | Yes | No | No | Yes |
[32] | No | No | No | No | No | Yes |
[52] | No | No | Yes | No | Yes | Yes |
[53] | No | No | Yes | No | Yes | Yes |
[55] | No | No | Yes | No | Yes | Yes |
[54] | No | No | No | No | No | Yes |
[12] | No | No | Yes | No | No | No |
[10] | No | No | Yes | No | No | Yes |
[27] | No | No | Yes | No | No | Yes |
[28] | No | No | Yes | No | No | Yes |
[38] | No | No | Yes | No | No | Yes |
[41] | No | No | Yes | No | No | Yes |
[56] | No | No | Yes | No | No | No |
[40] | No | No | Yes | No | No | No |
[42] | No | No | Yes | No | No | Yes |
[60] | No | No | No | No | No | No |
[57] | No | No | Yes | No | Yes | No |
Component Name | Calculation of Complexity |
---|---|
Complexity of the conversational AI | |
Complexity of RPA engine | |
Complexity of LLM operation | |
Complexity of News database | |
Overall Complexity of the proposed system |
Event Category | Count of Sub-Categories | Count of Title | Count of Source URL |
---|---|---|---|
Politics, Governance, and International Affairs | 19 | 190,702 | 163,466 |
Industry and Business News | 27 | 137,150 | 131,439 |
Economic and Financial News | 19 | 108,615 | 96,597 |
Crime, Safety, and Security | 25 | 95,859 | 85,790 |
Entertainment and Culture | 15 | 94,974 | 87,577 |
Uncategorized | NA | 88,499 | 81,278 |
Human Rights and Social Issues | 16 | 62,090 | 55,921 |
Disasters, Accidents, and Crisis | 20 | 32,851 | 27,783 |
Science and Technology | 19 | 29,372 | 28,008 |
Environment and Climate | 7 | 26,564 | 24,772 |
Legal and Justice | 4 | 25,588 | 23,560 |
Health and Medicine | 12 | 19,326 | 18,266 |
Lifestyle and Trends | 7 | 15,801 | 15,055 |
Education and Learning | 2 | 10,961 | 10,391 |
Media and Communication | 8 | 5782 | 5405 |
Unusual and Extraordinary Events | 2 | 848 | 793 |
Event Category | Community | Global | International | Local | Nation | Region | State | Total |
---|---|---|---|---|---|---|---|---|
Unusual and Extraordinary Events | 70 | 107 | 180 | 217 | 226 | 14 | 34 | 848 |
Science and Technology | 1916 | 12,143 | 8256 | 3089 | 3474 | 176 | 261 | 29,315 |
Politics, Governance, and International Affairs | 704 | 671 | 74,214 | 15,104 | 79,084 | 1171 | 19,677 | 190,625 |
Media and Communication | 368 | 1076 | 798 | 1080 | 2293 | 27 | 133 | 5775 |
Lifestyle and Trends | 4185 | 703 | 2543 | 6392 | 1496 | 143 | 325 | 15,787 |
Legal and Justice | 798 | 634 | 3285 | 8070 | 10,996 | 8 | 1753 | 25,544 |
Industry and Business News | 4046 | 28,030 | 23,878 | 36,149 | 39,019 | 1576 | 3839 | 136,537 |
Human Rights and Social Issues | 3956 | 266 | 9554 | 25,347 | 17,308 | 206 | 5441 | 62,078 |
Health and Medicine | 3057 | 1826 | 2494 | 5476 | 5569 | 101 | 786 | 19,309 |
Environment and Climate | 1240 | 2732 | 2996 | 13,387 | 3716 | 1022 | 1444 | 26,537 |
Entertainment and Culture | 11,411 | 2667 | 24,269 | 35,928 | 19,218 | 82 | 1123 | 94,698 |
Education and Learning | 586 | 158 | 516 | 5038 | 3359 | 36 | 1262 | 10,955 |
Economic and Financial News | 1682 | 26,844 | 11,320 | 18,888 | 46,571 | 1241 | 1450 | 107,996 |
Disasters, Accidents, and Crisis | 637 | 204 | 1217 | 23,114 | 3301 | 1697 | 2677 | 32,847 |
Crime, Safety, and Security | 2337 | 2953 | 17,156 | 43,563 | 22,806 | 662 | 6367 | 95,844 |
Total | 44,300 | 85,885 | 201,618 | 277,548 | 275,089 | 8720 | 49,963 | 943,123 |
Reference | Number of News Articles | Chatbot Integration |
---|---|---|
[25] | 33,979 | No |
[13,15,19,20] | 22,425 | No |
This Study | 989,432 | YES |
User Topic of Interest | Location | TP | FP | FN | Precision | Recall | F1-Score |
---|---|---|---|---|---|---|---|
Cyber | USA | 34 | 1 | 1 | 0.971429 | 0.9714 | 0.97143 |
Cyber | Global | 33 | 0 | 1 | 1 | 0.9706 | 0.98507 |
Cyber | UK | 39 | 1 | 2 | 0.975 | 0.9512 | 0.96296 |
Crimes | UK | 21 | 1 | 3 | 0.954545 | 0.875 | 0.91304 |
Elections | USA | 15 | 1 | 2 | 0.9375 | 0.8824 | 0.90909 |
Elections | UK | 23 | 1 | 2 | 0.958333 | 0.92 | 0.93878 |
Politics | Global | 28 | 2 | 2 | 0.933333 | 0.9333 | 0.93333 |
Politics | USA | 13 | 3 | 2 | 0.8125 | 0.8667 | 0.83871 |
Politics | Australia | 9 | 0 | 0 | 1 | 1 | 1 |
Business & Finance | USA | 41 | 2 | 1 | 0.953488 | 0.9762 | 0.96471 |
War | Global | 9 | 0 | 2 | 1 | 0.8182 | 0.9 |
Terrorism | UK | 35 | 3 | 1 | 0.958785 | 0.991 | 0.97464 |
Total | 300 | 15 | 6 | 0.957995 | 0.9874 | 0.97249 |
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Sufi, F. Just-in-Time News: An AI Chatbot for the Modern Information Age. AI 2025, 6, 22. https://doi.org/10.3390/ai6020022
Sufi F. Just-in-Time News: An AI Chatbot for the Modern Information Age. AI. 2025; 6(2):22. https://doi.org/10.3390/ai6020022
Chicago/Turabian StyleSufi, Fahim. 2025. "Just-in-Time News: An AI Chatbot for the Modern Information Age" AI 6, no. 2: 22. https://doi.org/10.3390/ai6020022
APA StyleSufi, F. (2025). Just-in-Time News: An AI Chatbot for the Modern Information Age. AI, 6(2), 22. https://doi.org/10.3390/ai6020022