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Paper ID:IJSREM67101

SmartSlide AI: An Intelligent Chatbot-Driven System for Automated PowerPoint and PDF Presentation Generation

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM67101

Volume:

10 |

9 |

Sep 2026

Pages:

1-11

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM67101

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

VA

Vijayalaxmi Angadi

Computer Applications

GM University, Davanagere, Karnataka, India

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VKS

Varun K S

Computer Applications

GM University, Davanagere, Karnataka, India

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Abstract

Creating a well-structured slide presentation is a time-consuming task that combines research, content writing, visual design, and file-format handling. This paper presents SmartSlide AI, a conversational, chatbot-driven web platform that converts a natural-language request into a fully editable presentation, exported simultaneously as an OOXML PowerPoint (.pptx) file and a print-ready PDF. The system decomposes presentation authoring into a five-stage pipeline — requirement extraction, outline generation, slide-content generation, layout/theme rendering, and dual-format export — coordinated by a REST API backend and an isolated Node.js presentation engine built on pptxgenjs. A provider-abstraction layer allows the AI engine to call OpenAI, Google Gemini, or Anthropic Claude interchangeably, while a deterministic “Demo AI Mode” guarantees the full pipeline remains demonstrable with no external API dependency and no fabricated citations The prototype was implemented end-to-end — authentication, project dashboard, chat-based requirement gathering, an in-browser slide editor, and export — and validated with an automated test suite (16 test cases, 100% pass rate) and empirical export-latency measurements. Results show that a complete 10-slide, thematically consistent, editable presentation can be produced and exported in under two seconds of computation (excluding network-bound LLM latency), demonstrating that a chatbot interface can meaningfully compress the presentation-authoring workflow while preserving full editability and design consistency.

KEYWORDS

Conversational AI Chatbot Natural Language Generation Automated Presentation Generation PPTX PowerPoint Automation Large Language Models Retrieval-Augmented Generation Human-Computer Interaction Web Application Architecture.

Cite This Article

Vijayalaxmi Angadi, Varun K S, "SmartSlide AI: An Intelligent Chatbot-Driven System for Automated PowerPoint and PDF Presentation Generation," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 9, pp. 1-11, Sep 2026. https://doi.org/10.55041/IJSREM67101

Full Article/Conclusion

This paper presented SmartSlide AI, a conversational chatbot-driven system that turns a natural-language request into an editable, dual-exported (PPTX + PDF) slide presentation. By staging generation into requirement extraction, editable outlining, layout-aware content generation, and a strictly native-object export engine, the system produces genuinely editable office files rather than rendered images, while a deterministic Demo AI Mode and defensive JSON-repair logic keep every code path demonstrable and crash-free even without a live LLM subscription. An automated test suite (16/16 passing), zero OOXML validation errors, and sub-two-second file-generation latency across the tested range of slide counts support the system's readiness as a working MCA-level prototype, and the identified future extensions — particularly retrieval-augmented, citation-grounded content generation — offer a clear path toward a more research-oriented deployment.

References

[1]  A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention Is All You Need,” in Proc. 31st Conf. Neural Information Processing Systems (NeurIPS), 2017. [2] T. Brown, B. Mann, N. Ryder, et al., “Language Models are Few-Shot Learners,” in Proc. 34th Conf. Neural Information Processing Systems (NeurIPS), 2020. [3] J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” in Proc. NAACL-HLT, 2019. [4]   P. Lewis, E. Perez, A. Piktus, et al., “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks,” in Proc. 34th Conf. Neural Information Processing Systems (NeurIPS), 2020. [5]  OpenAI, “GPT-4 Technical Report,” arXiv:2303.08774, 2023. [6]   G. Team, Google DeepMind, “Gemini: A Family of Highly Capable Multimodal Models,” arXiv:2312.11805, 2023. [7]    Anthropic, “Model Cards and Documentation,” Anthropic PBC, 2024. [Online]. Available: https://www.anthropic.com [8]     S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997. [9]   I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA, USA: MIT Press, 2016. [10]     Ecma International, “Office Open XML File Formats,” ECMA-376, 5th ed., 2021.

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Publication Date

9/3/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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