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Stream/Area:
-
Subject:
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Paper ID:
IJSREM67101
Volume:
10 |
9 |
Sep 2026
Pages:
1-11
Publication Frequency:
Monthly (12 issues per year)
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.
Computer Applications
GM University, Davanagere, Karnataka, India
Computer Applications
GM University, Davanagere, Karnataka, India
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
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.
[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.
Publication Date
9/3/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
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