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This paper reviews digital learning tools designed for handling diverse formats such as PDF, DOCX, TXT, videos, and audio. It explores how modern Natural Language Processing (NLP) methods (e.g., BERT, FAISS, and Hugging Face transformers) provide context-aware responses, ranging from simple factual queries to more complex, nuanced questions. Multimodal content analysis techniques are discussed, including processing YouTube video transcripts for on-the-fly quiz creation and answer validation. We discuss strengths, limitations, and open challenges in real-time quiz generation, advanced document indexing, and multimedia integration. Future research directions include personalized assessments and adaptive content delivery, aimed at improving learner engagement and knowledge retention in digital platforms.

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Contextual Querying and Learning Enhancement Through Document Interaction

  • 会议论文

  • 发表日期：

    2026年

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  • Sai Krishna Tej Talluri,
  • B. Veera Jyothi,
  • Pranav Vuddagiri,
  • Sivani Varada

摘要

This paper reviews digital learning tools designed for handling diverse formats such as PDF, DOCX, TXT, videos, and audio. It explores how modern Natural Language Processing (NLP) methods (e.g., BERT, FAISS, and Hugging Face transformers) provide context-aware responses, ranging from simple factual queries to more complex, nuanced questions. Multimodal content analysis techniques are discussed, including processing YouTube video transcripts for on-the-fly quiz creation and answer validation. We discuss strengths, limitations, and open challenges in real-time quiz generation, advanced document indexing, and multimedia integration. Future research directions include personalized assessments and adaptive content delivery, aimed at improving learner engagement and knowledge retention in digital platforms.