A Smart Collaborative Learning Platform Using NLP, Deep Learning Models, and Recommendation Algorithms for Automated Content Generation

This study presented the development of a Smart Collaborative Learning Platform that integrated Natural Language Processing (NLP), deep learning models, and recommendation algorithms to automate content generation and enhance learning experiences. The system was designed to process uploaded learning materials and transform them into structured outputs such as summaries, quizzes, and flashcards. It utilized NLP techniques to analyze and understand semantic content, enabling accurate interpretation of user inputs and educational materials. Deep learning models were employed to generate meaningful summaries and insights that supported efficient studying. Additionally, a recommendation engine personalized learning by suggesting relevant topics based on user behavior and performance. The platform also incorporated collaborative features that allowed users to interact, share knowledge, and engage in real-time learning activities. The system was evaluated using ISO 25010 software quality standards, focusing on functionality, usability, reliability, and performance. Results indicated that the platform achieved high user satisfaction and demonstrated strong system performance. Findings showed that the system improved learning efficiency, reduced study time, and enhanced knowledge retention. Furthermore, the integration of AI technologies enabled adaptive and personalized learning experiences. The study highlighted the effectiveness of combining automation and collaboration in modern education. Overall, the proposed platform provided an innovative and scalable solution for improving digital learning environments.

Natural Language Processing (NLP), Collaborative

1. Anderson, T. (2020). The theory and practice of online learning. AU Press. [Google Scholar] [Crossref]

2. Brown, T. B., et al. (2020). Language models are few-shot learners. NeurIPS. [Google Scholar] [Crossref]

3. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press. [Google Scholar] [Crossref]

4. Holmes, W., Bialik, M., & Fadel, C. (2021). Artificial intelligence in education. Center for Curriculum Redesign. [Google Scholar] [Crossref]

5. Johnson, D. W., & Johnson, R. T. (2019). Cooperative learning. [Google Scholar] [Crossref]

6. Jurafsky, D., & Martin, J. (2021). Speech and language processing. [Google Scholar] [Crossref]

7. Pressman, R. (2020). Software engineering: A practitioner’s approach. [Google Scholar] [Crossref]

8. Ricci, F., Rokach, L., & Shapira, B. (2015). Recommender systems handbook. [Google Scholar] [Crossref]

9. Vygotsky, L. S. (1978). Mind in society. Harvard University Press. [Google Scholar] [Crossref]

10. Wei, X., et al. (2025). Generative AI in collaborative learning. [Google Scholar] [Crossref]

11. Zhai, X., et al. (2023). Artificial intelligence in education: A review. [Google Scholar] [Crossref]

• The Role of Artificial Intelligence in Revolutionizing Library Services in Nairobi: Ethical Implications and Future Trends in User Interaction
• ESPYREAL: A Mobile Based Multi-Currency Identifier for Visually Impaired Individuals Using Convolutional Neural Network
• Comparative Analysis of AI-Driven IoT-Based Smart Agriculture Platforms with Blockchain-Enabled Marketplaces
• AI-Based Dish Recommender System for Reducing Fruit Waste through Spoilage Detection and Ripeness Assessment
• SEA-TALK: An AI-Powered Voice Translator and Southeast Asian Dialects Recognition