Personalized content generation in family education based on deep learning

Area of Science:

• Artificial Intelligence
• Educational Technology
• Computational Linguistics

Background:

• Personalized learning content generation in family settings is a growing research area.
• Existing methods often lack flexibility in adapting to learner context and family dynamics.
• Deep learning techniques offer new possibilities for advanced educational content recommendation.

Purpose of the Study:

• To propose an Adaptive Content Generation system for Family Education (DL-ACG-FE) using deep learning.
• To enhance personalization of educational content by considering learner interactions and family factors.
• To demonstrate the computational efficiency and feasibility of the proposed DL-ACG-FE framework.

Main Methods:

• Utilized Transformer-based NLP models for contextual content representation.
• Employed reinforcement learning to optimize content delivery policies based on learner interaction signals.
• Integrated sentiment-based weighting systems with structured performance indicators.
• Implemented curriculum-constrained sequencing for pedagogical coherence.

Main Results:

• DL-ACG-FE demonstrated significant improvements over baseline models: 15.7% in Accuracy, 15.9% in F1-score, and 16.9% in Adaptive Content Relevance Score (ACRS).
• Short-term retention analysis over 6 weeks showed improved comparative stability.
• Experimental results confirmed the computational efficiency of the adaptive deep learning approach in controlled settings.

Conclusions:

• The proposed DL-ACG-FE system shows significant potential for personalized family education.
• The findings validate the feasibility of using adaptive deep learning for educational content generation.
• Further research is needed for real-world deployment, long-term validation, and scalability testing.