Enhancing User Engagement through Adaptive Content Generation
Generating adaptable content has evolved significantly recently, driven by the need to grab and keep users’ attention. These models craft and adjust content based on user preferences, continually making content better and more appealing over time. Using these techniques, personalized content generation relies on user history and feedback to engage users—a crucial step in achieving desired goals. In this combined approach that merges different methods in this field, we introduce a model that uses user feedback, creates content, and adds variety to it. This model consists of three main parts: a part that uses Reinforcement Learning (RL) to understand user feedback and supervise the content created by the second part, the Generative Model (like GANs). In this context, RL ensures that the content generated by the Generative Model is accurate and aligns with what users prefer. Additionally, a Procedural Content Generation component contributes to diversifying content and generating new material based on the results of the previous two sections.
Contribution: In Computer-Supported Speech and Language Therapy (SLT), this work holds significant potential to enhance user engagement with the services offered through our methodology. It is very important, particularly to engage children with speech disorders in the exercises and tasks generated by our system. There are two key reasons why this is crucial: Firstly, such engagement is a must in aiding these individuals in their journey to overcome their speech disorders. Secondly, it contributes to optimizing the model’s output, resulting in higher quality and greater accuracy that aligns with user preferences. Consequently, this creates a continuous cycle of practice and training for children and individuals with speech disorders, and over time, increases the quality of the services provided.
Challenges: 1. Data Privacy: Managing sensitive data, especially children’s data, poses a dual challenge: ensuring data privacy while maintaining data quality for system training. 2. Balancing Personalization and Quality: Achieving the right balance between personalized content and high quality is challenging. Effectively integrating user preferences without compromising overall content quality is a key factor. 3. Content Diversity: Maintaining engaging and diverse content, especially in fields like speech therapy, is a complex task for Procedural Content Generation. 4. Real-time Adaptation: The technical challenge of dynamically adapting content based on user interactions and feedback adds complexity to the project. Collaboration Workflow: The user interacts with the system, specifying preferences or requirements for the content. RL component receives user input and generates a personalized policy for content generation. Generative Models take into account the user-specific features and preferences provided by RL and create content accordingly. Procedural Content Generation techniques introduce diversity or adapt content templates based on the generative models’ output. The generated content is presented to the user for interaction and feedback. RL collects user feedback, assesses content quality, and updates the policy for subsequent content generation iterations. This collaborative approach ensures that the content is not only personalized but also of high quality, diverse, and engaging, thereby enhancing the overall user experience.