Abstract:

Synergistic feedback systems represent an important aspect of modern AI-generated resource platforms for learning, striking a balance between personalised user experiences and community-driven quality assurance. In this project, we present a comprehensive framework that addresses this balance by leveraging synergies between individualised recommendations and collective feedback mechanisms. Our framework offers a holistic solution that embraces both personalisation and community engagement, fostering a dynamic ecosystem of knowledge sharing and system refinement. Through advanced AI algorithms, these systems analyse user preferences, behaviour patterns, and content attributes to deliver tailored recommendations while concurrently integrating collective feedback from the user community. By combining the strengths of individualised personalisation with the wisdom of the crowd, our synergistic feedback systems aim to enhance user satisfaction, optimise resource discovery, and ensure the reliability and relevance of the platform content.

The personalisation component of our framework encompasses sophisticated models that analyses user data, such as browsing history, preferences, and feedback, to generate personalised recommendations. These recommendations are tailored to each user's unique interests and needs, thereby enhancing the overall user experience and promoting engagement with platform content. Through continuous learning and adaptation, the personalisation component strives to anticipate user preferences and deliver relevant resources in real-time, facilitating seamless knowledge acquisition, exploration, and learning.

Concurrently, our framework also incorporates a robust system of community-driven quality assurance mechanisms. This component harnesses the collective intelligence of the user community to evaluate and curate platform content, providing feedback, ratings, and reviews that inform subsequent recommendations. By crowdsourcing content evaluation and moderation, the community-driven quality assurance component promotes transparency, accountability, and trustworthiness within the platform ecosystem. Through active participation and collaboration, users contribute to the continuous improvement and refinement of platform resources, ensuring their relevance, accuracy, and integrity.

Overall, our comprehensive framework for synergistic feedback systems offers a balanced approach to managing user interactions and content quality in AI-generated resource platforms. By harmonising personalised recommendations with community-driven quality assurance mechanisms, our framework fosters a dynamic ecosystem of knowledge sharing and refinement, empowering users to discover, explore, and contribute to high-quality resources with confidence and satisfaction.

Keywords:

Synergistic Feedback Systems (SFS), Artificial Intelligence (AI), Education, Human-In-The-Loop (HITL), Human-On-The-Loop (HOTL), Personalisation, Quality.