Transformation of the network and new media talent cultivation paradigm based on reinforcement learning and knowledge tracing from the perspective of human-machine collaboration

Key Points

• The aim is to explore how reinforcement learning and knowledge tracing can reshape media talent cultivation through human-machine collaboration.
• Analyzed current media talent development frameworks
• Integrated reinforcement learning models
• Implemented knowledge tracing for personalized learning pathways
• Evaluated collaborative approaches between humans and machines
• Enhanced talent cultivation efficiency was observed
• Improved learning outcomes reported by participants
• Demonstrated increased engagement in media training programs