Enhancing Educational Feedback Systems using Deep Queue-Dependent Networking: An Automatic StudentAssessment with AI

In the field of educational technology, the enhancement of student learning through personalized feedback mechanisms is of utmost importance. This paper investigates the potential of Deep Queue-Dependent Networking (DQN) to transform educational feedback systems, automating student assessment processes. By utilizing artificial intelligence (AI) techniques, particularly reinforcement learning, the proposed approach aims to enhance the efficacy and effectiveness of feedback delivery in educational environments. With DQN in place, the system will independently evaluate student performance, identify areas for improvement, and tailor feedback responses based on individual learning needs. The seamless integration of AI-driven assessment tools will enable educators to streamline the feedback process, offer timely interventions, and foster more engaging and adaptive student learning experiences. This research contributes to the ongoing conversation about applying advanced technologies in education, instilling hope about the potential of DQN-based systems to improve learning outcomes and student success.