A Hybrid Intelligence Approach to Wrong Answer Diagnosis and Adaptive Recommendation in Online Examination Systems: Leveraging AI and Knowledge Graphs
Keywords:
Online examination system, Wrong answer diagnosis, Precise recommendation, AI, Knowledge graph, Deep learningAbstract
The rapid development of online education has introduced new challenges for wrong answer diagnosis and personalized recommendation mechanisms within online examination systems. This paper proposes a hybrid approach that integrates artificial intelligence with knowledge graph technology to construct a domain-specific disciplinary knowledge graph. By extracting answering characteristics and learning patterns through deep learning techniques, the proposed method achieves efficient diagnosis of incorrect responses and enables accurate personalized recommendations.
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Copyright (c) 2026 Haiying Li, Juan Li, Ling Bian, Zhijia Cheng
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
