Guan-Ying Tseng

Papers

Total Citations

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About

Guan-Ying Tseng is an emerging researcher at the intersection of artificial intelligence and education, whose work focuses on developing intelligent systems for collaborative human-machine learning environments. Her primary research areas include transformer-based natural language processing, computational intelligence mechanisms, and educational robotics. In her most cited work, "Transformer-Based Semantic SBERT Robot with CI Mechanism for Students and Machine Co-Learning" (2024), Tseng proposed an innovative framework that integrates Sentence-BERT semantic understanding with computational intelligence to create a robot capable of meaningful interaction among teachers, teaching assistants, and students. This system employs attention ontology to dynamically adapt to the learning context, enabling a truly co-learning experience where human participants and AI collaborate to enhance educational outcomes. With 6 citations in its first year, this paper signals growing interest in her approach to human-centered AI in pedagogy. Tseng’s contributions are particularly notable for bridging advanced NLP techniques with practical classroom applications, offering a scalable model for integrating AI assistants into collaborative learning environments—a timely contribution as education increasingly embraces hybrid and technology-enhanced modalities.