Research on the Teaching Reform of the "Human-Machine Collaboration-R-A-R" Model in the Biomedical Detection Technology Course under the Background of New Engineering
DOI:
https://doi.org/10.55578/fepr.2607.010Keywords:
New Engineering, Biomedical Detection Technology, human-machine collaboration, R-A-R flipped classroom, process-oriented assessmentAbstract
New Engineering construction places strong emphasis on keeping course content updated in step with industrial technologies, engineering practice, and the cultivation of interdisciplinary competencies. The Biomedical Detection Technology course is characterized by pronounced interdisciplinarity, rapid knowledge renewal, and high demands on experimental resources. Traditional lecture-based teaching is prone to problems such as outdated content, insufficient student engagement, and difficulty in transferring theory to real-world scenarios [1-3]. In light of the actual teaching conditions of this course, this paper proposes a "human-machine collaboration-R-A-R" teaching reform scheme: classroom organization is restructured through the three-stage reporting model of Review-Analyze-Reflect; layered note-taking with blue and red pens is used to promote students' reprocessing of AI-generated content; and maker projects together with AI error-correction tasks are used to enhance students' practical ability and critical thinking. This article discusses the scheme design, implementation process, evaluation system, and expected outcomes. Research shows that this scheme can strengthen students' knowledge integration, engineering expression, and active learning ability under conditions of limited class hours and resources, providing an operational reference for the reform of biomedical engineering courses.
References
1. Li Y, Zhang L, Feng Y, et al. A preliminary exploration of teaching reform in the course Principles and Applications of Sensors [J]. Internet of Things Technologies, 2023, 13(01): 161–162.
2. Yu C, Sheng S, Kou X. Research on the teaching reform of the “Intelligent Sensing and Detection Technology” course based on OBE under the background of New Engineering [J]. ETR, 2024, 2(10): 114–116.
3. Zhu L, Zhao Y, Yang H, et al. Exploration and practice of teaching reform in the course design of Process Control Systems based on the OBE concept [J]. Advances in Education, 2025, 15(11): 15–27.
4. Tarun B, Du H, Kannan D, et al. Adaptive learning human–computer interaction systems based on generative AI [EB/OL]. arXiv:2508.11062, 2025.
5. University of Oxford Centre for Teaching and Learning. Guidance on using generative AI tools in teaching [EB/OL]. 2024.
6. Se Ga Group. Reviewing activities within RAR [EB/OL]. 2018.
7. Gurung R A R. Encouraging students to reflect on and evaluate what they have learned [EB/OL]. IDEA Center.
8. Ihara A S, Nakajima K, Kake A, et al. Advantages of handwriting over typing in vocabulary learning: Evidence from the N400 event-related potential index [J]. Frontiers in Human Neuroscience, 2021, 15: 679191.
9. Marano G, Kotzalidis G D, Lisci F M, et al. The neuroscience behind writing: Handwriting versus typing—who wins the battle? [J]. Life, 2025, 15(3): 345.
10. Diachenko I, Kalishchuk S, Zhylin M, et al. Color education: A study of methods affecting memory [J]. Heliyon, 2022, 8(11): e11607.
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Copyright (c) 2026 Minghui Wang, Minghao Chen, Hongliu Yu (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.