Author(s)

Junxi Liang ¹, Xinjie Wang ¹, Qiong Su ¹

Affiliation(s)

¹ Chemical Engineering Institute, Northwest Minzu University, Lanzhou, China

Corresponding Author

Junxi Liang

ABSTRACT

As science and technology continue to advance rapidly, the integration of artificial intelligence (AI) into education has become increasingly prevalent. Analytical chemistry, a key foundational subject in the field of chemistry, is currently facing significant challenges within its traditional training model, particularly in areas such as curriculum content, instructional methods, and evaluation systems. This research utilizes a combination of literature review, case studies, and practical teaching experience to investigate a new AI-enhanced training model for analytical chemistry education. By developing an intelligent teaching platform, incorporating virtual simulation experiments, and leveraging AI for data processing and instrument control, the curriculum framework is restructured and a new AI-driven evaluation system is introduced. The results demonstrate that this innovative approach greatly enhances the quality of instruction and effectively fosters students' independent learning skills, creative problem-solving abilities, and data analysis proficiency, thereby offering fresh perspectives and strategies for talent development in analytical chemistry.

KEYWORDS

Artificial intelligence; analytical chemistry; talent cultivation; teaching reform; new paradigm

CITE THIS PAPER

Junxi Liang, Xinjie Wang, Qiong Su, AI-Enabled Talent Training in Analytical Chemistry. Curriculum and Teaching Methodology (2025) Vol. 8: 1-7. DOI: http://dx.doi.org/10.23977/curtm.2025.080901.

REFERENCES

[1] Bond, M., Khosravi, H., De Laat, M., Bergdahl, N., Negrea, V., Oxley, E., Pham, P., Chong, S. W., and Siemens, G. (2024). A meta systematic review of artificial intelligence in higher education: A call for increased ethics, collaboration, and rigour. International Journal of Educational Technology in Higher Education, 21, 4. 
[2] Luo, J., Zheng, C., Yin, J., and Teo, H. H. (2025). Design and assessment of AI-based learning tools in higher education: A systematic review. International Journal of Educational Technology in Higher Education, 22, 42. 
[3] Liang, J., Ren, F., and Su, Q. (2024). Exploration of blended teaching methods and reform measures in analytical chemistry. Advances in Educational Technology and Psychology, 8(4), 142-149. 
[4] Chan, P., Van Gerven, T., Dubois, J.-L., and Bernaerts, K. (2021). Virtual chemical laboratories: A systematic literature review of research, technologies and instructional design. Computers & Education Open, 2, 100053. 
[5] Bradley, C. (2025). Integrating AI and machine learning in analytical chemistry. Lab Manager. https://www.labmanager.com/integrating-ai-and-machine-learning-in-analytical-chemistry-34221.

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