Artificial Intelligence Empowers Organic Spectroscopy Analysis Teaching —Focusing on Innovative Teaching Models and Intelligent Assessment System Construction
Year: 2025
Type: article
Abstract: Organic Spectroscopy Analysis, as a core course in chemistry disciplines, has long faced teaching challenges including highly abstract theoretical content, limited practical resource accessibility, and delayed assessment feedback. This study proposes a restructuring of the teaching model and evaluation system through artificial intelligence (AI) technology. By developing AI-driven seminar-style, problem-driven, and project-based teaching frameworks, constructing an AI-assisted spectroscopic interpretation training platform, designing intelligent-guided personalized learning pathways, and establishing a machine learning-powered dynamic assessment system, we aim to achieve intelligent transformation across the entire teaching process. Leveraging the significant advantages of AI technology in addressing traditional pedagogical limitations and cultivating students’ critical thinking capabilities, this approach provides a replicable implementation pathway for reforming chemistry-related curricula.
Source: Education Study
DOI: 10.35534/es.0704099
Authors: Xu Peng, Xiong Wei, Zhang Jinling
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W4410416366