The safety risk and time-consuming research project “Catalytic oxidation of hydrogen chloride and reaction kinetics” is designed as a comprehensive experiment in applied chemistry in the form of virtual simulation under the impetus of digitalization. The research project has been digitized into an experimental course. Applied chemistry is characterized by both chemistry and chemical engineering. It promotes the cultivation of innovative researcher. The experimental course involves the preparation of catalysts, crystal structure analysis, chemical reaction mechanism prediction, reaction kinetics testing, catalyst characterization and other multi-phase contents. Teaching in the form of digital virtual simulation greatly ensures the safety of the experiment, improves the efficiency of experimental teaching, with three-dimensional effect enhances the students’ sense of experience, and interactivity increases the students’ sense of participation. In the course time, students can experience the whole process of scientific research in catalysis from microscopic chemical structure to macroscopic kinetic processes, which can stimulate their interest in learning and enhance their scientific research literacy.

In the context of the ongoing promotion and implementation of educational digitalization, the Physical Chemistry course at Shaanxi Normal University has introduced the flipped classroom on the Zhihuishu SPOC (Small Private Online Course) platform. This study adopts a dual-mode teaching strategy that is merging the concepts of “combination of online and offline learning” and “integration of pre-class, in-class, and post-class activities”. Various teaching methods and digital assessment techniques have been employed to improve the teaching and learning experience. The primary objective is to foster students’ independent learning abilities and improve their comprehensive competencies.

Using the Analytical Chemistry course as a case study, this paper investigates the construction of knowledge graph-based teaching resources and explores practical approaches for intelligent teaching implementation. The study demonstrates that knowledge graphs enable personalized learning pathways and tailored resource recommendations by analyzing student learning data, thereby enhancing self-directed learning capabilities. Furthermore, real-time feedback mechanisms facilitate targeted instructor interventions and content optimization. The visual representation of knowledge graphs improves knowledge integration and instructional design, enabling data-driven adaptive learning and offering innovative perspectives for educational reform.

Recent advancements in digital and information technologies have significantly enhanced the quality of chemistry education in Chinese universities. The construction and utilization of online information resources have emerged as a key focus in the reform of classroom teaching. This article provides an overview of the development of the “Inorganic Chemistry and Chemical Analysis” course at Tianjin University. It discusses the construction of multidimensional information-based teaching resources linked by knowledge graphs, explores innovations in teaching methodologies, evaluates the effectiveness of teaching applications, and outlines future reform plans.

Cyclic voltammetry (CV) stands as a pivotal electrochemical technique for investigating redox reactions. Traditional CV teaching experiments often lack in-depth exploration of the physicochemical processes underlying CV curves, leading to students' limited comprehension of electrochemical kinetics during CV characterization. To address this pedagogical gap, we developed a digital simulation program using Python to visualize both CV curves and the dynamic concentration changes of electroactive species at the electrode surface. This computational approach not only minimizes material consumption and enhances experimental efficiency but also transforms complex electrochemical data into accessible visual representations through dynamic visualization. The digital simulation experiment serves as a valuable complement to traditional methods, enhancing instructional interactivity and student engagement while fostering greater interest and curiosity in electrochemical studies.

This study explores the construction of a dual-teacher teaching model for inorganic chemistry, leveraging a smart teaching platform and integrating both domestic and international educational resources. Based on constructivist learning theory and the learning pyramid theory, this approach aims to promote open and integrated education while innovating the international curriculum teaching mode. Practice has demonstrated that the digitally empowered dual-teacher teaching model can provide students with comprehensive learning support, achieve multi-dimensional, personalized, and scientific teaching evaluations, effectively enhance students’ self-learning abilities, strengthen teaching outcomes, accurately diagnose teaching issues, and foster the continuous improvement of teaching methodologies.

The Ministry of Education has explicitly outlined the implementation of the “MOOC Westward Journey Plan” and emphasized the importance of enhancing digital intelligence to better achieve personalized teaching. Guided by the Party’s strategy for governing Xinjiang in the new era and the goal of cultivating regional talents in the western region, the core course teaching team for the Chemistry major at Xinjiang Normal University relies on the “Smart Tree” education platform. This platform, in conjunction with the East-West University Curriculum Sharing Alliance, leverages high-quality resources to promote course sharing through the use of knowledge graphs and artificial intelligence. Partnering with prestigious universities both within and outside Xinjiang, the team conducts synchronous classrooms and collaborates on the construction of knowledge graphs. This model enables multi-school resource sharing, multi-directional synchronous classroom sharing, and digital intelligence empowerment. We have developed a cooperative model for multi-school, multi-directional synchronous classrooms and a new teaching approach based on artificial intelligence-driven knowledge graphs.

With the advancement of educational informatization, traditional chemistry teaching models are increasingly challenged by issues such as knowledge fragmentation, ambiguous learning pathways, and insufficient focus on student-centered learning. Addressing these challenges necessitates the application of knowledge graph technology to facilitate systematic knowledge integration, adaptive learning, and personalized teaching support. This study focuses on two key aspects: the development of a course knowledge graph for university chemistry and the implementation of curriculum reform. Leveraging the capabilities of the knowledge graph in data analysis, intelligent recommendation, and pathway planning, a five-stage progressive teaching model—“predict, test, learn, practice, guide”—has been established. This model enables personalized teaching through “pre-class profiling and precise strategy formulation, in-class focus on diverse teaching methods to enhance competencies, and post-class personalized practice evaluation and intelligent tutoring”. The findings offer novel insights and practical approaches for university chemistry curriculum reform, providing theoretical guidance for adaptive learning models for students and personalized teaching strategies for educators, thereby driving the evolution of courses towards greater intelligence and digitalization.

The extraction of capsanthin from red chili peppers constitutes a comprehensive experiment in natural product chemistry education. This experimental protocol encompasses the extraction, separation, and purification of capsanthin, involving multiple experimental variables, utilization of sophisticated instrumentation, and constraints on instructional time. The strategic incorporation of digital teaching methodologies facilitates both process optimization and enhanced comprehension of theoretical principles and experimental procedures. The digital framework comprises two principal components: (1) offline “segmented” data acquisition coupled with mathematical modeling for numerical simulation of extraction parameters, including temperature, solvent composition, developing/eluting agent ratios, and silica gel column height, enabling students to analyze predicted outcomes and refine experimental conditions; (2) virtual simulation technology for dynamic visualization of experimental results, assisting students in apparatus assembly (e.g., reflux systems) and instrument operation (e.g., liquid chromatography). The assessment system automatically evaluates performance based on experimental outcomes and operational repetitions. This digital approach actively engages students, ensures preservation of bioactive components, fosters awareness of plant utilization and conservation, and promotes environmental stewardship.