This paper explores the application of artificial intelligence (AI) in the field of chemistry and the revolutionary changes it brings. By leveraging AI's powerful data analysis and pattern recognition capabilities, it is transforming traditional chemical research methodologies, achieving breakthroughs in areas ranging from molecular synthesis to drug discovery. Specific case studies illustrate AI’s role in accelerating the discovery of new materials, enabling autonomous chemical laboratory operations, and advancing personalized chemistry education. Additionally, AI’s applications in environmental and green chemistry demonstrate its potential in pollutant behavior analysis and the development of new energy technologies. The paper emphasizes the importance of interdisciplinary collaboration, data sharing, and the introduction of AI courses to fully harness AI's potential in chemistry, thereby advancing scientific research.

Amidst rapid technological advancements, artificial intelligence (AI) is transforming educational paradigms at an unprecedented pace, significantly enriching teaching resources and methodologies in higher education. Within the framework of China’s “Emerging Engineering Education” initiative, this study examines the inorganic chemistry course as a representative case. Addressing limitations inherent in traditional teaching approaches, the course systematically incorporates information technologies to establish an AI-empowered blended learning model that combines online and offline instruction. This innovative approach fosters a comprehensive smart classroom ecosystem designed to enhance students’ learning motivation and self-directed engagement. Furthermore, the curriculum effectively integrates AI-empowered ideological and political education components, achieving synergistic alignment among value cultivation, knowledge acquisition, and competency development.

This study investigates innovative approaches to AI-enhanced experimental teaching within the context of marine-themed integrated physical chemistry experiments. The research demonstrates the effective utilization of knowledge graphs for systematic learning and interdisciplinary knowledge integration. By incorporating DeepSeek and Python-based open-source models, the study significantly enhances intelligent data processing capabilities, thereby promoting the advancement of smart experimental teaching methodologies.

In the context of educational digital transformation, this study addresses the challenges of fragmented knowledge, low resource integration, and insufficient personalized support in traditional inorganic chemistry experiment teaching. A “Knowledge Graph + AI” integrated model is proposed, which constructs a “concept-operation-resource” triple network to achieve structured mapping of essential elements such as experimental principles and operational standards. Through an intelligent tutoring system and dynamic reasoning algorithms, the model supports personalized learning path planning and formative assessment. Teaching practice demonstrates that this approach significantly enhances students’ knowledge integration efficiency and innovation capabilities, providing a novel pathway for the digital transformation of experimental education.

As a vital theoretical branch of chemistry, physical chemistry is characterized by its rigorous logical framework and practical applications, making it an essential medium for implementing ideological and political education. This article proposes the integration of digital technology and artificial intelligence to create a resource library for ideological and political education within physical chemistry courses. This initiative aims to foster the convergence of ideological and political education with cutting-edge technological advancements. Additionally, it advocates for the innovative application of digital technology to develop contextual frameworks for ideological and political discussions in these courses, facilitating a multi-channel and multidimensional integration of these elements. Furthermore, digital technology will be utilized to establish a feedback mechanism for ideological and political education within the curriculum.

This study addresses prevalent issues in the teaching of general education courses, particularly focusing on the course “Chemistry and Human Civilization”. It emphasizes the enhancement of the course’s “intelligence” through the construction of a knowledge graph, the establishment of AI teaching assistants, the innovation of the SCIENCE teaching model, the elevation of learning task challenges, the reform of evaluation methods, and the strengthening of learning process assessments. The knowledge graph and AI teaching assistant were employed to facilitate personalized learning navigation and precise assessment for students across various disciplines. Initial teaching practice results indicate that AI-enabled teaching reform effectively addresses critical issues in general education, alters both teachers’ and students’ perceptions of such courses, increases student engagement, and enhances scientific literacy and higher-order learning capabilities.

This study explores the innovative application of generative artificial intelligence (AI) in the simulation of acid-base titration images. Through hands-on experimentation, we employed generative AI techniques to develop code for dynamic acid-base titration images, establishing a “code generation and optimization for experimental images” paradigm. This approach is tightly aligned with the practical needs of educational settings, aiming to offer cutting-edge technological support for chemistry educators in experimental teaching. It also aims to create intelligent, interactive learning environments to enhance students’ understanding of chemistry.

This paper delineates the distinctive features of physical chemistry courses and identifies the limitations inherent in traditional teaching methodologies. It examines the synergistic advantages of integrating artificial intelligence (AI) with the Rain Classroom platform and details the implementation process of AI-enabled Rain Classroom in blended teaching of physical chemistry, encompassing pre-class, in-class, and post-class instructional design. Through empirical teaching practices and student feedback, the study investigates the impact of the blended teaching model on students’ learning outcomes, academic interest, autonomous learning capabilities, and innovative practical skills. The findings demonstrate significant improvements in students’ academic performance with no failing grades, a substantial increase in learning interest accompanied by approximately threefold engagement enhancement, and notable advancements in both learning and practical abilities. This research aims to provide innovative approaches and methodologies for the reform of physical chemistry education, facilitating the profound integration of educational technology with disciplinary instruction.

In the context of the rapid advancement of artificial intelligence, smart teaching has emerged as an innovative approach to higher education curriculum reform. This study establishes a student-centered smart teaching framework through the strategic design of curriculum levels, refinement of competency development pathways, and deep integration of disciplinary knowledge with intelligent technologies. Using the organic chemistry course as a case study, the proposed model incorporates advanced technologies such as artificial intelligence and big data analytics to optimize the allocation and utilization of teaching resources, thereby achieving personalized and precise teaching processes. The implementation of virtual laboratories, intelligent Q&A systems, and online learning platforms enables students to engage comprehensively in pre-class, in-class, and post-class learning activities, significantly enhancing their autonomous learning capabilities and practical operational skills. Empirical teaching practices demonstrate that this model effectively improves students’ classroom engagement, comprehensive competencies, and learning outcomes.

In the context of advancing digital intelligence technologies, this study preliminarily explores reform strategies for “digital intelligence” in the blended teaching of inorganic chemistry, aiming to address existing challenges. A knowledge graph was constructed, and an AI teaching assistant was implemented, both of which were integrated into the blended curriculum to develop an AI-enhanced teaching model for inorganic chemistry. Using “precipitation dissolution equilibrium” as a case study, we demonstrate how the knowledge graph and AI teaching assistant facilitate intelligent guidance and accompaniment in students’ learning. Additionally, we highlight the role of educators in accurately assessing student learning conditions and optimizing teaching strategies. Results from post-class assessments and course evaluations indicate that this AI-enhanced approach significantly improves students’ motivation to learn.

In recent years, the application of artificial intelligence (AI) in education has garnered increasing attention. This paper analyzes the distinctive characteristics of university chemistry experiments, including their comprehensiveness and systematization, exploratory and innovative nature, focus on safety and standardization, as well as their foundational and challenging aspects. It reviews the current state of AI integration in chemistry laboratory teaching, highlighting developments in intelligent teaching assistant systems, smart learning platforms, and virtual reality applications. However, AI is still rarely employed in the core aspect of chemistry laboratory — the teaching of experimental operations. The paper also explores the potential future of AI in university chemistry education, identifying key development needs, such as understanding symbolic systems, capturing action details, enhancing comprehensive support capabilities, and ensuring laboratory safety. Finally, it provides an outlook on the evolving role of AI in enhancing university chemistry laboratory teaching.

Against the backdrop of rapid advancements in artificial intelligence (AI) technology worldwide, universities face significant challenges in addressing contemporary issues by integrating traditional scientific research with AI. This integration aims to broaden the perspectives of engineering graduate students, stimulate innovative thinking, enhance the efficiency of innovative outputs, and cultivate versatile talents for national and societal needs. This paper, using electrocatalytic carbon dioxide reduction (CO2RR) within the framework of carbon neutrality as a case study, underscores the importance of merging scientific research with AI to augment research output and accuracy. It highlights how AI computing facilitates the screening and prediction of high-performance catalysts, deepens the understanding of complex reaction mechanisms, optimizes electrolytes, and aids in experimental design. Furthermore, it promotes interdisciplinary collaboration and serves as a reference for engineering graduate students embarking on experimental research in universities.

With the rapid advancement of artificial intelligence (AI) technology, the limitations inherent in traditional educational paradigms are increasingly being recognized and addressed. This paper examines the current state of chemical education in higher institutions, offering a comprehensive analysis of the deficiencies and underlying causes in conventional university chemistry instruction. By leveraging the strengths of AI technology, the study proposes strategic measures from the perspective of AI-enhanced chemistry education. These initiatives aim to drive meaningful reform in university chemistry teaching methodologies and elevate the overall quality of chemical education in academic institutions.

Generative AI, with its remarkable capabilities in natural language processing and knowledge generation, is profoundly influencing educational reform. This paper explores the application of generative AI in analytical chemistry education, examining its multiple roles within the teaching process. It provides a detailed exploration of intelligent lesson planning, the design of personalized learning paths for students, and the analysis of exam results, all aimed at enhancing the learning experience and teaching efficiency for both students and instructors. This study offers new insights into the modernization of teaching methods. Additionally, the paper objectively assesses the challenges associated with AI integration in education and suggests strategies to address these challenges, providing valuable guidance for the deeper application of generative AI in analytical chemistry and other educational fields..

This paper investigates innovative approaches to organizing chemistry instruction through the application of artificial intelligence (AI) technology, utilizing specific case studies to demonstrate how AI can transform traditional teaching methods. To address the shortcomings of conventional chemistry education, we propose three primary innovation pathways based on AI technology: adaptive learning systems, intelligent experimental platforms, virtual laboratories, and collaborative learning enhanced by intelligent coordination. Adaptive learning systems leverage personalized data analysis to dynamically tailor learning pathways for students, effectively addressing the limitations of “one-size-fits-all” teaching models. Virtual laboratories and intelligent experimental platforms transcend the constraints of physical experiments by providing students with a safe and flexible environment for conducting experiments. Additionally, intelligent collaboration tools optimize group dynamics in cooperative learning settings, enhancing learning efficiency through real-time feedback. Nevertheless, the implementation of AI technology in chemical education faces challenges, including the uneven distribution of educational resources and inadequate technological proficiency among educators. This paper recommends addressing these challenges by optimizing resource allocation, strengthening teacher training, and integrating virtual and traditional teaching methods to enhance the effectiveness of AI applications in chemistry education. Looking ahead, AI technology is poised to continue driving innovation in chemical education, facilitating the intelligent transformation of instructional methods, and playing a crucial role in promoting educational equity and improving teaching quality.

Driven by advancements in computer science and technology, machine learning has emerged as a powerful tool in chemical research. This paper begins by introducing the basic concepts of machine learning, followed by an exploration of its four key applications in the field of chemistry: predicting synthetic pathways in organic total synthesis; conducting efficient sampling and reconstruction of potential energy surfaces in atomic simulations; revealing reaction pathways and screening catalysts in heterogeneous catalysis design; and processing and interpreting signals in nuclear magnetic resonance spectroscopy. Additionally, the concept of the robotic chemist is introduced, illustrating the potential of integrating machine learning with automation technologies. Finally, the paper discusses the future prospects of machine learning in chemical research, highlighting its potential transformative impacts.

In recent years, the integration of artificial intelligence (AI) technology and chemistry education has given rise to a new track for the development of professional chemistry education in higher education institutions. How to transform chemistry teaching content, innovate teaching methods, and promote curriculum construction has become one of the key focuses. This article introduces the overview of the course construction of “Chemical Informatics and AI Chemistry” at Tianjin University, discusses the necessity of carrying out the project construction of artificial intelligence chemistry course, summarizes the teaching objectives, knowledge areas and teaching content, characteristic innovations and operational effects of the course, and puts forward shortcomings and future improvement plans, hoping to provide reference for teaching peers in related fields.

DNA computing, an innovative technology that utilizes biological molecules to execute computational tasks such as data storage, problem-solving, and logical operations, offers novel pathways to transcend the limitations of conventional computing. As DNA computing continues to evolve, challenges pertaining to complexity, error rates, and computational efficiency have become increasingly pronounced. The advent of artificial intelligence (AI) presents significant opportunities to optimize and enhance DNA computing. Particularly in the realms of data analysis, model optimization, and error correction, the integration of AI technologies has markedly improved the efficiency, accuracy, and stability of DNA computing. This paper provides an in-depth review of AI algorithm classifications and examines how AI empowers DNA computing, with a particular emphasis on optimizing computational workflows, enhancing logic gate functionality, and refining error correction mechanisms. Additionally, we summarize the pivotal applications of AI and DNA computing at the forefront of bioinformatics, including genomics, protein structure prediction, disease diagnosis, and precision medicine. Looking ahead, the ongoing innovation in AI and DNA computing is anticipated to further broaden their application scope, positioning them as a transformative force in the advancement of biosciences.

The emergence of Artificial Intelligence (AI) is significantly changing the course of human social development, and education cannot remain unaffected. This article briefly discusses the potential impact of AI on education, drawing on the author’s recent experience of learning alongside ChatGPT-4, as well as the extreme importance of learning how to learn in the context of AI-powered education. Based on this foundation, suggestions are made for AI-enabled education, particularly in the field of chemistry education.

Organic chemistry laboratory is a fundamental, compulsory course in chemistry and related disciplines, with synthesis experiments at their core. This paper introduces artificial intelligence prediction models into the teaching of synthesis experiments in organic chemistry courses. Specifically, the ASKCOS model is applied to perform both forward synthesis prediction and retrosynthesis prediction. The results indicate that the existing model provides reliable predictions for the synthesis experiments covered in the curriculum, with hit rates for both forward and retrosynthesis predictions reaching as high as 88.5%. The integration of AI prediction models into undergraduate laboratory teaching enhances students’ understanding of organic chemical synthesis, promotes the concept of AI-assisted organic synthesis, fosters interdisciplinary collaboration, improves teaching quality, and encourages divergent thinking, thereby laying a strong foundation for their future research work.

Laboratory safety education constitutes a critical element in graduate training, playing a pivotal role in maintaining a secure academic and research environment. This study investigates an AI-enhanced laboratory safety education system implemented within the Forestry Engineering graduate program at the School of Materials Science, Northeast Forestry University. To address prevalent issues such as the disconnection between theoretical knowledge and practical application, as well as the limitations inherent in conventional teaching methodologies, the proposed system incorporates three integrated modules: theoretical instruction, virtual simulation, and practical training. The integration of AI technology facilitates immersive safety training experiences, enables intelligent assessment mechanisms, and provides personalized feedback, thereby optimizing the learning process. Furthermore, the practical training component enhances emergency response capabilities and elevates laboratory safety competencies. The implementation of this system has demonstrated significant improvements in students’ safety awareness and a reduction in laboratory incidents, offering valuable insights for the optimization and dissemination of laboratory safety education in higher education institutions.

The rapid advancement of Artificial Intelligence (AI) has brought forth significant opportunities for innovation in polymer chemistry education, while simultaneously presenting a range of challenges. This paper provides an in-depth analysis of AI’s innovative applications in various aspects of teaching, including curriculum content, pedagogical approaches, classroom dynamics, and the evolving roles of educators and students. Furthermore, it examines the associated challenges and proposes practical solutions through case studies, focusing on enhancing faculty competencies and optimizing educational resources. The study aims to facilitate the seamless integration of AI into polymer chemistry instruction, thereby comprehensively improving the quality of teaching and learning.

This study addresses the challenges faced in the teaching of chemistry history amid the rapid advancement of information technology. By constructing a knowledge graph based on online course resources and implementing AI teaching assistants, we explore a novel pedagogical approach empowered by artificial intelligence. The integration of knowledge graphs and AI assistants facilitates personalized learning for students and enables teachers to accurately assess learning conditions. Preliminary results from teaching practice suggest that AI-enabled reforms in chemistry history education effectively address existing issues, enhance students’ interest in learning, and improve educational quality. Moreover, the data generated from the knowledge graph and AI teaching assistants serves as a valuable foundation for the continuous enhancement of intelligent curricula.

In the context of developing new quality productive forces, artificial intelligence (AI) has become a powerful tool in chemical research and education. This study presents the reform of the “Learning-Understanding-Application” cultivation model in Materials Chemistry at Northeast Forestry University, integrating AI technologies through the establishment of an intelligent virtual simulation platform and an industry-education collaborative intelligent evaluation system. These innovations effectively address key challenges in traditional education: the theory-practice disconnect (with knowledge application rates below 42%), fragmented knowledge systems (exhibiting only 31.7% cross-course relevance), and delayed innovation commercialization (averaging over 18 months for implementation). Over three years of implementation, the project has demonstrated significant outcomes: the innovation-to-market cycle for student projects shortened to 5.8 months (a 67.8% improvement), while national competition awards (including those from the China International College Students’ Innovation Competition) increased by 147.8%. This research confirms the unique value of AI in resolving fundamental industry-education integration challenges and establishes a replicable “Technology Empowerment - Practice Reinforcement - Industry Feedback” educational ecosystem for materials chemistry education within the emerging engineering education framework.

This study integrates the Bohrium cloud computing platform with the domestic open-source software ABACUS to develop an innovative hybrid teaching model, applied in the “New Energy Materials and Chemistry” course at Tianjin University. The course design effectively combines theory and practice, encompassing topics such as self-consistent calculations, structural optimization, electronic density of states, band structure, and phonon characteristics. The introduction of the cloud computing platform significantly reduces operational complexity, enhances classroom interactivity, and fosters students’ independent learning and innovative thinking. This teaching approach has demonstrably improved instructional quality and offers a novel pathway for cultivating chemistry professionals with a global perspective and innovative capabilities.

To further enhance the application value of artificial intelligence (AI) in university chemistry education and strengthen students’ information literacy and technological proficiency, this study proposes a series of teaching reforms and practical measures. Based on an analysis of the current state of chemistry education, it explores the deep integration of AI, big data, and large models into chemistry teaching, emphasizing their role in improving students’ data processing, intelligent decision-making, and innovative thinking skills. Focusing on cutting-edge technologies such as adaptive learning platforms and AI-powered data analysis, this study proposes specific strategies, including the development of intelligent teaching platforms, the integration of advanced data analysis tools, the optimization of research processes, the encouragement of student participation in interdisciplinary competitions, and the promotion of cross-disciplinary integration. Practical applications have demonstrated that the deep integration of intelligent technology with teaching reforms significantly enhances students’ self-directed learning, scientific research innovation, and practical application abilities, contributing to the establishment of a modern chemistry education system that aligns with future technological advancements. This study provides valuable insights for the intelligent transformation of chemistry education in universities and outlines potential future research directions.

Guided by the principle of “integrating science and education, collaboratively cultivating talent”, this paper explores the integration of Artificial Intelligence (AI) with biochemistry teaching and research. It examines the application of AI technology in the reform of biochemistry education, specifically through the development of a case study in biomedical engineering that predicts the functional effects of gene mutations using AI. The paper discusses the design and implementation of this AI-driven teaching case, focusing on the case’s background, curriculum design, teaching strategies, and evaluation of its impact. This approach aims to cultivate interdisciplinary thinking in students, enhancing their ability to integrate knowledge across fields.

This study employs the DeepSeek large model to conduct simulated learning and explores its effective elements in inorganic chemistry education. In the era of digital-intelligent empowerment, it is imperative to reconsider innovative approaches for chemistry curriculum teaching and learning, thereby addressing the evolving requirements and challenges posed by social development in the field of inorganic chemistry education.

Undergraduate experimental design typically requires extensive trial-and-error experimentation to identify optimal reaction conditions, a process that demands considerable time and resources. To simplify this complex experimental design process and to enhance students’ interest in advanced technologies and interdisciplinary fields, an intelligent reaction optimization model framework was introduced. This model is designed to predict the optimal combination of reaction conditions for achieving the highest yield, and this framework has been integrated into the undergraduate organic chemistry curriculum, specifically for the synthesis of acetylsalicylic acid. Herein, 1054 reaction data points were collected conforming to this reaction mechanism as training data for the model, mainly including product, reactant, catalyst, solvent, the main reaction reagents, reaction temperature and yield. Initially, a yield prediction model was pre-trained based on a chemical multi-modal transformer, which served as the objective function for subsequent reaction optimization. Then the Bayesian optimization algorithm was utilized to ascertain the optimal combination of reaction conditions, with the aim of minimizing the negative value of the yield (the maximizing yield). Finally, the model-predicted yields were experimentally validated against the actual yields, and in 100 model tests, the predicted yields consistently fell within the range of the actual yields, demonstrating the model’s excellent performance. The model also identified a reaction reagent combination that yielded up to 90.1%, providing a variety of experimental options for this undergraduate experiment.

The digitalization of education represents a significant breakthrough for China in exploring new pathways for educational development, and the integration of artificial intelligence into education is an inevitable trend. This article analyzes the design principles for incorporating artificial intelligence into physical chemistry laboratory courses, based on the exploration of intelligent teaching models. It presents a concrete vision for an artificial intelligence-enabled physical chemistry laboratory platform and evaluates the initial practical outcomes at our institution. This research provides a novel perspective for advancing the transformation of traditional chemistry laboratory teaching methods towards a more intelligent approach.

This paper explores the integration of artificial intelligence technology into the construction of chemistry courses, highlighting its practical applications in chemical research. It outlines the teaching objectives, curriculum design, and syllabus for AI-enhanced chemistry courses, along with implementation strategies and case studies. Additionally, the paper introduces the development of an AI+chemical engineering virtual simulation platform. The establishment of these courses aims to significantly enhance chemical education and research in China.

The progression from data-driven methodologies to the establishment of chemical symbols represents a fundamental process in the advancement of chemical education. The integration of logical reasoning with model construction serves as a pivotal approach for comprehending abstract chemical symbols, constituting a cornerstone of chemistry bridging education. This study employs the Transformer model to simulate and compute Avogadro’s constant, while utilizing Scikit-learn’s integrated models to deduce the most probable distribution. Through the implementation of the triple representation teaching strategy, the research investigates the application of AI models in chemistry bridging education, with particular emphasis on elucidating the conceptual underpinnings of Avogadro’s constant as a proportionality factor. This exploration extends to Boltzmann’s constant, the Boltzmann formula (interpretation of entropy), and the definition and derivation of temperature formulas. This pedagogical approach facilitates students’ mastery and comprehension of chemical symbols, enabling them to develop a profound understanding of chemistry as a discipline fundamentally concerned with the study of aggregates.

As artificial intelligence (AI) technology continues to develop rapidly, universities face the challenge of integrating traditional subject education with AI to broaden students’ perspectives, stimulate innovative thinking, and cultivate high-quality, versatile talent for society and the nation. This paper takes the general education course “Materials ‘Meeting’ Artificial Intelligence” as an example to discuss the importance of incorporating AI teaching into general education. It proposes a curriculum design that includes the integration of ideological and political education, the exploration of content linking materials science with AI, and the introduction of cutting-edge scientific cases in materials. The study also suggests improvements in classroom teaching methods and a more diverse evaluation of course effectiveness, offering a reference for reforming traditional general education courses in universities.

The groundbreaking advancements in Generative Artificial Intelligence (GAI) technology have introduced novel perspectives for reforming traditional educational curricula. This study focuses on physical chemistry courses, presenting and implementing an AI-enhanced teaching reform strategy. By harnessing DeepSeek’s capabilities in natural language understanding and reasoning, we have developed an integrated approach incorporating Xmind, Mathematica, and JiMing AI. This integration facilitates the creation of a systematic, hierarchical mind map that enables dynamic knowledge connections and intelligent expansion. Furthermore, complex mathematical models are transformed into dynamic, interactive representations, effectively overcoming the challenges posed by abstract mathematical concepts. Additionally, key concepts are presented through high-precision diagrams and concise video demonstrations, achieving a “visual, interactive, and mobile” delivery of educational content. This approach provides a practical framework for the digital transformation of chemistry education.

Simulated annealing algorithm is an artificial intelligence combinatorial optimization algorithm. Building upon the classic simulated annealing algorithm, we propose an enhanced version for predicting the molecular formulas of organic compounds. The algorithm begins by using a genetic algorithm to calculate the fitness values of individuals in the population, selecting the optimal individual as the initial solution for the simulated annealing process. Based on this initial solution, new solutions are generated through random perturbation, and their fitness values are calculated. If the change in fitness is less than or equal to zero, the new solution is accepted. Otherwise, the Metropolis criterion is applied to determine whether the new solution should be accepted. As the annealing temperature gradually decreases, the algorithm’s termination condition is used to determine if the global optimal solution has been found. Experimental results show that this improved algorithm increases the success rate of finding the global optimal solution. When applied to predict the molecular formulas of organic compounds, it demonstrates significantly better convergence of the fitness function compared to the classical simulated annealing algorithm.

In the context of “new quality productive forces”, the accelerating intelligent transformation of the chemical industry has created an urgent demand for high-quality chemical engineering professionals with intelligent technology literacy. The effective cultivation of such talents has become a critical focus in higher education reform. This paper, centered on the concept of “new quality productive forces”, thoroughly explores the pathways and strategies for developing intelligent chemical engineering talents, aiming to provide insights and recommendations for the educational reform in chemical engineering.