Journal of Education and Educational Research
Journal of Education and Educational Research (JEER) is a peer-reviewed journal published by Darcy & Roy Press. The mission of the journal is to provide an academic forum for discussing the issues of education across the world. All articles published are rigorously and fast reviewed meeting the Journal Quality standards.
Research on Backward Design and Multi-dimensional Evaluation Teaching of Discrete Mathematics Based on OBE
Based on the OBE philosophy for engineering education accreditation, a three-in-one teaching model of Discrete Mathematics featuring "backward design, module restructuring and diversified evaluation" is constructed. With mapping analysis between curriculum objectives and graduation requirements, the corresponding relationship between knowledge units and competency cultivation is established. A case-driven modular teaching approach is adopted, and formative plus summative assessments are integrated to build a closed-loop feedback system. This study aims to improve the teaching quality of Discrete Mathematics and realize preset teaching outcomes via the implementation of the OBE philosophy.
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Developing and Implementing a Teaching Model for Personalized Learning in Governmental Accounting Supported by an AIGC Knowledge Base
In response to the teaching challenges posed by the abstract standards, complex practical rules, and substantial individual differences among students in the Governmental Accounting course, this study constructs and implements a five-stage personalized teaching model supported by the AIGC knowledge base, centered on the knowledge system of the course and students' differentiated learning needs. The model comprises pre-class preview diagnosis, in-class teaching by learning, in-class practice linking theory to practice, after-class personalized consolidation and extension, and outcome evaluation based on multi-source data, forming a dynamic closed loop in which the AIGC knowledge base functions as a hub throughout all stages to provide differentiated support for each student. Using the ima knowledge base platform, a course-specific Governmental Accounting knowledge base was constructed, and an eight-week teaching intervention was conducted with a single-group pretest–posttest quasi-experimental design (n = 88). The results showed that the posttest mean scores for learning self-efficacy (M = 4.08, SD = 0.52) and task value (M = 4.18, SD = 0.48) were significantly higher than the corresponding pretest means, with a large effect size for self-efficacy (d = 1.19) and a large effect size for task value (d = 1.03). Knowledge-base usage frequency was moderately and positively correlated with learning self-efficacy (r = 0.44, p < 0.001) and task value (r = 0.30, p < 0.01). Learning self-efficacy showed a gradient increase from low- to high-use groups, and objective grades were also higher in the intervention cohort than in the historical cohort. The findings suggest that the AIGC knowledge base can provide a feasible practical pathway for personalized learning in professional courses such as Governmental Accounting, and that the five-stage teaching model can effectively translate the technological affordances of AIGC into teachers' instructional decisions and students' learning processes.
Research on the Teaching Reform Path of Python Courses for Mechanical Majors
Driven by the rapid advancement of intelligent manufacturing technologies, Python has gained broader application scenarios within mechanical engineering disciplines. Nevertheless, existing Python courses tailored for mechanical majors still face multiple teaching drawbacks, including ambiguous curriculum positioning, disconnection between teaching materials and industry specialties, as well as monotonous teaching modes. Combined with project-based learning and integrated teaching concepts, this paper puts forward a targeted reform scheme for Python courses in mechanical majors. The reform covers four key dimensions: clarifying curriculum orientation, reorganizing teaching content, innovating teaching approaches and optimizing assessment systems. Practical teaching cases are also provided to verify the actual effect of the proposed reforms. After implementing the above adjustments, the updated teaching framework can effectively stimulate students’ learning initiative and strengthen their practical programming capabilities. This research delivers solid support for cultivating mechanical engineering talents who can meet the talent demands under the intelligent manufacturing industry background.
Research on Multidimensional Strategies to Enhance University Students' Classroom Engagement and Participation
Classroom teaching is the main place for talent cultivation in higher education. Its quality has a direct effect on the quality of talent development. At present, many college students show low enthusiasm in class. Their participation is also not high enough. This has become an important problem in improving teaching quality. Based on years of teaching practice, this paper focuses on this problem and discusses possible solutions from different aspects. The study shows that students’ classroom performance is affected by many factors. These factors include intrinsic motivation, teaching methods, and teacher-student relationships. This paper builds a systematic solution framework. It includes pre-class preparation, in-class interaction, post-class consolidation, and teacher-student relationship building. Practice shows that this method can effectively improve students’ classroom participation. It can also provide useful reference for teaching reform in higher education.
Research on the Path of Empowering Ideological and Political Education through Generative Artificial Intelligence to Forge a Strong Sense of Community for the Chinese Nation
The pervasive digital transition in education provides the backdrop for this paper’s analysis of generative AI’s contributions to ideological and political courses, specifically in reshaping pedagogy, expanding content supply, and boosting instructional performance via dialogue-based interactions. It further examines three major risk challenges arising from technological application: technological dependence, value deviation, and ethical security. Accordingly, the study proposes three practical pathways: adhering to value leadership to construct a human-machine collaborative model,strengthening content review to establish a gatekeeping mechanism,and improving institutional safeguards to develop a sound normative system.
Design of Curriculum for Development of Legal Practice Competence in Financial Law and Regulation Courses
Finance law and regulation is a required core course of higher vocational finance majors. Whether financial law and regulation instruction design can achieve the purpose of improving students' financial legal literacy and professional practice ability greatly depends on whether it has appropriate instructional design strategies. Taking loan contract legal system as an example, starting from the three aspects of constructivist learning theory, situational cognition theory, and ideological-political curriculum (kecheng sizheng) construction, by means of systematic diagnosis method, four kinds of instructional structural defects existing in today’s financial law and regulation courses have been found. Firstly, there is a lack of coherent connection between teaching content and professional practice. Secondly, teachers dominate most of the teaching activities instead of letting students engage fully. Thirdly, the whole process evaluation pays more attention to results but neglects practitioners’ abilities. Fourthly, the ideological-political curriculum construction is still very weak because it cannot be deeply integrated into every aspect of teaching activities. To solve these problems, this paper tries to build up a tripartite instructional design framework of integrating law knowledge, vocational situation and value shaping together. Theoretical reasoning and experimental evidence indicate that if finance law and regulation courses could put real financial legal cases as organizing threads of teaching contents, use Problem-based Learning (PBL) and Flipped Classroom Model in combination as teaching methods, establish multi-dimensional evaluation system consisting of process evaluation and terminal evaluation parts simultaneously, students’ legal practice ability will surely improve and their own contractual spirit will also grow up.
Analysis of the Interpersonal Meaning in English Environmental Slogans
With the increasing seriousness of environmental problems, the improvement of environmental protection awareness has become crucial. As a communication tool, environmental protection slogans play an important role in awakening public awareness of environmental protection and guiding behavior change. This article uses the theory of interpersonal function in systematic functional linguistics to explore how English environmental protection slogans can achieve interpersonal meaning. The results show that English environmental protection slogans mainly convey information and stimulate action through declarative tone and imperative mood. The frequency of use of modal expression is relatively low and concentrated in medium and low values, while the personal system prefers to use the first person to enhance the inclusiveness and directness of information. These findings reveal the impact of the choice of language strategy of English environmental protection slogans on the effect of conveying environmental protection information, which is inspiring for the design of effective environmental protection communication strategies. Although there are some limitations in research, such as small sample size and single corpus source, it is still of certain significance for the study of English environmental protection slogans. Future research can expand the sample scale, enrich the corpus source, empirically examine the effect of slogans, and improve the breadth and depth of research.
AIGC-Empowered Talent Cultivation Model for the Data Science and Big Data Technology Major under Engineering Education
This study examines two main issues in the engineering education of the Data Science and Big Data Technology program: the gap between what students learn and actual industry demands, and the limited application of AIGC technology in daily teaching. To address these problems, we developed a new talent training model based on a four dimensional collaborative framework and three supporting systems; following the approach of not adding separate new courses while integrating AIGC into all teaching links, we added AIGC related content to the existing knowledge-ability-quality training system, revised course content, improved practical training tasks, and promoted cooperation among government, universities and enterprises to ensure AIGC is deeply applied throughout classroom instruction, lab sessions and internship training. We piloted this model with three groups of students at Suqian University, and the results show that students have made clear progress in their ability to complete AIGC assisted tasks and their overall engineering skills, enterprises have given higher recognition to student project outputs, more awards have been obtained in subject competitions and innovation projects, and government-university-enterprise cooperation has been further strengthened. This work provides a practical reference for other emerging engineering majors to keep up with technological development without making major adjustments to their current curriculum systems.
Analysis of Inter-Regional Tax Cooperation Rule Alignment in the Greater Bay Area
The central government has incorporated the development of the Greater Bay Area into the broader national development strategy. Improving the inter-regional tax cooperation system among the mainland, Hong Kong, and Macao is a crucial component of advancing judicial integration in the Greater Bay Area. This paper analyzes existing cases by interpreting key documents of central and local government, integrating the latest academic research, and examining the current tax cooperation arrangements with taxpayer information sharing mechanisms in the Greater Bay Area. It identifies challenges within the existing system: a final tax dispute resolution mechanism has yet to be established, a coordinated mechanism among the three regions' tax authorities is yet to be formed, and differences persist in tax procedures across the three regions. Finally, drawing on comparative law perspectives, this paper reviews existing inter-regional tax cooperation models in other countries. Considering the Greater Bay Area's unique circumstances, it proposes selectively adapting international experiences to enhance the tax cooperation framework. Key recommendations include establishing a unified tax filing system, promoting coordination among tax authorities in the three regions, and appropriately adopting the Mutual Agreement Procedure (MAP) arbitration process. By examining the challenges in inter-regional tax rule coordination within the Greater Bay Area, this paper aims to provide new perspectives and solutions for advancing judicial integration in the region.
Research and Practice on Artificial Intelligence-Assisted Teaching Models in Interior Design Education That Integrate Hand-drawn Design Techniques
As digital design trends continue to advance, hand-drawing instruction in interior design faces numerous challenges, including insufficient creativity stimulation, suboptimal skill development, and inadequate personalized guidance—factors that significantly diminish the effectiveness of traditional teaching methods. Artificial intelligence assistance, leveraging its strengths in rapid rendering, style transformation, and intelligent feedback, brings new momentum to hand-drawing education and holds promise for reshaping students' learning patterns. This paper presents an in-depth study of a three-stage integrated teaching model: foundational training, stylistic integration, and practical project application. By analyzing the diversity of creative expression in students’ hand-drawn works and incorporating student feedback, the research demonstrates that this approach significantly enhances students’ drawing proficiency and creative output while optimizing resource allocation. The seamless integration of AI and hand-drawing effectively overcomes long-standing limitations of conventional teaching, paving innovative pathways for cultivating interior design talent. Nevertheless, further work is needed to explore the adaptability of AI tools and to establish more comprehensive mechanisms for collaboration between teachers and students.
A Case-Based Teaching Module for Red Tourism Culture Translation under the CATTI Competence Framework
CATTI has set up a competence framework which serves as an important reference to translator education. Based on the competence framework of CATTI and red tourism cultural resources of Heilongjiang Province, the paper adopts a case-based teaching method to design a teaching module of red tourism culture translation characterized by the “competence-oriented, case-driven, and practice-enhanced” framework. In line with the OBE principle, this module sets up learning objectives according to the CATTI translator competence requirements, and implements teaching via case-based teaching method and e-portfolio evaluation. These translation cases can provide authentic context in translation training, which is favorable for the combination of CATTI competence requirements and translation teaching. It will be beneficial to improve students’ professional competence and provide implications for curriculum reform.
Construction of an Evaluation System for Chinese Stories and Its Pedagogical Applicability: A Study Based on the Delphi Method and Analytic Hierarchy Process
In view of the lack of scientific, systematic and operable text quality evaluation standards in the process of international communication of Chinese stories, this study integrates international communication, cognitive linguistics, narratology and creative writing theory to construct a Chinese story evaluation system. Firstly, the initial index framework is established based on literature analysis. Then, the Delphi method is used to screen and optimize the indicators, and the analytic hierarchy process is used to determine the weight of each level of indicators. Finally, an evaluation system including 4 first-level indicators, 13 second-level indicators and 37 third-level indicators is formed. In order to test the teaching application value of the system, this study carried out a one-semester English creative writing teaching experiment among 154 college students in a university in Guangdong Province, and used the system to evaluate and analyze students’ creative works. The results show that the subjects perform better in the two dimensions of theme value and story creativity, while they are relatively weak in the dimensions of discourse construction and narrative strategy. Both English proficiency and Chinese proficiency can significantly positively predict the quality of Chinese story creation. The system can effectively identify the advantages and disadvantages of learners in theme construction, discourse organization, creative design and narrative expression, and provide theoretical basis and practical reference for the evaluation of Chinese story quality and the cultivation of cross-cultural narrative talents.
Digital Platforms as Creative Intermediaries: Algorithmic Mediation, Cultural Visibility, and the Case of Li Ziqi
Social media is increasingly becoming a necessary infrastructure in the digital age, organising cultural production and visibility. Within this context, this paper examines how digital platforms operate as intermediaries that reshape the relationships between creators, audiences, and cultural value. Drawing on theories of cultural and creative intermediaries, the study explores how platform-based systems transform processes of cultural mediation. The analysis integrates approaches to self-presentation and frameworks concerning algorithmic visibility, focusing on how the affordances and constraints of platform algorithms shape both opportunities and limitations for cultural expression. Drawing on the case of the Chinese video creator Li Ziqi, the paper examines how cultural identities are constructed within the recommendation algorithms of major video-sharing platforms. The findings show that Li Ziqi’s visual style and narrative strategies align with platform expectations for immersive, aesthetically engaging content while simultaneously offering an alternative to accelerated algorithmic temporalities by highlighting authenticity, craftsmanship, and a slower life rhythm. This dual negotiation illustrates the paradoxical nature of platforms as both facilitators and regulators of creative visibility, revealing how cultural agency can still be exercised within algorithmically governed environments. Overall, the study contributes to broader sociological understandings of digital cultural mediation and offers new insights into how creative labour navigates visibility in platformised creative economies.
Research on the "Human-Machine Collaboration" Mode in AI-Aided Design and Its Influence on Designers' Creativity
With the comprehensive popularization of generative artificial intelligence represented by Midjourney and Stable Diffusion in the design industry, AI has become an indispensable auxiliary tool for designers in conceptual sketching, scheme iteration, material rendering and visual output. However, the dual contradictions of AI stimulating creative inspiration and triggering design homogenization have become prominent pain points in the current design industry. Existing research mostly focuses on the final design output of AI-assisted creation, while ignoring the dynamic changes of designers’ whole creative process under human-machine collaborative working mode, lacking systematic research on the internal mechanism of creativity gain and loss. Taking graphic design, environmental design and product design practitioners as research objects, this paper takes generative AI tools as the core auxiliary carrier, defines the connotation and classification of "human-machine collaboration modes" in design work, and constructs a whole-process analysis framework of "pre-creation inspiration—mid-process scheme iteration—post-production optimization evaluation". This research reveals the positive driving and negative restriction mechanism of AI on designers’ creative thinking, solves the problem of creative homogenization caused by over-reliance on AI, provides theoretical reference for standardized human-machine collaborative design workflow, and forms a replicable practical paradigm for designers to give full play to independent creative ability under AI auxiliary environment.
Cultivation Pathways of Narrative Spatial Thinking in Short Video Set Design: A Design Thinking Framework-Based Approach
The rise of the micro-drama industry has imposed new competency demands on set design professionals, the core of which lies in a specific design cognition—"narrative spatial thinking," the ability to translate script texts into spatial solutions. At present, interior design pedagogy still predominantly focuses on static functional space training, and the systematic cultivation of this ability remains insufficient. Drawing on the five-stage model of design thinking, this study deconstructs narrative spatial thinking into three teachable sub-competencies—text translation, emotional spatialization, and sequence organization—and develops three corresponding pedagogical tools. Through an 8-week quasi-experiment, the experimental group significantly outperformed the control group in three dimensions—narrative clarity, emotional expressiveness, and creative novelty—and exhibited more systematic and reflective design thinking characteristics. This path provides an operable and scalable practical framework for teaching narrative spatial design.
Research on Mental Health Portrait and Dynamic Early Warning System of University Students with Digital Intelligence Empowerment
This study focuses on the cross-integration of digital intelligence technology and college students' mental health work, and puts forward a set of mental health portrait and dynamic early warning system for college students empowered by digital intelligence. At the level of portrait construction, we study the integration of multi-source heterogeneous data such as academic, behavioral, social, psychological evaluation and active reporting by mobile terminals, adopt the federal learning framework to realize data fusion under privacy protection, construct a dynamic psychological portrait from four dimensions: basic risk, behavioral dynamics, stress event detection and protection factors, and realize student group classification through K-means clustering. At the level of early warning system, a four-level dynamic early warning index system of red, orange, yellow and blue is designed, which integrates rule engine, isolated forest anomaly detection and LSTM-Attention time series prediction model to upgrade from static threshold to dynamic, multi-level and interpretable early warning. At the same time, a closed-loop response process is constructed to ensure timely follow-up after early warning. Based on the experimental data of 8,420 students in a university in one academic year, it shows that the coverage rate of the system is 21.2%, and the overall effective rate of early warning is 53.6%, among which the effective rate of red warning is 100%, which significantly improves the identification and intervention efficiency of psychological crisis. The research provides a theoretical framework and practical path for the digital transformation of mental health services in universities empowered by digital technology.
Dilemmas and Countermeasures of Professional Development Among Young Teachers in Private Colleges and Universities
As China’s higher education system transitions from mass-scale enrollment to universal popularization, private higher education institutions have evolved into an indispensable and vital component of the national educational framework. Young teachers constitute the core workforce of private college teaching teams, and their sustainable professional growth directly determines institutional talent cultivation quality as well as the long-term operational vitality of private universities. In current development contexts, young teachers working in private higher education commonly face multiple developmental obstacles, including insufficient occupational welfare guarantees, limited improvement of teaching and research capabilities, unclear career advancement routes, and weak professional belonging and identity. These restrictive factors seriously hinder individual career growth and impede the overall high-quality development of private higher education. Based on the actual operational characteristics and educational management status of private colleges, this paper systematically sorts out the major practical difficulties encountered by young teachers in their professional development and explores the underlying root causes. From the multi-dimensional perspectives of government policy support, institutional internal governance, and individual self-improvement, the study proposes targeted optimization strategies and improvement solutions. The research results can provide valuable theoretical references and feasible practical paths for strengthening faculty team construction and promoting sustainable professional growth of young teachers in private higher education institutions.
Generative AI in Cinematic Production: Practical Applications, Technical Bottlenecks and Industry Evolution Paths
The global film industry is experiencing a fundamental transformative revolution driven by artificial intelligence technologies. Although image generation rooted in deep learning first emerged with Generative Adversarial Networks (GANs) in 2014, it was the large-scale application of diffusion models around 2020 that triggered a qualitative breakthrough in photorealistic visual content creation. From 2022 to 2025, commercial platforms and open-source weight frameworks represented by Midjourney, DALL-E 2, Stable Diffusion, Sora and Runway Gen-3 have continuously verified that algorithm-generated visuals can achieve parity with traditional visual effects in terms of fidelity, and even outperform manual production in certain standardized workflows. For film practitioners, this rapid technological iteration has reshaped the entire production chain. Traditional filmmaking depends on highly fragmented and labor-intensive processes, covering storyboard creation and location reconnaissance in pre-production, lighting design and camera operation in principal photography, as well as fine-grained color grading and VFX compositing in post-production. As generative AI penetrates each independent link, it brings significant advantages in shortening production cycles and cutting costs. Meanwhile, it has also sparked fierce industry discussions on artistic authorship, skilled labor substitution and the essential nature of visual storytelling. At present, a large number of computer science studies focus on the technical breakthroughs of single video generation algorithms, but there is an obvious research gap in the practical evaluation of these tools from the perspective of professional film production. Existing review articles mostly analyze model architectures in isolation or discuss multimedia applications in a broad sense, ignoring the unique workflow requirements of cinematography. To fill this gap, this paper constructs a functional classification system of generative AI based on the full life cycle of film production, prioritizing application scenarios over algorithmic structures. By evaluating mainstream platforms against professional standards such as output controllability and hardware compatibility, this paper identifies the core technical obstacles in current applications and proposes a practical human-AI collaborative creation framework that preserves directorial creative intention.
Research on Online-offline Blended Teaching Mode of College English in Smart Education Context
With the comprehensive advancement of digital teaching reform in higher education, traditional single offline college English teaching fails to satisfy diversified and individualized learning needs of contemporary undergraduates. Supported by intelligent teaching platforms and informationalized education technology, online-offline blended learning becomes a vital reform direction for university public English courses. As a compulsory foundational curriculum, college English focuses on improving students’ practical language competence and cross-cultural literacy, which calls for breakthroughs against fixed class schedules and rigid traditional teaching arrangements. Taking non-English major undergraduates from comprehensive universities as research objects, this paper constructs a three-phase closed-loop blended teaching framework including pre-class online preview, in-class offline interactive lessons and post-class online consolidation. Relying on a semester-long contrast teaching experiment covering 286 participants and questionnaire survey, the research compares learning initiative, self-directed learning competence, classroom engagement and academic performance between traditional lecturing and optimized blended teaching. Empirical data proves the proposed teaching framework remedies the obvious defects of cramming teaching, stimulates students’ learning enthusiasm and promotes balanced progress in listening, speaking, reading and writing. Teachers adopt learning big data from online platforms to carry out targeted tutoring and personalized instruction, upgrading the overall quality of college English teaching effectively. This study provides practical empirical evidence and optimization suggestions for college English curriculum innovation under smart education background.
Implementation Paths and Case Analysis of Integrating STEM Education into Interdisciplinary Classrooms in Primary and Secondary Schools
STEM education, as a core interdisciplinary educational paradigm focusing on Science, Technology, Engineering, and Mathematics, aims to cultivate students’ innovative thinking, practical problem-solving abilities, and interdisciplinary comprehensive literacy, which is highly compatible with the competency-oriented reform goals of primary and secondary education worldwide. To address the prominent problems of disciplinary fragmentation, isolated classroom teaching, and insufficient practical inquiry in traditional primary and secondary curricula, this study systematically explores the feasible implementation paths of STEM education integrated into interdisciplinary classrooms. Based on mixed research methods of literature analysis, questionnaire survey, classroom observation, and case tracking, this paper constructs a systematic implementation framework covering curriculum reconstruction, teaching design, teacher training, and evaluation optimization. Meanwhile, this study adopts credible empirical data from 24 primary and secondary schools in eastern and central China, constructs comparative data tables to verify the teaching effectiveness of STEM interdisciplinary integration, and selects typical elementary and secondary school classroom cases for in-depth analysis. The research results show that standardized interdisciplinary curriculum embedding, project-based teaching iteration, school-university collaborative training, and diversified competency evaluation can significantly improve students’ STEM literacy and interdisciplinary problem-solving abilities. This study provides practical implementation paths and empirical references for the popularization and high-quality development of STEM interdisciplinary teaching in primary and secondary schools.
Research on AI-Driven Digital Exhibition Curation and Dissemination in Private Universities
Against the backdrop of the in-depth advancement of digital transformation in higher education, campus exhibitions in private colleges and universities have become an important carrier for carrying the cultural connotations of the campus, showcasing the school's distinctive features and achievements in talent cultivation, and also a key window for external communication and brand promotion. At present, most exhibitions of private colleges and universities still follow the traditional offline static mode, which generally has prominent problems such as monotonous and rigid display forms, lack of novelty in content presentation, insufficient integration of online and offline, and limited coverage of cultural dissemination. It is difficult to meet the development needs of the digital age. The rapid development of artificial intelligence technology provides a brand-new path and technical support for the innovation of exhibition models in private colleges and universities. Based on the educational positioning, unique characteristics and existing resource conditions of private colleges and universities, this article systematically analyzes the practical value of applying artificial intelligence to digital exhibitions in colleges and universities, sorts out the shortcomings and deficiencies of traditional exhibition models, and constructs implementation paths and optimization strategies for AI digital exhibitions that are suitable for the actual situation of private colleges and universities from multiple dimensions such as intelligent curation, content innovation, all-domain dissemination and operation and maintenance management.
AI + Film and Television IP-driven Private Media Curriculum Innovation Research
With the rapid development of artificial intelligence technology and the increasingly mature film and television IP industry chain, media education is facing profound pressure of change. As an important force in the cultivation of media talents, private colleges and universities are flexible in mechanism, but they also face resource constraints and transformation difficulties. This paper discusses the path and mechanism of ' AI + film and television IP ' driving the innovation of private media courses, analyzes its positive impact on the renewal of teaching content, the cultivation of practical ability and the integration of production and education, and puts forward corresponding curriculum reform strategies for practical challenges such as teachers, resources and ethics. The research shows that the systematic integration of AI tools and IP operation thinking into the curriculum system is an effective way to improve the quality of private media education and enhance students ' employment competitiveness.
Status, Hotspots, and Trends of Teacher Competence Research in Education: A Bibliometric Analysis Based on CiteSpace
Teacher competence has become an increasingly important topic in contemporary educational research, particularly in the context of educational digitalization and technology-supported teaching. This study employed CiteSpace and bibliometric methods to analyze teacher competence research in education based on publications indexed in the Web of Science Core Collection database from 2022 to early 2026. A total of 2,946 publications were included in the analysis. Publication trends, country and institutional distributions, keyword co-occurrence, clustering analysis, timeline visualization, and burst keyword detection were conducted to identify the major research hotspots and developmental trends in this field. The results indicate that teacher competence research has shown a continuous growth trend in recent years and has gradually expanded from traditional pedagogical competence toward more technology-related and interdisciplinary research areas. Major research topics include teacher education, higher education, medical education, nursing education, inclusive education, and self-determination theory, while digital competence and technology-enhanced teaching have emerged as important cross-cutting themes across different educational contexts. The keyword evolution and burst analyses further suggest that recent studies have increasingly focused on online learning, student learning outcomes, digital pedagogy, and teachers’ psychological adaptability in changing educational environments. In addition, the findings reveal that teacher competence research has become increasingly contextualized and practice-oriented across different educational settings, including higher education, medical education, and inclusive education. Contemporary studies place growing emphasis on teachers’ technological adaptability, instructional innovation, and continuous professional learning in digital educational environments. This study provides a systematic overview of the knowledge structure and evolutionary trends of teacher competence research and offers references for future teacher education, educational technology integration, and professional development research in the digital era.
Research on Ethical Boundary and Aesthetic Paradigm of AIGC's Collaborative Creation with Designers
The breakthrough development of artificial intelligence generated content (AIGC) technology is profoundly reconstructing the underlying logic of design and creation, which has spawned a new paradigm of "human-computer collaborative creation". Focusing on the ambiguity of ethical boundary and the transformation of aesthetic paradigm in AIGC's collaborative creation with designers, this paper systematically analyzes the three dimensions of ethical conflict in human-computer collaborative creation, such as the ambiguity of rights and responsibilities of the creative subject, the dislocation of the value of the creative process and the conflict of the social influence of the creative achievements, and puts forward four dynamic definition principles: human subject-oriented, equal rights and responsibilities, transparency and traceability, aesthetic positive and value bottom line. On the level of aesthetic paradigm, this paper discusses the deconstruction process of traditional design aesthetic paradigm, reveals the reconstruction trend of aesthetic characteristics under the intervention of AI, and constructs a four-layer dialogue cycle model of human-computer cooperation aesthetic mechanism, which includes "intention translation-generation expansion-evaluation negotiation-feedback evolution". Finally, this paper puts forward the specific path of ethical and aesthetic collaborative governance from the three dimensions of technology, system and education, and emphasizes that the balance between technological innovation and humanistic value can be achieved through the construction of the technical base of "goodness" and "beauty", the establishment of the rule framework and the remolding of designers' aesthetic quality, so as to ensure that man-machine collaborative creation always serves the subjective value of people and the deep authenticity of culture.
Expression, Spread and Governance of Misogynistic and Anti-Feminist Discourse on Social Media
Misogynistic and anti-feminist discourse finds in social media a medium that is both large-scale mass communication and intimate peer network, as well as have been amplified by the amplification logic of algorithmic recommendation systems. Based on the examination of how this kind of discourse is expressed, disseminated and regulated across major social media platforms through application of Foucault's Discourse Theory, digital misogyny studies and platform-politics paradigm respectively. The following three interrelated arguments are developed as follows: First, Online misogynistic discourse exists along a continuous spectrum from direct harassment to subtle ideological infiltration; The second is that algorithm-based recommender Systems and the dynamic formation mechanisms of communities actively strengthen Anti-feminist information by exceeding individual users' actions; Third, current governance response measures - platform content moderation, legal framework system, Counter-discussion Strategies have structural flaws making it difficult to effectively address the systemic aspects of this issue. Based on this research, the following conclusions have been reached in relation to platform liability, feminist digital activism, etc., under the framework of political discussion online.
Research on the Path of Transforming Ideological and Political Education in Police Colleges from Separated Model to Integrated Model
Currently, the education system in police colleges focuses on the systematic transmission of basic theoretical knowledge as the core of imparting professional knowledge to undergraduates, a process which is closely connected and mutually supportive with ideological and political education (IPE). However, the traditional "separated" IPE model faces problems such as the disconnection between ideological and political elements and professional knowledge teaching, and ambiguous course attribute definitions. The core of the IPE reform at Police Academy M lies in the transformation and upgrading from a separated model to an integrated model. By constructing a comprehensive and systematic integrated IPE architecture, the reform deeply analyzes the subsystems and constituent elements of the IPE system, clarifies the interaction relationships among them, and achieves the organic integration of implicit education and explicit education. This reform reflects the innovation and exploration of police colleges in the field of IPE, helps promote the iteration of the "New Policing" education concept in police colleges, implements the educational goal of "Three Alls Education" (All Members, Whole Process, and All Dimensions), cultivates modern policing talents with firm political positions and high moral qualities, and enhances the new quality of public security combat effectiveness through the educational environment.
Research on Ethical Boundaries and Emotional Responsibility of AI Companion Products from the Perspective of Human-Machine Communication
With the rapid iteration of emotional computing and natural language processing technologies, AI companion products have gradually penetrated into daily life, reconstructing the pattern of human-machine communication and forming a new type of emotional interaction relationship between humans and machines. From the perspective of human-machine communication, this paper explores the ethical boundaries and emotional responsibilities of AI companion products, which are core issues in the process of their popularization. By using literature research, data analysis and case study methods, this paper sorts out the evolution of human-machine communication in the context of AI companions, identifies the ethical dilemmas such as data privacy leakage, emotional dependency alienation and false emotional expression faced by AI companion products, and quantifies the impact of these dilemmas through relevant data. The research finds that 51% of users regard AI companions as friends or romantic partners, and 0.15% of weekly active users of AI companion platforms have mentioned suicide thoughts or plans, which indicates that the blurred ethical boundaries of AI companion products may bring potential psychological risks to users. On this basis, this paper clarifies the emotional responsibility connotation of AI companion products from three dimensions: product design, operation and supervision, and puts forward targeted ethical norms and responsibility fulfillment paths. The research enriches the theoretical system of human-machine communication, and provides practical reference for the healthy development of AI companion industry and the standardized construction of human-machine emotional interaction.
A Study on Discursive Construction of Gender Identity in Literary Translation Driven by Big Data
With the rapid development of big data technology, the research paradigm of literary translation has gradually shifted from traditional qualitative analysis to a mixed research method combining qualitative and quantitative analysis. Gender identity, as a core issue in feminist translation studies and discourse analysis, is often constructed and reshaped through the discursive choices in translation practice. This study takes the English translations of modern Chinese female writers’ works as the research corpus, constructs a big data-driven analytical framework, integrates corpus linguistics, critical discourse analysis (CDA) and gender studies, and explores the discursive construction mechanism of gender identity in literary translation. By using big data mining tools to extract and analyze lexical, syntactic and rhetorical features in the translation corpus, this study establishes a quantitative evaluation model of gender identity discursive construction, verifies the influence of translators’ gender awareness, cultural context and big data analysis tools on the discursive construction of gender identity, and puts forward targeted optimization strategies. The research finds that big data technology can effectively identify the hidden discursive clues of gender identity in translation, and the choice of translation strategies has a significant impact on the shaping of gender images; the combination of quantitative data analysis and qualitative discourse interpretation can more accurately reveal the dynamic construction process of gender identity in literary translation. This study enriches the research perspective of gender identity in literary translation, provides a new methodological reference for the integration of big data and literary translation research, and promotes the innovation and development of translation studies.
Data Property Rights, Algorithm Intellectual Property Rights and Legal Protection in the Artificial Intelligence Era
With the rapid iteration of artificial intelligence (AI) technology, data and algorithms have become the core production factors driving the digital economy, and their property rights definition and legal protection have become urgent issues in global legal practice and academic research. Data, as the "fuel" of AI, and algorithms, as the "brain" of AI, are interdependent and mutually reinforcing, but their unique attributes—such as the non-exclusivity of data, the invisibility of algorithms, and the complexity of rights subjects—have brought unprecedented challenges to the traditional intellectual property and property rights legal systems. This paper systematically explores the connotation and structural characteristics of data property rights and algorithm intellectual property rights in the AI era, constructs mathematical models to quantify the rights boundary and value distribution, analyzes the legal dilemmas in practice, and proposes targeted legal protection paths based on comparative law research. The research shows that the multi-dimensional definition of data property rights based on the whole life cycle and the hierarchical protection of algorithm intellectual property rights based on technical characteristics are the core of solving the current disputes; the establishment of a dynamic legal protection system that integrates legislation, law enforcement and judicial protection can effectively balance the interests of multiple subjects, promote the rational flow and innovative application of data and algorithms, and provide legal guarantee for the high-quality development of the AI industry.
Research on Digital Teaching Innovation and Literacy Cultivation Paths of University Teaching in the Integrated Media Environment
The in-depth development of integrated media has pushed college teaching into a new stage of digital transformation. Traditional college teaching faces practical problems such as solidified teaching forms, low utilization rate of digital resources, and insufficient adaptation of teachers and students’ digital literacy, which can hardly meet the educational needs of higher education in the new era. Taking the internal correlation between integrated media communication characteristics and college digital teaching as the entry point, this paper adopts the literature research method and current situation analysis method to sort out the development opportunities and practical dilemmas of college digital teaching in the integrated media environment. Based on educational technology theory, constructivist learning theory and media literacy theory, it explores the core logic of digital teaching innovation. From four dimensions including teaching mode innovation, digital resource construction, teacher and student literacy cultivation, and teaching evaluation optimization, this paper constructs a college digital teaching system adapted to the integrated media ecology, and proposes practical teaching innovation strategies and literacy cultivation paths. The research aims to solve the problems of formalized digital teaching and fragmented literacy cultivation in colleges and universities, help colleges and universities implement the requirements of new liberal arts construction, improve the quality of digital education, and provide theoretical reference and practical guidance for college integrated media teaching reform.