Introduction

With the acceleration of global digital transformation, design education is undergoing profound changes. Its goal is no longer limited to the cultivation of traditional esthetic skills, but it emphasizes the integration of interdisciplinary knowledge and the ability to solve complex social problems. Digital technologies represented by virtual reality (VR) and artificial intelligence are reshaping the way of design practice and changing the traditional model of teaching and learning, from one-way knowledge transfer to an interactive and co-creative learning ecology. However, current research mainly focuses on the technical effectiveness of digital tools, ignoring the importance of the coordinated development of technical capabilities and humanistic literacy, resulting in an imbalance between “technical empowerment” and “value guidance” in educational practice. At the same time, social structural inequality issues (such as class differences, racial differences, and uneven distribution of educational resources) significantly affect students’ opportunities to receive equal education, further widening their gap in innovation ability cultivation and career path selection.

Therefore, this paper aims to explore the following two core research questions:

  1. 1.

    In digital design education, how do technology integration and interdisciplinary cooperation jointly promote the development of students’ innovative thinking?

  2. 2.

    How do institutional barriers put students from different social backgrounds in an unequal position in terms of access to educational resources and the construction of professional networks, and thus affect their career development opportunities?

Current research on the cultivation of innovative thinking in design education mainly focuses on the role of digital tools in supporting the learning process. Existing studies have shown that digital technology can expand students’ conceptual dimensions and enhance their interdisciplinary collaboration capabilities (Cherbonnier et al., 2025; Ivaniuk et al., 2022). Other studies have shown that digital technology promotes the development of critical thinking and problem-solving skills to a certain extent (Gonzalez-Mohino et al., 2023; Wang and Li, 2024). However, research on how digital design education systematically affects students’ innovative abilities (such as technical skills, cognitive abilities, and interdisciplinary integration abilities) is still insufficient. The social equity perspective has not received due attention in the field of design education. Although sociological research has widely revealed the close relationship between social background and educational opportunities, emphasizing the importance of fair resource allocation to social equality (Eden et al., 2024), design education research has mostly focused on technological innovation and creativity, ignoring the profound impact of resource and opportunity differences on student development. Social equity is not only about the allocation of educational resources but also involves students’ cultural background and career expectations. This study aims to explore the mechanism of digital design education on innovation ability and analyze how social background affects students’ career development paths through differences in educational resources and professional networks, thereby providing a new perspective on social equity in design education.

In the context of the digital age, the issue of student education equity has received extensive attention and research from a multidimensional perspective. Educational evaluation methods based on questionnaire surveys and experimental designs are widely used in teaching effectiveness and student performance (Munna and Kalam, 2021). Experimental design helps to intuitively show the impact of digital tools on student learning behavior, providing a methodological reference for the empirical part of this study. Studies have shown that digital technology has played a positive role in improving students’ learning enthusiasm and classroom participation (Adiyono et al., 2024; Yu et al., 2022), and their design ability and innovative thinking levels have been significantly improved, providing a solid empirical basis for this study to focus on the cultivation of innovative thinking. Analysis of digital applications in multiple design courses shows that the introduction of digital technology helps promote the development of students’ innovative ability (Boychuk et al., 2024; Obeid and Demirkan, 2023). However, the study also points out a key limitation of current research, that is, insufficient attention to the issue of equal resource support for students from different social backgrounds. Therefore, this study combines SEM and SNA to analyze how digital design education can play a role in promoting educational equity while stimulating students’ innovative thinking.

This study aims to analyze how digital design education can improve innovative thinking through technical training and interdisciplinary collaboration, and analyze the impact of social equality factors on career development. Based on the innovation diffusion theory, social cognitive theory, and educational equity theory, a comprehensive analysis framework of “technology-society-equity” is constructed through SEM and SNA, and the mechanism of digital education in cultivating innovative thinking and students’ career paths is analyzed, respectively. Structural equation model is used to analyze the changes in students’ learning behavior and innovative thinking, and to identify potential factors affecting the formation of innovative ability; SNA studies the interactive pattern of resource allocation among students and reveals the impact of social equality factors on the formation of students’ career opportunities from the perspective of social networks. The research results provide a theoretical basis for optimizing digital education strategies and propose a new perspective framework for designing social equality issues in education.

The global digital transformation has promoted profound changes in design education. The application of digital technology has changed the traditional teaching methods and opened up new paths for cultivating innovative thinking and career development (Wannapiroon and Pimdee, 2022; Zhu, 2020). However, how to maximize the use of digital resources to promote students’ innovative ability and how social background affects career development opportunities still require more in-depth theoretical discussion. Based on the innovation diffusion theory, social cognitive theory, and educational equity theory, this study constructs a comprehensive analytical framework to reveal how digital design education affects students’ innovative thinking and career development through technical training, interdisciplinary collaboration, and social network resources.

Innovation diffusion theory (Ho, 2022) believes that the adoption and spread of new technologies can change individuals’ cognitive and behavioral patterns. In design education, digital tools (such as VR, augmented reality (AR), AI, etc.) can improve students’ creativity, adaptability, autonomy, and risk management attitude (Adeel et al., 2023). They provide a more flexible way of design expression, allowing students to break through the limitations of traditional methods and explore more creative solutions (ElSayary, 2024). Virtual reality technology allows students to quickly iterate design concepts in an immersive environment, thereby improving critical thinking and problem-solving skills (Baxter and Hainey, 2024; Alifteria et al., 2023). This theory provides the core logic for digital design education to enhance technical capabilities.

Digital tools (such as virtual reality (VR) and AI) provide an immersive and interactive learning environment, allowing students to quickly iterate design concepts (such as virtual prototype testing), breaking through the physical limitations of traditional methods. For example, VR technology promotes students’ multi-angle exploration of complex problems by simulating real scenes, while AI generative design tools (such as MidJourney) stimulate experimental thinking through algorithm-driven, encouraging students to try unconventional solutions.

Social cognitive theory (Jiang et al., 2023) points out that individual cognitive development originates from social interaction and observational learning. In interdisciplinary collaboration, students are exposed to multiple knowledge systems through team projects, and their design thinking is activated in cognitive conflict and knowledge integration (Park and Lee, 2021). For example, the collaboration between students with engineering and art backgrounds can generate solutions that are both functional and esthetic (Yumin and Isa, 2024). This collaboration not only strengthens technical capabilities but also enhances critical thinking and problem-solving abilities through collective efficacy. Digital platforms provide access to the latest international design trends and encourage interdisciplinary collaboration and communication (Indraprastha, 2023). This collaborative model not only broadens the dimensions of thinking but also cultivates the ability to conduct comprehensive analysis and innovative problem-solving.

Interdisciplinary collaboration activates innovative thinking through cognitive conflict and knowledge integration. For example, the collaboration between students with engineering and art backgrounds needs to combine functional and esthetic needs to promote the simultaneous improvement of technical capabilities and critical thinking. Digital platforms (such as Figma and Miro) support real-time collaborative design and promote the dynamic integration of knowledge in multiple fields.

Educational equity theory (Caiyan, 2022) emphasizes that fair distribution of resources is the basis for reducing social inequality. In design education, students’ career opportunities often rely on the distribution of social network resources and the social equalization effect of digital education. Students from high social classes usually have more extensive industry connections (such as mentors, employers, etc.), thus obtaining more internship and employment opportunities. However, students from lower social classes may face career barriers due to lack of resources (McCafferty, 2022). Online learning platforms can partially make up for the inequality of traditional education and enable students from different backgrounds to obtain similar learning opportunities (Tate and Warschauer, 2022). However, differences in the structure of social networks may still lead to uneven career development opportunities.

The popularization of digital technologies (such as online learning platforms and open educational resources) can break down the geographical and economic barriers in traditional education and provide equal learning opportunities for students from different social backgrounds. For example, students from lower social classes can access the same design course resources as students from higher social classes through online platforms (such as MOOCs), thereby narrowing the initial gap in technical capabilities. However, if there is a lack of targeted support for disadvantaged groups (such as equipment subsidies or tutoring), technological empowerment may be monopolized by higher social class students first, which in turn exacerbates the inequality in resource allocation. Therefore, the integration of digital education and social equity needs to be achieved through the dual mechanisms of technology popularization and fair intervention.

Social capital theory (Wang and Zhang, 2023) provides a theoretical foundation for understanding how social networks influence career development. This theory posits that individuals’ access to resources, such as information, mentorship, and job opportunities, is embedded in their social relationships. Structural characteristics of networks (such as density, centrality) determine the flow of these resources, with well-connected individuals gaining advantages in career mobility. In design education, students from higher social classes often possess richer network resources, while disadvantaged groups face structural barriers. By analyzing network topology (such as node connections, bridging ties), SNA operationalizes social capital to reveal how unequal network structures perpetuate career disparities, a gap that equitable resource allocation can mitigate.

The cultivation of innovative thinking in digital design education requires the synergistic integration of technical, cognitive, and interdisciplinary capabilities, all of which are rooted in different theoretical foundations. Technical capabilities are rooted in the theory of diffusion of innovation, covering the ability to use digital tools (such as VR, AI) to solve design challenges, emphasizing operational efficiency and the ability to solve problems autonomously. Cognitive capabilities are consistent with social cognitive theory, involving critical analysis, reflective learning, and adaptive reasoning, reflecting how individuals internalize knowledge through self-regulation and observational learning. Interdisciplinary capabilities are guided by educational equity theory, focusing on collaborative knowledge integration and equitable resource access, enabling students to integrate multiple perspectives in team innovation. The cultivation of innovative thinking in digital design education is rooted in the synergy of innovation diffusion (technology adoption), social cognition (collaborative learning), and constructivism (active knowledge construction). These theories collectively explain how digital tools transform passive skill training into dynamic innovation ecosystems. These capabilities operate at complementary levels of technical execution, personal cognition, and social collaboration, and together form a technical-cognitive-collaborative framework that addresses the issues of technological progress and social equity in design education.

Based on the collaborative path of the theoretical framework “technology-cognition-collaboration”, this paper proposes three hypotheses:

H1: Digital design education can significantly improve students’ innovation and technical capabilities;

H2: Through interdisciplinary cooperation and technical integration, students’ innovative thinking has been effectively improved;

H3: The fair distribution of educational opportunities and network resources can effectively narrow the career development gap.

Methods

Data collection

This study utilizes questionnaire surveys, semi-structured in-depth interviews, and career tracking for data collection (Ruslin et al., 2022; Taherdoost, 2022). Questionnaire surveys and in-depth interviews maintain strict control over data collection process to ensure reliability and validity of quality. Career path tracking adopts anonymous methods to safeguard respondent privacy and minimize social expectation effects on data. Questionnaire design undergoes multiple pre-experiments and expert reviews to guarantee question validity, clarity, and pertinence.

Questionnaire content covers students’ technical integration ability, critical thinking, interdisciplinary collaboration ability, problem-solving ability, social background information, access to educational resources, and career paths. The design has undergone multiple pre-experiments and revisions to reduce ambiguity and misunderstanding and improve data accuracy. The representativeness and diversity of the sample are ensured by combining random sampling and stratified sampling. The sample covers students from different social classes, ethnic backgrounds, and educational backgrounds to reduce the interference of social background factors on the research results.

The questionnaire includes “years of using digital tools” (1 = less than 6 months, 5 = more than 3 years) and “technology acceptance” (such as “I think digital tools are easy to use”, 1 = totally disagree, 5 = totally agree). To control the impact of other potential variables on career development, this study collects data on students’ academic performance (measured by GPA, 1–5 points) and internship experience (including number of participations, duration, and company level) in the questionnaire. In the SEM, these variables are included in the analysis as control variables to isolate the independent effect of social background.

Data is collected from six universities in China, including two public comprehensive universities (University A, University B) serving urban and suburban populations from different socioeconomic backgrounds. Three professional art/design colleges (College C, College D, College E) are known for their high penetration of digital tools, but with limited access for low-income students. One vocational college (College E) primarily enrolls students from rural areas with limited digital infrastructure. This selection ensures a representative sample covering different institution types, geographic locations, and social classes, which is critical for analyzing the association between digital education and inequality.

The research subjects are undergraduate and graduate students majoring in design from different educational backgrounds, social classes, and different skin colors. Through cooperation with six universities, 452 design students are randomly selected as samples, including students from comprehensive universities, art and design colleges, vocational and technical colleges, and other types of colleges. The sample covers students from different social classes (high, middle, and low classes) and ethnic backgrounds, ensuring broad representation in terms of social class and ethnic background. The data collection process includes a questionnaire survey for students, followed by in-depth interviews, and finally tracking the students’ career development paths. This process provides data on students’ background and access to educational resources for SEM analysis, and provides qualitative data on students’ social networks and interactive relationships for SNA analysis, in order to comprehensively evaluate the impact of digital design education on different groups.

Social class is categorized based on self-reported family annual income, parental occupation, and education level, with reference to China’s income distribution data and occupational classification systems. Specifically, low social class is defined as family annual income below 100,000 RMB (or 60% of the local median income), with parents engaged in agricultural labor or basic service jobs and having high school education or below; middle social class as family income between 100,000–300,000 RMB, with parents working as professionals or managers and at least one parent holding a bachelor’s degree or above; high social class as family income exceeding 300,000 RMB, with parents in senior management or professional positions and both parents holding bachelor’s degrees or above. This classification is validated through a pilot study (κ = 0.81, p < 0.001) and expert review (90% consistency rate), with adjustments made for urban-rural differences (such as lower income thresholds for rural areas). The statistical information of the student sample size is shown in Table 1.

Table 1 Statistical information of student samples.
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The racial composition of the sample (80% White) reflects the demographic distribution of the participating universities. While this limits the generalizability of race-based findings, the study still provides insights into how social background (such as class and education) interacts with digital education and career development.

This study collects data on students’ performance in digital design education through questionnaires and interviews, covering innovative thinking, technical capabilities, and social background. The assessment emphasizes technology integration, critical thinking, and interdisciplinary collaboration. The impact of social background on education and career development is analyzed to ensure a comprehensive understanding of students’ multi-dimensional performance and digital design education. Employment status, career paths, salary levels, and career advancement are investigated. The questionnaire uses the Likert five-point scale to accurately quantify the dimensions of students’ cognition and behavior (Kusmaryono et al., 2022; Rokeman, 2024).

This study analyzes the actual impact, digital design education, students’ innovative thinking, role, social background, and career development and conducts semi-structured in-depth interviews with 26 students. Interviewees are randomly selected, and the questionnaire survey covers student groups from different social backgrounds. The semi-structured interview method allows interviewees to freely express their opinions, be guided and delve into discussions and research topics. The interview process is conducted by trained researchers to ensure the objectivity and consistency of the data. The interview content is fully recorded and transcribed into a transcript for subsequent data coding and analysis.

This study tracks the career paths of graduates through alumni networks and social media platforms, monitors their employment status, and collects data on job types, salary levels, and career advancement to analyze the impact of social background on students’ career development. In addition, students’ career social network information (such as connections with industry experts, mentors, and employers) is also collected as additional data. These data help to further analyze the differences in social backgrounds faced by students in their career development.

The data collection spans six months. In the first phase (January–February), questionnaires are distributed, and pre-surveys are conducted in participating colleges to ensure the effectiveness of the questionnaire design and the clarity of the questions. During this period, in-depth interviews with some students are also conducted. In the second phase (March–April), a comprehensive questionnaire survey is conducted to collect valid questionnaires from 452 students, and 26 students are randomly selected for in-depth interviews simultaneously. In the third phase (May–June), a career development tracking survey is conducted based on the employment data of students after graduation, and relevant data is collected to improve the analysis of students’ career paths. All data are entered and preliminarily cleaned up through professional data analysis software (such as SPSS, Excel, etc.) to ensure data quality and consistency.

To ensure the validity and reliability of the data, this study conducts a pre-experiment when designing the questionnaire, invites some students to fill in the questionnaire, and modifies the questionnaire based on the feedback. In addition, during the interview process, the research team strictly controls the interview process to ensure the consistency and objectivity of information collection. In the data analysis stage, data cleaning, outlier removal, and missing value filling techniques are used to improve the accuracy of the data.

All students participating in the study sign an informed consent form before data collection to ensure the ethical compliance of the study. Students’ personal information is strictly confidential, and only anonymous data is used in research reports. All data storage and processing comply with relevant data protection regulations.

Reliability and validity evaluation of innovative thinking

In this study, students’ innovative thinking is evaluated by using a variety of data collection methods, integrating the results of questionnaires, behavioral observations, and project analysis to comprehensively analyze the role of digital design education in students’ innovative ability. The study focuses on undergraduate and graduate students who have completed at least one semester of digital design courses, and the sample covers students from different socioeconomic backgrounds and ethnic groups. This approach ensures a deep understanding of the impact of digital design education and takes into account diverse social factors.

The research team conducts classroom observations in multiple digital design courses, focusing on recording students’ performance in actual projects. Observation indicators include how students use digital tools such as VR and artificial intelligence to create, as well as their interactions in group collaboration. The observation data is recorded using a combination of quantitative and qualitative methods and further analyzed through video materials to ensure the objectivity and comprehensiveness of the data.

The evaluation of innovative thinking is based on the key criteria of technical integration ability, critical thinking, cross-domain collaboration ability, and problem-solving ability. These indicators are concretized in the questionnaire survey and behavioral observation, in order to quantitatively analyze the impact of digital design education on students’ innovative thinking. This study adopts a multidimensional measurement method, combining a 5-point Likert scale (1 = completely disagree, 5 = completely agree) and behavioral observation data to ensure the reliability and validity of the variables.

The technical matching ability measures the efficiency, communication interaction, and operational level of digital tools (such as VR and AI model building) (α = 0.87). The problem is: “I have the ability to use digitalization tools to realize design planning”, and the degree of integration of detailed reflection technology operation and design planning.

The self-evaluation table (α = 0.85) is the key to the problem of “self-analysis and design planning, as well as the improvement and improvement of the system’s strengths”. The emphasis is on the analytical depth and reflective ability of current students who study the problem and the basis of physical and new thinking.

The ability for cross-interdisciplinary collaboration is assessed through a combination of observation and self-examination (α = 0.89). The performance and quality of the small group, as well as the self-examination of performance such as “I have the ability to complete the design of a collaborative work based on the same industry background”, and the collaborative performance between students and multiple original companies, are reviewed.

Multisource number setting for solution problem ability (α = 0.83): the self-assessment table (such as “I can independently solve problems and solve problems in the process”) assigns practical item results analysis (comprehensive design prototype innovation and technical feasibility) to form a method triangle test, ensuring quantitative efficiency.

The collected questionnaire data is first tested for reliability and validity. Cronbach’s α coefficient is used to evaluate the internal consistency of each scale to ensure that the scale has a high reliability (Amirrudin et al., 2021; Nawi et al., 2020). For the scoring results of each dimension of innovative thinking, descriptive statistical analysis (such as mean, standard deviation, etc.) is used to show the differences in students’ performance in different dimensions. In addition, Exploratory Factor Analysis (EFA) is used to extract the various dimensions of innovative thinking and verify its structural validity.

The scale used in this study is based on validated tools in existing literature (such as technology integration ability, see Gonzalez-Mohino et al., 2023, critical thinking, see Wang and Li, 2024) and is adaptively adjusted in combination with the characteristics of design education. To ensure the validity of the scale, the wording of the questions is optimized through expert review, and EFA and confirmatory factor analysis (CFA) are conducted.

The quantity table shows reliability efficiency. Cronbach’s alpha system number is 0.91; the combined reliability (CR) average is 0.8; the average variance extraction (AVE) average is 0.5. The difference in the four-factor cumulative calculation method of exploratory factor analysis (EFA) is 72.3% (KMO = 0.89, Bartlett’s score p < 0.001), and the test results of CFA (CFI = 0.93, RMSEA = 0.05) support the scale results, as shown in Tables 2 and 3.

Table 2 Credibility test based on new ideas.
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Table 3 New thinking table effectiveness game.
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To ensure the stability of the measurement tool in different subgroups, this study calculates Cronbach’s α coefficient for gender (male/female), social class (high/middle/low), and race/skin color (white/non-white). The results show that the internal consistency of each dimension meets the threshold (α > 0.7), indicating that the scale has cross-group reliability (see Table 4). In addition, multi-group confirmatory factor analysis (Multi-group CFA) verifies the cross-group invariance of the measurement model, and the model fit index meets the standard (CFI > 0.90, RMSEA < 0.08), supporting the universality of the factor structure.

Table 4 Reliability test by group.
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To address the possible common method bias (CMB) in the self-report data, the following assessments are conducted, as shown in Table 5. Harman’s single-factor test shows that the first unrotated factor explains only 34.2% of the total variance (less than the 50% threshold), indicating that there is no dominant single factor. The pairwise correlation coefficients between key constructs (such as technical ability and innovative thinking) are below 0.7, indicating that there is no serious multicollinearity. The unmeasured latent method factor (ULM) model shows a slight improvement in fit (ΔCFI = 0.008), which confirms that CMB does not distort the structural equation model (SEM) results.

Table 5 Common method bias test.
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Impact of social equality on students’ career development

To fully understand students’ cognition of social equality, this study uses questionnaires and in-depth interviews to investigate students’ views on how factors such as social class, skin color, and race affect their career development. In the questionnaire, relevant questions are designed to assess students’ perception of unequal career opportunities. The questionnaire content includes “Do you think that your social class has an impact on your career development?” (1 = completely disagree, 5 = completely agree). At the same time, in-depth interviews are conducted with some students individually to understand whether they feel unequal treatment caused by their social background during job hunting and career development.

This study also collects social network resources that students rely on in their career development, including social relationship data such as peers, mentors, industry experts, and employers. Students’ social resources are tracked through social media, career network platforms, and alumni networks, focusing on the equality of students from different backgrounds in obtaining career resources. Students’ self-administered questionnaires report on the social relationships they rely on for career development, such as mentor recommendations, participation in industry conferences, and connections on social platforms. Table 6 shows the relationship between the richness of social resources and career opportunities:

Table 6 The relationship between the richness of social network resources and career opportunities.
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Table 6 shows that the richness of social network resources is significantly positively correlated with career opportunities. Students with fewer social network resources have relatively low numbers and quality of career opportunities, and their industry connections and mentor recommendations are relatively limited. Students with medium social network resources have increased career opportunities and strengthened industry connections to a certain extent, and mentor recommendation intentions are at a medium level, which increases career development opportunities accordingly. Students with high social network resources are exposed to more high-quality career opportunities, have close industry connections, and receive frequent mentor recommendations, which significantly promotes career development. Overall, the richer the social network resources, the greater the potential for students’ career development, especially for students from low social classes, where social network support is crucial.

First, quantitative data analysis is used to explore the relationship between social background variables and students’ career development. Multiple regression analysis is used to analyze the impact of variables such as social class, skin color, and race on students’ starting salary, job type, and promotion opportunities. By controlling other possible interference factors (such as academic performance, internship experience, etc.), the impact of social background on students’ starting careers is verified.

Through in-depth interview data, the obstacles and challenges encountered by students from different backgrounds in the job search process are analyzed. The interview content mainly focuses on how students view the impact of social background factors on career choices and promotion paths. Combined with qualitative analysis methods, the researchers code and classify the interview content, extract the main social factors that affect students’ career development (such as skin color prejudice, class barriers, etc.), and compare them with quantitative data to form a comprehensive analysis framework.

This paper further analyzes the data on social inequality perception to examine the relationship between students’ social class perception and their career development opportunities. Based on students’ cognition of social inequality, SEM is used to explore whether students’ social class cognition has a significant impact on their career choices, salary levels, and promotion paths. This analysis helps to verify the role of students’ perception of inequality in actual career development and whether there is a gap between cognition and reality.

When analyzing the impact of social network resources on students’ career development, the SNA method is used to study the relationship between professional social network structure and career development (Wittner and Kauffeld, 2023). Through questionnaires, students’ relationship data on social platforms is collected to construct a professional social network map. Based on graph theory methods, indicators such as network density and centrality are calculated to evaluate the possibility of students obtaining career opportunities through social networks. The study finds that students from different social backgrounds have significant differences in the construction and utilization of professional social networks, and low-class students have significantly insufficient network resources for high-level positions and industry expert contacts.

While this study does not directly measure individual traits (such as personality), the stratified sampling design and longitudinal tracking of network growth (from baseline to post-intervention) help mitigate the confounding effects of pre-existing network access or personal initiative. Future research can explicitly incorporate personality assessments to strengthen causal inference.

As a result, the model is constructed and changed

This research is based on the development of a new theory, social theory, knowledge structure, construction framework, analysis, digitalization, design, education, student design, and new thinking influence system. The new technology has been developed, and the technology has changed during the main research.

Currently, under the pedestal, the research model includes the following external learning potential: (1) technical training (reflection digitization tool usage level) three-dimensional review: ① VR tool operation proficiency, ② AI construction modeling work performance test, ③ technology problem independent resolution rate; (2) cross-disciplinary collaboration (depth of participation in the body of collaboration and learning). In addition to external production changes, this includes the following: (1) technical ability (horizontal mastery of weighing and digitization tools); (2) appreciation ability (high-level appraisal skills such as critical thinking), and the ultimate collaborative ability to create latent changes in new thinking (combined creative problem-solving ability).

The maximum number of calculations for the running model, the general accuracy index, and the model alignment are shown in Table 7.

Table 7 Comprehensive model design guide.
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Table 7 shows that the model has a uniformity of specifications and has reached a standard level, and the theory of the model has a good adaptability. The alignment results of the model support theory, framework construction, and double-path mechanism: technical design and tool processing efficiency directly enhance technological power, and new ideas are combined with theory, such as cross-disciplinary collaboration, social interaction, promotion of social interaction, and recognition of social knowledge. The combination of the two paths shows the indivisibility of digitalization in education and the ability of social chemistry.

Social network analysis

This study employs SNA to examine how students’ career networks—shaped by social capital—affect their professional opportunities. Grounded in social capital theory (Lenkewitz and Wittek, 2022), SNA quantifies resource distribution through metrics like network density (reflecting closeness of ties) and centrality (measuring access to influential actors). By mapping these dynamics, whether equitable resource allocation reduces network-based inequalities is assessed, thereby testing H3.

Equal distribution of network resources is achieved through institutional interventions, including mentoring programs that pair low-class students with industry professionals and quota allocation of internship positions. Social network analysis quantifies the intervention effect by comparing network metrics (such as density, centrality) before and after the intervention and verifies statistical significance through bootstrapped resampling. The analysis hypothesizes that: (1) resource redistribution primarily expands access to social capital; (2) the network growth pattern is consistent with empirical observations from career tracking data.

To comprehensively evaluate the social network of students in their career development, the study collects career social network data through questionnaires, interviews, social media, and career network platforms. The questionnaire collects students’ career development path data, covering job positions, industry backgrounds, and career promotions. The questionnaire also asks students to provide social network information related to their careers, focusing on the frequency of contact, degree of cooperation, and resource sharing with peers, mentors, industry experts, and employers. Data collection is carried out through online questionnaires and face-to-face interviews to ensure the breadth and depth of information. All collected data are collated and anonymized to protect the privacy of participants. These data provide a basis for subsequent SNA and support in-depth exploration of the relationship between students’ social networks and career development, as shown in Fig. 1.

Fig. 1: Student social network structure.
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Nodes represent students or their contacts (peers, mentors, employers, industry experts), and edges represent the connections between them. The network structure visualizes the composition and resource distribution of students’ career networks, highlighting differences in social capital among students from different social backgrounds.

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Figure 1 shows the social network structure of students in their career development. Nodes represent students or their contacts (peers, mentors, employers, industry experts, etc.), and edges represent the connections between them. The intuitive display of the composition and structure of students’ social networks helps to explain the position of students from different social backgrounds in the network and the distribution of resources.

In network analysis, students are regarded as nodes in the network, and their professional relationships are represented by the lines between nodes. Based on the social network data provided by students, the social network structure of each student can be constructed. Network density reflects the ratio between actual connections and possible connections in a social network and measures the closeness of social resources available to students in their career development. The density of each student’s social network is calculated to analyze whether students from different social backgrounds can obtain the same network resources. A high-density social network usually means smoother information flow and more convenient resource acquisition. By comparing the network density of different groups, it is explored whether factors such as social class and race affect the density of student networks. Network centrality analysis focuses on evaluating the importance of students in their career networks. By calculating the degree centrality, closeness centrality, and betweenness centrality of nodes, students’ core position in the network and their ability to circulate information are evaluated. Degree centrality reflects the number of contacts that a student is directly connected to; closeness centrality measures the average distance between a student and other nodes; betweenness centrality shows the bridge role that a student plays in information flow. Students with high centrality are usually able to obtain more career opportunities and resource support. Through the centrality index, whether students from different social backgrounds are in a central position in the network can be analyzed, so as to obtain more career resources and opportunities.

Network density is calculated by dividing the actual number of connections in the network by the maximum possible number of connections:

$${Density}=\frac{2L}{N(N-1)}$$
(1)

Among them, L is the number of observed ties, and N is the number of nodes. Higher density indicates stronger group cohesion and resource sharing.

Centrality measures include degree (number of direct connections), closeness (average shortest path to other nodes), and betweenness (frequency of acting as a bridge between disconnected groups). These metrics are computed using social network data collected from student questionnaires, LinkedIn profiles, and alumni records, and analyzed via Gephi software to visualize and quantify career-related social capital.

In the SNA framework, social capital refers to the resources that individuals obtain from social relationships, such as information, opportunities, and support. This study focuses on the social capital that students rely on in their career development, analyzing industry connections, resource allocation, and information exchange. The SNA method is used to evaluate the industry resources that students are exposed to and their contribution to their career development, track resource flow patterns, and reveal the impact of social inequality on resource acquisition. Studies have shown that students with superior socioeconomic backgrounds tend to have access to more network resources, which is crucial for job hunting and career advancement. The network resource flow path is evaluated to study how social background affects the acquisition of career opportunities. It is found that there are significant differences in the opportunities obtained by students from different social backgrounds in the career network. Students from low socioeconomic backgrounds lack industry connections and information support in the early stage, which leads to more prominent inequality in their career development process.

Results

Innovative thinking improves the effect

This study focuses on the role of digital design education in promoting students’ innovative thinking, focusing on analyzing the changes in four dimensions: technical integration ability, interdisciplinary collaboration ability, critical thinking, and problem-solving ability. A Likert 5-point scale is used for quantitative analysis. By comparing the performance differences before and after students are exposed to digital tools (such as VR technology, artificial intelligence, etc.), the effect of this education model on stimulating students’ innovative potential is evaluated. The research results provide data support for the optimization of digital design education and a theoretical basis for the planning of students’ future career development, as shown in Fig. 2.

Fig. 2: Comparison of the effect of improving innovative thinking before and after using digital tools.
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a Mean values: Technical integration ability increased from 3.2 to 4.1; interdisciplinary collaboration ability from 3.4 to 4.2; critical thinking from 3.1 to 4.0; problem-solving ability from 3.3 to 4.0. b Standard deviations: Decreased overall, indicating more balanced improvements across groups. Data demonstrates that digital tools enhance students’ innovative thinking uniformly across diverse demographics.

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Figure 2 shows the effect of improving students’ innovative thinking ability before and after using digital tools. As can be seen from Fig. 2a, the students’ technical integration ability increases from 3.2 to 4.1; the interdisciplinary collaboration ability increases from 3.4 to 4.2; the critical thinking increases from 3.1 to 4.0; the problem-solving ability increases from 3.3 to 4.0; the standard deviation generally decreases, indicating that students’ improvement in these abilities is more balanced and consistent. This reflects that digital tools not only enhance students’ innovative ability but also achieve a more unified improvement effect in different groups. These results verify Hypothesis 1: digital design education can significantly improve students’ innovative ability and technical ability, and support Hypothesis 2: through interdisciplinary collaboration and technical integration, students’ innovative thinking has been effectively improved. In summary, the application of digital tools plays a key role in the cultivation of innovative thinking.

Path analysis results

The structural equation model is used to analyze the impact of technical training and interdisciplinary collaboration on technical ability and cognitive ability in digital design education, and to explore the path relationship between various factors, as shown in Table 8.

Table 8 Evaluation of the associations between technical ability, cognitive ability, and innovative thinking.
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The data in Table 8 shows that technical training has a significant impact on the improvement of students’ technical ability and cognitive ability, with path coefficients of 0.45 and 0.52, respectively (p < 0.001); interdisciplinary cooperation has a significant impact on the improvement of students’ technical ability and cognitive ability by 0.37 and 0.41 (p < 0.001), and both have passed the significance test. This result shows that in digital design education, technical training and interdisciplinary cooperation have a direct positive impact on students’ technical ability and cognitive ability. After controlling for extroversion and proactivity, the path coefficient from resource fairness to network density remains significant (β = 0.39, p < 0.01), indicating that social capital disparities are primarily driven by structural inequality rather than individual differences. In addition, technical ability and cognitive ability also have a significant impact on the cultivation of innovative thinking, with path coefficients of 0.49 and 0.43, respectively (p < 0.001). The comprehensive SEM model fit index (CFI = 0.92, TLI = 0.91, RMSEA = 0.06) shows that the model fit is good, which verifies the validity of the hypothesis. Therefore, digital design education effectively promotes the cultivation of students’ innovative thinking through technical training and interdisciplinary cooperation.

When examining the moderating effect of social class on the impact of digital education, the study finds that the fairness of resource allocation has significant differences for students of different social classes. For low-social-class students, when resources are equally distributed, technical training has a more significant impact on their cognitive abilities (β = 0.58, p < 0.001). For high-social-class students, interdisciplinary collaboration becomes the main factor affecting their academic development (β = 0.45, p < 0.001), and they rely more on existing social networks. This result shows that fair resource allocation can, to a certain extent, amplify the positive impact of digital education on disadvantaged groups, especially in terms of cognitive abilities, strengthening the effect of technical training, while for high-social-class students, the role of interdisciplinary collaboration and social networks is more prominent.

Results of causal analysis

To explore the relationship between career path diversity, salary gap, and social background, this study uses the SEM model to quantify the causal relationship between these factors based on students’ employment data and social background (such as class and education level), and verifies the impact of different social backgrounds on students’ career choices and salary levels. Due to the limited representation of non-White students (20%), findings on racial disparities should be interpreted cautiously. Future studies with balanced racial sampling are needed to validate these patterns, as shown in Fig. 3.

Fig. 3: Impact of different social backgrounds on career path choice and salary level.
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a Participant distribution: High, middle, and low social class groups. b Career path choices: 80% of high-class students chose design-related careers vs. lower proportions in low-class groups. c Salary levels: High-class students had higher starting salaries but similar salary growth rates. d Job change frequency: Low-class students exhibited higher turnover (25% vs. 12% in high-class groups). Results highlight social class disparities in career stability and opportunities.

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The data in Fig. 3b shows that 80% of high-social-class students tend to choose the design field as their career direction, while Fig. 3d shows that the job change rate of these students is only 12%, indicating a high level of career stability. In contrast, the proportion of low-social-class students choosing design-related careers is relatively low, and their job change rate reaches 25%, indicating that they have greater instability in their career choices. This difference reveals the significant impact of social class on career path choice. Further observation shows that graduate students have more advantages than undergraduates in choosing design-related careers, which reflects the positive impact of education on career stability. In addition, white students have significantly higher chances of choosing design careers than non-white students, revealing the potential influence of skin color in career choice. In Fig. 3d, the salary increase of the high social class is the same as that of the low social class group. However, according to Fig. 3c, the starting salary of the high social group is higher than that of the low social class. This shows that the influence of social class on salary increase is also indirect. In general, according to the analysis of these factor variables by the SEM model, it can be concluded that social class, education, and racial background have a significant impact on career path and salary, indicating that social inequality factors still play a key role in career development.

Social network analysis results

When exploring the impact of social background on students’ career networks, the study focuses on how social background differences affect students’ career resource acquisition before and after equal distribution of resources. The analysis focuses on the role of students’ education level (undergraduate and graduate), skin color characteristics (white and non-white), and social class (low, middle, and high class) in the expansion of career networks. Using the SNA method, 12 student samples are selected from different social backgrounds to compare and analyze the changes in the number of network nodes and network density before and after equal distribution of resources. Network nodes represent the number of social connections that an individual has in his or her career development, while network density reflects the closeness of these connections. This study aims to reveal the differences in how students from different social backgrounds build their career networks, as well as the specific impact of equal resource distribution on network structure, as shown in Fig. 4.

Fig. 4: Impact of different social backgrounds on career networks before and after resource allocation.
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a, b Network nodes and density by social class: Low-class students showed significant increases in network nodes (e.g., Student 1: 5 → 9) and density (e.g., non-white Student 9: 0.28 → 0.53). c, d Academic level: Graduate students maintained denser networks than undergraduates. e, f Race: Non-white groups saw substantial network density improvements post-intervention. Findings support Hypothesis 3: Fair resource distribution reduces career development gaps.

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Figure 4 shows that after the redistribution of network resources, the number and density of network nodes of low social class and non-white groups increase significantly, indicating that these groups have gained more social capital and support in their career development. In Fig. 4a, the number of network nodes of low social class student 1 increases from 5 to 9, reflecting the positive effect of resource redistribution on the expansion of his network. Figure 4f shows that the network density of non-white student 9 increases from 0.28 to 0.53. These results support the conclusion of Hypothesis 3, that is, the fair distribution of educational opportunities and network resources can effectively narrow the gap in career development. In particular, low-class students and non-white groups have gained a wider range of professional social networks through the equal distribution of educational opportunities, providing support for them to break through the limitations of social background and improve their career development opportunities. Overall, the equality of educational opportunities promotes equal opportunities for all groups in career development and effectively narrows the gap caused by social background.

Discussion

Mechanism analysis

At present, the global education system is undergoing profound changes brought about by digital transformation, especially in the field of design education. Faced with the growing demand for innovative skills in society, educational policies and practices need to pay more attention to the cultivation of innovative thinking and the fairness of resource allocation.

The research results show that within the sample size of this survey, there is a certain positive correlation between the use of digital tools and students’ abilities in technology integration, interdisciplinary collaboration, and critical thinking. Specifically, the data shows that after receiving digital design education, students’ average values of innovative thinking related indicators have increased, indicating that this teaching method may have a positive impact on the development of students’ innovative abilities (as shown in Fig. 2). After using digital tools, the students’ technical integration ability increases from 3.2 to 4.1; the interdisciplinary cooperation ability increases from 3.4 to 4.2; the critical thinking increases from 3.1 to 4.0; the standard deviation generally decreases. The data shows that after using digital tools, students from different backgrounds have improved their innovative thinking in different dimensions, and the differences between groups have narrowed. This suggests that digital tools may have alleviated the inequality of educational resources caused by differences in social backgrounds to a certain extent. It is recommended that policymakers consider promoting the application of digital tools in design education to promote the development of innovative thinking. Especially in regions with insufficient educational resources, promoting balanced distribution of digital resources can help narrow the gap in technology application and innovation capacity cultivation. However, the current distribution of educational resources in many regions is still uneven, especially in low-income regions, and the popularization of digital educational tools still faces great challenges. Therefore, it is imperative to increase investment in these regions and ensure educational equity, especially in digital education.

Figures 3 and 4 reveal that social class, education, and race significantly influence students’ career development. Students from higher social classes tend to choose design jobs more often, enjoying greater career stability and lower job-changing frequencies. Conversely, students from lower social classes are less likely to pursue design careers and experience higher job turnover. This demonstrates the strong impact of social background on career choice and stability. Education and race also affect career decisions. Graduate students and white students have more opportunities in design careers and higher starting salaries compared to undergraduates and non-white students. This highlights the role of social inequality in shaping career paths. Despite progress in design education and innovative thinking, social class and racial disparities still significantly influence students’ career trajectories at the outset of their careers. It is recommended that education policy makers give priority to improving their access to career network resources through institutional arrangements, such as setting up vocational internship quotas for disadvantaged groups, implementing mentor matching programs, and strengthening school–enterprise cooperation. These strategies have shown certain positive effects in the intervention model of this study, but they still need to be flexibly adjusted in combination with the local education ecology and resource allocation to ensure the effectiveness of policy implementation.

The equal distribution of educational opportunities plays a key role in reducing the impact of social background on career development. Through the SNA analysis of Fig. 4, the density and number of nodes of the career network of low social class and non-white groups increase significantly after obtaining equal educational opportunities. For example, after receiving educational equity support, the network nodes of low-class students increase from 5 to 9, and the network density of non-white student 9 increases from 0.28 to 0.53. This shows that the equal distribution of network resources not only improves the innovation ability of these groups but also helps them build a wider professional social network, thereby improving their career development opportunities. This result suggests that policymakers should promote the fair distribution of educational resources, especially in the construction of career development support and social network resources. By providing more career support, social network opportunities, and industry connections for low-class and non-white groups, the negative impact of social background factors on their career development can be effectively reduced. The analysis of the impact of network structure on career development outcomes shows that network density significantly affects career development opportunities, especially for disadvantaged groups. For example, after fair resource distribution (Fig. 4b), the network density of low-income students increases from 0.28 to 0.53, and the job recommendation rate increases by 40% (Table 5). This shows that dense networks can enhance information flow and mentor guidance. The results show that social class and race significantly predict career development outcomes (β = 0.39, p < 0.01; Table 8). This is consistent with the pattern of occupational stratification caused by unequal resource distribution worldwide. Fair interventions alleviate these gaps by increasing the network density of marginalized groups (Fig. 4f), highlighting the feasibility of policy-driven solutions. Therefore, policies should focus on providing more career internship opportunities, mentor guidance, and industry cooperation projects to enhance the social capital and career opportunities of disadvantaged groups.

While personal traits (such as proactiveness) may contribute to network building, the analysis suggests that equitable resource allocation is the primary factor in bridging career gaps, particularly for disadvantaged groups whose initial networks are constrained by structural barriers.

Digital education has a “double-edged sword” effect: without fair intervention, technological advantages may be monopolized by high-class students. As shown in Fig. 4, before resource redistribution, the network density of low-class students (0.28) is significantly lower than that of high-class students (0.61), resulting in limited career opportunities; however, through fair policies (such as mentor matching programs), the network density of low-class students increases to 0.53, close to the level of high-class students. This shows that technological empowerment needs to be promoted simultaneously with fair mechanisms to avoid exacerbating social divisions.

Theoretical contributions

This study has certain theoretical contributions in the intersection of design education and social equity: (1) it organically combines theories such as innovation diffusion, social cognition, and educational equity to establish a comprehensive analysis framework of “technology-society-equity”, providing systematic theoretical support for the cultivation of digital design education innovation capabilities and career development. Based on revealing the mechanism of digital tools in improving college students’ creativity, this study explores the mechanism of social context (social class, race, education level, etc.) on college students’ employment opportunities, and expands the scope of application of traditional education theories in the digital environment. (2) This study combines SEM and SNA to systematically reveal the mechanism of digital technology on the cultivation of college students’ innovative ability, and analyzes the constraint effect of social context on the development of college students’ innovative ability at the social network level. This study provides new ideas and methods for educational research, especially in exploring the interaction mechanism between structural inequality and personal ability development. (3) The results show that the rational allocation of education and Internet resources can effectively reduce the impact of different social classes, nationalities, and other factors on career development, and apply them to education and teaching. The results provide empirical evidence for educational equity research and also put forward the important role of policy measures in bridging the digital divide and promoting social capital of disadvantaged groups. This study enriches and improves the theoretical system of cultivating innovative ability in design education to a certain extent, helps to deepen the understanding of the intrinsic connection between educational equity and career development, and provides a theoretical basis for subsequent related research.

Management implications

To promote the efficient development of digital design education and achieve a balance between educational equity and career development, this study proposes corresponding countermeasures and suggestions to education authorities, policy makers, and practitioners from four aspects.

Regarding the allocation of teaching resources, this study finds that after using digital tools, students’ technical integration ability, interdisciplinary cooperation ability, and critical thinking have been significantly improved. To this end, education authorities should focus on the promotion and popularization of digital teaching methods, especially in places with fewer educational resources. Specifically, they can set up special funds, purchase hardware equipment, and develop localized digital courses to improve the level of design teaching in low-income areas. On this basis, a cross-regional teaching resource sharing platform can be built to promote schools and enterprises to jointly develop high-quality digital resources.

After considering the role of social class and racial differences in career development, this study finds that the distribution of resources in social networks among individuals is very important. To achieve this goal, schools should actively establish a diversified employment support system, including establishing a mentor-apprentice pairing mechanism, developing employment counseling courses, and organizing school-enterprise cooperation practice programs to help disadvantaged college students expand their professional networks and social capital. In addition, universities should cooperate with industry associations, design agencies, etc. to establish special training programs or scholarships for students with lower social status or non-white students to improve their employment competitiveness.

Based on SNA analysis, it is found that the greater the density of social networks, the greater the employment opportunities. To this end, it is necessary to strengthen the construction of student social networks, such as conducting interdisciplinary research, design competitions, and holding industry lectures, so as to enhance communication and cooperation among students. At the same time, teachers should also be encouraged to participate in industry practices, expand exchanges with the outside world, and give students the opportunity to get in touch with more design fields.

Digital education has the potential to improve students’ technical and innovative capabilities, but the distribution of its benefits may be affected by structural factors. Without targeted intervention measures, the imbalance of educational resources and network support may lead to higher-income students further consolidating their advantageous position. Therefore, educational practice should focus on the construction of fair access mechanisms to avoid exacerbating social inequality.

Limitations and future research directions

This study uses various data collection methods such as questionnaire surveys, in-depth interviews, and career trajectory tracking, which, to some extent, ensure the comprehensiveness and reliability of the data, but also have certain limitations. Firstly, the data collection adopts a combination of questionnaire surveys and in-depth interviews, which may result in subjective biases and social expectation biases of the respondents, affecting the objectivity of the results. Secondly, the samples mainly come from specific regions and universities, and do not cover students with diverse cultural and multi-level educational backgrounds, which limits the external validity of the research conclusions. Thirdly, the time span of career development data is relatively short, making it difficult to capture the long-term cumulative effects of educational inequality. In addition, the study does not include potential moderating variables such as individual traits of students (such as personality, motivation, family support), which may play an important role in cultivating innovative thinking and career development.

In view of the above limitations, future research should consider the following aspects: first, the sample range is expanded to cover more types, levels, and diverse students from regional cultural backgrounds; second, a long-term tracking research design is adopted to gain a deeper understanding of the lasting impact of digital design education; third, more objective evaluation indicators are applied, such as teacher evaluation or design project results, to cross-validate the validity of subjective data; fourth, the role of factors such as students’ personal characteristics, learning motivation, and family background in the education process is further explored, and attention is paid to the promotion of interdisciplinary collaboration and technological innovation on education quality, so as to provide more operational theoretical support and practical guidance for optimizing the digital design education model.

Conclusions

This study uses an empirical method combining SEM and SNA to analyze the impact of digital design education on the cultivation of students’ innovative thinking and explores the effect of social inequality factors on students’ career development. Based on the analysis of SEM, digital tools and interdisciplinary collaboration statistically show a positive impact on students’ technology integration ability (path coefficient = 0.49, p < 0.001) and innovative thinking (path coefficient = 0.43, p < 0.001), supporting Hypothesis 1 and Hypothesis 2. At the same time, in the context of social inequality, the balanced distribution of educational opportunities and network resources effectively reduces the negative impact of factors such as class and race on career development, verifying Hypothesis 3. Although the study provides solid evidence, it has certain limitations because the sample is limited to a specific region and lacks long-term career development data. Future research should expand the sample range, further examine the long-term impact of digital education on innovative thinking, and deeply analyze the continuous effect of fair distribution of network resources in different career paths.