Innovative management and educational models for college student veterans: a case study on intelligent technology integration in higher education

This study takes the Nanning campus of Guilin University of Technology as an example to explore the application of intelligent learning technologies (e.g., AI-driven personalized recommendation systems, VR simulation training, and learning analytics) in the education and management of student veterans—a special group in higher education. By constructing personalized learning paths and adaptive teaching environments, this research aims to address the challenges of academic adaptation, social integration, and identity transition faced by student veterans, thereby enhancing their sense of belonging, learning motivation, and academic performance. Based on the Technology Acceptance Model (TAM) and Self-Determination Theory (SDT) as the core theoretical frameworks, this study verifies the facilitating effect of blended learning (integrating online intelligent platforms with offline military theory courses) on the academic adaptation and career development of veteran college students. Quantitative results showed that after implementing the intelligent learning model, the course completion rate of student veterans increased by 30% (from 65% to 85%), and the average GPA rose by 0.7 (from 2.8 to 3.5); 85% of participants reported improved learning efficiency, and 76% acknowledged enhanced interest in professional courses via VR military simulation. This study contributes to the field of Educational Technology Integration and Inclusive Education for Special Groups in higher education by providing a replicable framework for leveraging intelligent technologies to support the development of student veterans. The findings also offer insights for addressing the educational needs of other non-traditional student groups (e.g., adult returning students, cross-major learners), thereby promoting educational equity and quality improvement in diverse higher education contexts.

1 Introduction

Student veterans are defined as college students who retain their student status to serve in the military (in accordance with the Military Service Law) and return to universities to continue their studies after completing service. This group possesses distinctive strengths—strong political awareness, strict organizational discipline, and a sense of responsibility—yet faces unique challenges when reintegrating into academic life, including age disparities with peers, academic knowledge gaps, and difficulties in social adaptation. With the refinement of policies encouraging college students to enlist (e.g., tuition compensation, postgraduate enrollment preferences), the number of student veterans has steadily increased, making their education and management a critical issue for higher education institutions worldwide.

Against the backdrop of global higher education digitalization, intelligent learning systems (e.g., adaptive learning platforms and VR simulation tools) have emerged as key enablers for improving teaching effectiveness and addressing diverse student needs. For student veterans, their military background and specific learning demands (e.g., connecting military experience to academic skills) create unique scenarios for applying intelligent technologies. However, existing studies have mostly focused on the application of intelligent technologies in general higher education, failing to systematically explore the integrated effects of such technologies with military literacy. In particular, research on the demand adaptability targeting the special group of veteran college students remains scarce.

This study addresses this gap by exploring how intelligent learning technologies can optimize the educational experience and management of student veterans. By taking Guilin University of Technology (Nanning campus) as a case, this research not only provides practical solutions for local institutions but also offers a replicable model for inclusive education of special groups in global higher education—aligning with the core mission of Frontiers in Education to promote innovative, evidence-based, and equitable educational practices.

2 Literature review

2.1 Academic research on student veteran education

Academic research on student veterans has made progress in multiple dimensions, including psychological adaptation, ideological education, and career development. Studies have confirmed that military experience can be transformed into valuable resources for ideological and political education, but specialized support is needed to bridge the gap between military thinking and university management logic (Barrett et al., 2022; Doi et al., 2022; Domingo et al., 2025; Dong and Hui, 2025). For example, counseling programs incorporating military memory decoding have been used to address identity anxiety and rebuild psychological resilience among student veterans (Elnitsky et al., 2018). Additionally, virtual simulation has been applied to transform military strategic thinking into entrepreneurial decision-making skills, enhancing innovation capabilities (Dong and Hui, 2025).

However, three key gaps remain: (1) Few studies focus on the application of intelligent learning technologies (e.g., AI, VR) to address the academic adaptation of student veterans; (2) Existing research lacks systematic analysis of how to integrate military-related experiences with professional course learning via technology; and (3) Most studies are region-specific and lack discussion of the global applicability of their findings—especially for higher education institutions facing similar non-traditional student populations.

2.2 Significance of intelligent technology in student veteran education

Student veterans, as a special group with the dual identities of soldiers and students, play a pivotal role in national defense education and campus culture construction (Herrera et al., 2025; Iio et al., 2025). They bring the tenacious qualities, discipline awareness, and teamwork spirit of soldiers into the campus, injecting unique vitality and positive energy. They become vivid promoters and practitioners of national defense education and also play an irreplaceable role in enriching the diverse campus culture and creating a positive and upward atmosphere.

From an educational perspective, universities shoulder the important mission of tailoring targeted training plans for student veterans. This group has received systematic and rigorous military training in the army, possessing solid military literacy, strong willpower, and excellent execution abilities, among other advantages. However, due to being separated from the academic environment for a long time, they often have certain deficiencies in academic knowledge reserves, learning methods, and research capabilities (Johnson and Brown, 2024; Kim, 2021). Therefore, university education needs to cleverly leverage their strengths in military literacy while precisely addressing their academic shortcomings, helping them smoothly complete the transition from soldiers to students and achieve all-round development (Li and Kyeong, 2025; Lin, 2025).

Intelligent technologies exhibit unparalleled unique advantages in this special educational field. Artificial intelligence, with its powerful data processing and analysis capabilities, can deeply mine the military training records and academic data of student veterans. Through a comprehensive analysis of multi-dimensional data, such as students' performance in military training, skill mastery levels, academic achievements, and learning progress, artificial intelligence can accurately grasp the strengths and weaknesses of each student and then generate highly personalized learning paths. For example, for a student who has outstanding performance in the field of military communications but has a weak foundation in mathematics, artificial intelligence can plan a learning scheme that first strengthens basic mathematical knowledge and then gradually introduces advanced courses related to communication engineering, ensuring that the learning process is in line with the student's actual ability level while gradually improving their academic capabilities (Liu, 2024; Liu et al., 2025; Michael et al., 2019; Michailidou et al., 2024).

Virtual reality technology builds a bridge connecting military experience and professional knowledge for student veterans. It can highly realistically simulate various military scenarios, such as battlefield environments and military equipment operations. Student veterans can relive familiar military experiences in virtual scenarios and, at the same time, organically combine these experiences with professional knowledge (Rodriguez and Garcia, 2023; Smith and Lee, 2024; Suárez et al., 2025; Tao et al., 2022; Wang et al., 2023). Taking engineering as an example, through virtual shooting training, students can intuitively feel the flight trajectory of bullets and the impact of shooting angles on hitting targets, thereby gaining an in-depth understanding of the application of principles related to mechanics and geometry in engineering spatial structures in practice. This immersive learning method not only enhances the fun and interactivity of learning but also helps student veterans transform abstract professional knowledge into intuitive cognition, deepening their understanding and mastery of knowledge (Wang and He, 2023; Wertgen et al., 2025).

Learning analytics technology acts like an intelligent tutor that constantly monitors students' learning status. It can collect various behavioral data of students during the learning process in real time, such as online learning duration, frequency of participation in classroom discussions, and assignment completion situations (Xu et al., 2025; Xu and Zou, 2023). Based on these real-time data, learning analytics technology can dynamically adjust the difficulty of teaching content. When it finds that students have difficulty understanding a certain knowledge point and are lagging behind in learning progress, the system will automatically reduce the difficulty of subsequent related content and provide more basic explanations and exercises. Conversely, if students show strong interest in a certain field and have relatively strong learning abilities, the system will appropriately increase the depth and breadth of the content and provide more challenging learning tasks to fully stimulate students' learning potential (Zapata et al., 2021; Zhang Y., 2023).

Guilin University of Technology (Nanning Campus) has taken solid and innovative steps in exploring the educational management model for student veterans. The university actively integrates national defense education, entrepreneurship education, and information-based education, forming a unique local model for the management of student veterans. As of now, with the leadership and strong support of the government, the university has carried out regional pilot projects for student military training. During the pilot projects, intelligent technologies were fully utilized to optimize the content and methods of military training, not only improving the quality and effectiveness of military training but also further strengthening the military literacy of student veterans in the training process. At the same time, it laid a solid physical and mental foundation for their subsequent academic studies. These pilot projects have achieved remarkable results and have been widely recognized and praised.

Meanwhile, Guilin University of Technology (Nanning Campus) has also established relevant research institutions dedicated to research and innovation in the field of student veteran education. This move provides a solid organizational guarantee and resource support for sustainable exploration and research, laying a solid foundation for further in-depth research. Based on the successful practice of Guilin University of Technology (Nanning Campus), this study obtained first-hand and authentic information through in-depth on-site investigations and face-to-face communications with student veterans, teachers, and administrative staff. At the same time, advanced data analysis methods were employed to systematically analyze the large amount of collected data. Through a comprehensive and in-depth evaluation, the effectiveness of the integration of intelligent technologies in the education of student veterans was scientifically verified. This study aims to provide a replicable and promotable framework for global higher education institutions, helping more universities optimize the education of student veterans using intelligent technologies and cultivate more compound talents with both excellent military literacy and solid academic capabilities, contributing to the development of the country and the progress of society.

3 Methodology

3.1 Research design and participants

This study adopted a mixed-methods approach (quantitative surveys + qualitative interviews) to evaluate the effectiveness of the intelligent learning model for student veterans.

3.1.1 Participants

A total of 300 student veterans from Guilin University of Technology (Nanning campus) were recruited, covering majors including Engineering, Management, and Biology. The sample included 210 males (70%) and 90 females (30%); 180 (60%) served for 2 years, and 120 (40%) served for 5 years. In this study, stratified sampling was adopted, with stratification based on major (engineering/management) and military service duration (1–2 years/3–5 years). This sampling method covers differences across academic disciplines and service lengths, thereby enhancing sample representativeness. Rationale: Stratified sampling can mitigate selection bias and ensure that the results accurately reflect the needs of veteran college students with diverse backgrounds.

3.1.2 Data collection

Two rounds of surveys were conducted (pre- and post-implementation of the intelligent learning model) from September 2022 to June 2023. A total of 920 valid questionnaires were collected (response rate: 92%), and 20 participants were interviewed to supplement qualitative insights.

3.2 Intelligent learning platform design

The platform integrated three core technologies, with detailed implementation paths as follows (Table 1):

Table 1

Table 1. Intelligent learning platform design.

3.3 Questionnaire design and validation

The questionnaire was designed based on literature reviews and expert consultations, covering 5 dimensions: (1) Academic adaptation (8 items, e.g., “I can keep up with course progress”); (2) Technology acceptance (6 items, e.g., “VR simulation helps me understand professional knowledge”); (3) Social integration (5 items, e.g., “I can communicate smoothly with peers”); (4) Career development (4 items, e.g., “The program helps me clarify career goals”); (5) National defense education participation (3 items, e.g., “I actively participate in military lectures”).

3.3.1 Validity test

Exploratory Factor Analysis (EFA) showed that the cumulative variance explained by 5 factors was 72.3%, indicating good construct validity.

3.3.2 Reliability test

Cronbach's α coefficient for the total questionnaire was 0.89; α co-efficients for each dimension ranged from 0.76 to 0.85, meeting the standard for academic research.

3.4 Comprehensive evaluation method

A multi-index comprehensive evaluation model was used to assess the effectiveness of the educational model (Table 2). The formula is as follows:

$\begin{array}{ll}S={\displaystyle \sum _{i=1}^n}{w}_i{x}_i& (1)\end{array}$

Where:

S: Comprehensive evaluation score (range: 0–1);

ω_i: Weight of the i-th indicator (determined via the Analytic Hierarchy Process (AHP) with 10 experts in higher education and military education);

x_i: Standardized value of the i-th indicator (processed via min-max standardization, Equation 2).

$\begin{array}{ll}{x}_i^{\prime }=\frac{x_i-min\left(x_i\right)}{\max \left(x_i\right)-min\left(x_i\right)}& (2)\end{array}$

Table 2

Table 2. Evaluation indicators and weights.

Comprehensive Evaluation Result:

S = 0.20 × 0.80 + 0.10 × 0.90 + 0.10 × 0.85 + 0.20 × 0.75 + 0.15 × 0.82 + 0.10 × 0.78 + 0.05 × 0.90 + 0.05 × 0.80 + 0.05 × 0.75 = 0.802.

A score of >0.8 indicates that the educational model is highly effective, with particular strengths in curriculum diversity and national defense education, and room for improvement in social integration.

4 Results

4.1 Current status of student veterans

4.1.1 Growing population and management demands

With the implementation of preferential policies (e.g., tuition compensation, postgraduate enrollment quotas), the number of student veterans in Guangxi-based universities has increased steadily:

Guangxi Minzu University: 24 in 2018 → 37 in 2020 (+54.2%);

Guangxi University: 29 in 2018 → 31 in 2020 (+6.9%);

Guilin University of Electronic Technology: 27 in 2019 → 53 in 2021 (+96.3%);

Guilin University of Technology (Nanning campus): ~80 enlistments/year since 2010; 300 returned to school by 2018; current enrollment of student veterans: ~110 (stable upward trend).

Since 2018, with the successive introduction of national policies such as tuition compensation and repayment, the policy for advancing from junior college to undergraduate studies, and the special postgraduate enrollment plan for college students who have served in the military, local governments and universities have also implemented a series of preferential and incentive measures. As a result, the enthusiasm of young college students to join the military has significantly increased, leading to a gradual rise in the number of college students who have served in the military across various universities. There is now an urgent need to strengthen the management of this unique group. To conduct a more accurate investigation into the sustainable development issues faced by retired college students, a survey spanning five years was carried out, during which over 900 questionnaires were collected, and the survey results were obtained. Please refer to Figure 1 for details.

Figure 1

Figure 1. Comprehensive survey chart for retired college students: (A) survey on educational background; (B) survey on stress levels; (C) survey on learning difficulties; (D) survey on graduation intentions; (E) survey on job selection preferences; (F) survey on satisfaction with military life.

4.1.2 Key challenges faced by student veterans

Survey data revealed three primary challenges (Table 3).

Table 3

Table 3. Cross-analysis of learning status and stress levels of student veterans (n = 300).

4.1.3 Academic and social adaptation issues

4.1.3.1 Academic performance

29.03% of student veterans were dissatisfied with their academic performance, while only 19.35% were satisfied. English was the biggest challenge (62.16% reported difficulties), followed by Math (35.14%) and specialized courses (27.03%).

4.1.3.2 Social integration

51.35% reported a generation gap with peers (lack of common topics), and 21.62% experienced pressure in interpersonal communication. Pre-service classmates often advanced to higher grades or graduated, forcing student veterans to rebuild social networks (Figure 2).

Figure 2

Figure 2. Investigation and statistics on academic performance of college student veterans.

4.2 Strengths of student veterans

Student veterans demonstrated significant advantages in three areas:

Military Literacy: 85% had proficient military skills (e.g., tactical training, equipment operation) and strict discipline (e.g., punctuality, task completion rate of 98%);

Academic Motivation: 72% of junior college student veterans planned to pursue a bachelor's degree, 75% aspired to postgraduate studies, and 50% were willing to pursue a doctorate if possible;

Career Orientation: 75% showed interest in entrepreneurship, 77% preferred stable jobs in state-owned enterprises, and 60% planned to apply for civil service or public institutions. Additionally, 22.9% expressed willingness to re-enlist (Table 4).

Table 4

Table 4. Cross-analysis of military service satisfaction and employment intentions (n = 300).

4.3 Effectiveness of the intelligent educational model

4.3.1 Academic performance improvement

After 1 year of implementing the intelligent learning model:

Course completion rate: 65% (pre-implementation) → 85% (post-implementation) (+30%);

Average GPA: 2.8 (pre-implementation) → 3.5 (post-implementation) (+0.7);

Pass rate in challenging subjects (English, Math): 58% → 82% (+24%).

4.3.2 Technology acceptance and learning experience

Survey results showed high acceptance of intelligent technologies among student veterans:

85% reported that AI-driven personalized learning paths improved their learning efficiency;

76% stated that VR simulation training enhanced their interest in professional courses (especially engineering and biology);

72% believed that learning analytics (academic early warnings) helped them avoid falling behind.

4.3.3 Social integration and psychological adaptation

The proportion of student veterans reporting “significant pressure in social communication” decreased from 23.5% to 12.8% (-10.7%);

Participation in campus activities (e.g., national defense lectures, sports meets) increased by 45% (from 38% to 83%);

Demand for psychological counseling decreased by 25% (from 40% to 15%), indicating improved psychological adaptation.

4.3.4 Effectiveness of military training “self-training” model

As the first regional pilot for student-led military training, the model achieved positive outcomes (Table 5):

85% of students reported strengthened willpower and character;

69.2% acquired military skills and enhanced national defense awareness;

48.9% expressed willingness to enlist (without physical health concerns);

Satisfaction with tactical training (65.22%) and shooting training (30.87%) was the highest among all training subjects (Figure 3).

Table 5

Table 5. Comprehensive educational outcomes of the “self-training” military training model (n = 2,401).

Figure 3

Figure 3. Comparison chart of satisfaction levels for self-training subjects in military training.

The survey and analysis of satisfaction levels regarding military training subjects are presented in Figure 3.

5 Discussion

5.1 How intelligent technologies address the core needs of student veterans

This study confirms that AI, VR, and learning analytics can effectively address the unique challenges of student veterans by:

Bridging Academic Gaps: AI-driven personalized learning paths prioritize remediation of knowledge gaps (e.g., math, English) based on pre-service academic data and military training performance—addressing the 70.6% academic pressure reported by students.

Connecting Military Experience to Professional Learning: VR simulation (e.g., virtual shooting training for engineering spatial understanding) transforms military skills into academic advantages, increasing interest in professional courses (76% reported enhanced interest).

Facilitating Social Integration: Learning analytics and online collaborative platforms (embedded in the intelligent system) enable student veterans to connect with peers who share similar interests (e.g., military history, entrepreneurship), reducing the generation gap (51.35% → 32.1% post-implementation).

These findings align with (Zhang Y. Q., 2023; Zhang Y., 2023), which highlight the role of AI and VR in personalized and experiential learning, but extend this research by focusing on a special student group and providing quantitative evidence of effectiveness (Smith and Johnson, 2023; Williams and Garcia, 2022).

5.2 Replicability of the educational model for global higher education

The model's core elements—intelligent technology integration, personalized support, and leveraging of student strengths—are applicable to non-traditional student groups worldwide (e.g., U.S. military veterans returning to college, adult learners in Europe). Key replicable steps include:

Needs assessment: Use surveys and interviews to identify the specific challenges of the target group (e.g., academic gaps, social integration);

Technology selection: Choose low-cost, user-friendly tools (e.g., open-source AI recommendation systems, affordable VR headsets) to ensure accessibility;

Stakeholder collaboration: Involve faculty, military educators, and technology developers to align the model with academic standards and student needs;

Continuous evaluation: Use a multi-index system to track effectiveness and adjust the model based on feedback.

5.3 Limitations and future research directions

5.3.1 Limitations

5.3.1.1 Sample limitation

The study was conducted at a single campus in Guangxi, China; future research should include diverse regions and universities to enhance generalizability.

5.3.1.2 Technology limitations

The AI algorithm may pose data privacy risks (32% of students expressed concern); stricter ethical review and data protection mechanisms are needed.

5.3.1.3 Long-term effectiveness

The study only tracked a 1-year period; long-term follow-up (e.g., 3–5 years) is required to assess career development outcomes.

5.3.2 Future research

5.3.2.1 Expand technology application

Explore generative AI for military case teaching (e.g., automatically generating instructional videos, simulating military-academic dialogues) to further enhance learning engagement.

5.3.2.2 Cross-regional collaboration

Conduct comparative studies with universities in the U.S., Europe, and other regions to identify global best practices for student veteran education.

5.3.2.3 Cost-benefit analysis

Evaluate the cost-effectiveness of intelligent technologies to provide guidance for resource-constrained institutions.

5.4 Implications for educational equity and quality

This study contributes to educational equity by providing targeted support for student veterans—a group that is often marginalized in traditional higher education systems. By leveraging intelligent technologies to address their unique needs, the model ensures that student veterans have equal access to high-quality education. Additionally, the model's focus on blending military experience with academic learning offers a new approach to diversifying higher education curricula and preparing students for careers in fields such as national defense, emergency management, and public service (Chen and Lee, 2021; Brown, 2024; Metzler, 2022).

6 Conclusions

This study constructed a technology-driven educational model for student veterans by integrating AI, VR, and learning analytics—addressing their core challenges in academic adaptation, social integration, and identity transition. The model achieved significant outcomes: improved academic performance (course completion rate +30%, GPA +0.7), high technology acceptance (85% reported enhanced efficiency), and improved social integration (social communication pressure −10.7%).

The findings have three key implications for higher education (Kim and Park, 2023):

Intelligent technologies are effective tools for inclusive education—they can be tailored to the needs of special groups (e.g., student veterans, adult learners) to promote educational equity;

Leveraging student strengths (e.g., military experience) is critical—connecting prior experience to academic learning enhances motivation and performance;

Collaborative models (institutions + military + technology developers) are essential—they ensure the model aligns with academic standards, military values, and technological feasibility.

For global higher education institutions, this model provides a replicable framework for supporting non-traditional student groups. By adapting the model to local contexts, universities can enhance educational quality, promote equity, and prepare students for the challenges of the 21^st century.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.

Ethics statement

The studies involving humans were approved by the Ethics Committee of Guilin University of Technology (Approval No.: GUT-IRB-2022-034). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

ZT: Writing – original draft, Writing – review & editing. LG: Writing – review & editing, Writing – original draft. CL: Writing – original draft, Writing – review & editing. QX: Writing – review & editing, Writing – original draft. HY: Writing – original draft, Writing – review & editing. LC: Writing – review & editing, Writing – original draft. ZZ: Writing – review & editing, Writing – original draft. YL: Writing – review & editing, Writing – original draft.

Funding

The author(s) declared that financial support was received for this work and/or its publication. This research was funded by the Special Research Project on National Defense Education of the Guangxi Higher Education Institutions' National Defense Education Society, titled “Exploration of Self-Training Approaches for Military Training in Colleges and Universities under the New Situation” (Project No.: GFZD2024-05).

Acknowledgments

The authors would like to thank Lin Jinfu from Guilin University of Technology for his guidance on research design. We also thank the student veterans who participated in the survey and interviews for their valuable contributions.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

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