Design of an Immersive Basketball Tactical Training System Based on Digital Twins and Federated Learning

Abstract

To address the challenges of dynamic adversarial scenario modeling distortion, insufficient cross-institutional data privacy protection, and simplistic evaluation systems in collegiate basketball tactical education, this study proposes and validates an immersive instructional system integrating digital twin and federated learning technologies. The four-tier architecture (sensing layer, digital twin layer, federated layer, and interaction layer) synthesizes multimodal data (motion trajectories and physiological signals) with Multi-Agent Reinforcement Learning (MARL) to enable virtual–physical integrated tactical simulation and real-time error correction. Experimental results demonstrate that the experimental group achieved 35.2% higher tactical execution accuracy (TEA) (p < 0.01), 1.8 s faster decision making (p < 0.05), and 47% improved team coordination efficiency compared to the controls. The hierarchical federated learning framework (trajectory ε = 0.8; physiology ε = 0.3) maintained model precision loss at 2.4% while optimizing communication efficiency by 23%, ensuring privacy preservation. A novel three-dimensional “Skill–Creativity–Load” evaluation system revealed a 22% increase in unconventional tactical applications (p = 0.013) through the Tactical Creativity Index (TCI). By implementing lightweight federated architecture with dynamic cognitive offloading mechanisms, the system enables resource-constrained institutions to achieve 87% of the pedagogical effectiveness observed in elite programs, offering an innovative solution to reconcile educational equity with technological ethics. Future research should focus on long-term skill transfer, multimodal adaptive learning, and ethical framework development to advance intelligent sports education from efficiency-oriented paradigms to competency-based transformation.

1. Introduction

1.1. Research Context and Challenges

In the context of the global digital transformation in education, the application of intelligent technologies is reshaping various aspects of education. A careful analysis of the nature of learning tasks, combined with the characteristics of these technologies, is equally applicable to physical education. Research shows that image- and video-based teaching methods significantly enhance students’ athletic performance and interest in learning in university physical education programs [1]. Educators need to understand how to find, access, and utilize these educational technologies [2].

Current challenges in tactical teaching for university basketball include the complexity of teaching strategies and the need for effective training methods. Virtual reality technology can help students better understand and execute complex tactics by creating immersive training environments. This technology not only simulates dynamic confrontation scenarios in real games but also analyzes players’ running trajectories and tactical execution using multi-camera tracking systems, thereby improving the effectiveness of training [3]. Furthermore, the Tactical Game Approach (TGA) is proven to enhance students’ physical fitness and game performance [4]. Therefore, designing specialized tactical knowledge and cognitive skill training programs for basketball players may be beneficial [5].

Although the applications of digital twin technology and federated learning frameworks have been widely studied in industrial and privacy protection fields, there is still a significant research gap in their use within physical education. In the industrial sector, digital twin technology is primarily applied to optimize production processes and improve efficiency by creating virtual models of physical objects [6]. Meanwhile, federated learning frameworks are widely used for privacy protection, especially in the processing of medical data, by allowing model training without sharing data and, thus, safeguarding patient privacy [7,8].

In physical education, digital twin technology can be employed to simulate athletes’ training processes, provide personalized training plans, and monitor their performance in real time. Similarly, federated learning frameworks hold potential in physical education, particularly for providing personalized learning experiences while ensuring privacy protection [9]. This study aims to address three core issues: how to achieve high-fidelity dynamic tactical simulations; how to ensure cross-institutional data collaboration while protecting privacy; and how to establish a multidimensional competency evaluation framework.

1.2. Proposed Solutions and Contributions

We propose a four-tier “Perception–Digital Twin–Federated Learning–Interaction” framework for application in Multi-Agent Reinforcement Learning (MARL). The application of MARL in complex environments has made significant progress. For instance, in 3D multiplayer games, researchers have achieved human-level performance using MARL [10]. Additionally, the KnowRU method has realized knowledge reuse through knowledge distillation in MARL, significantly reducing training time and improving agent performance [11]. In heterogeneous MARL, the Hierarchical Attention Master–Slave (HAMS) architecture optimizes communication and decision making between agents, significantly enhancing performance in complex tasks [12].

Federated learning, an emerging machine learning method, has gained attention due to its privacy-preserving nature. One of its main advantages is that it allows data to remain on local devices rather than being centralized on a server for processing [13], effectively addressing the issue of data silos. When designing federated learning frameworks, a hierarchical structure can further help in understanding and analyzing model performance, especially in high-dimensional-to-high-dimensional problems, where clear instance groups or categories are typically not available for study [14].

To assess and optimize the effectiveness of this hierarchical federated learning framework, we propose a “Three-Dimensional Evaluation Model”, which includes the Skill–Creativity–Load model. This multidimensional evaluation approach helps educators and researchers gain a comprehensive understanding of the various factors involved in the learning process, thus enabling the design of more effective learning paths and strategies [15]. The innovations in this research are as follows:

• Theoretical innovation: expanding embodied cognition theory through the concept of “Dynamic Educational Digital Twins”;
• Practical innovation: enabling resource-limited institutions to achieve 87% of the performance of top-tier programs, thus promoting educational equity;
• Methodological innovation: developing Spatiotemporal Federated Alignment (ST-FedAlign) and the Tactical Creativity Index (TCI), which establish a new paradigm for intelligent education research.

1.3. Paper Organization

The remainder of this paper is structured as follows: Section 2 critically reviews limitations in digital twin educational adaptation and federated learning applications. Section 3 details the system architecture and hybrid experimental design. Section 4 validates technical efficacy and pedagogical improvements through multidimensional metrics. Section 5 discusses human–AI collaboration boundaries and future optimization pathways. By fostering technology–education–ethics tripartite discourse, this work provides a systematic reference for theoretical advancement and ecological implementation of intelligent sports education.

2. Materials and Methods

2.1. Data Collection

2.1.1. Collection Protocol

Ethical compliance: this study was approved by the university IRB (No. IRB-2024-EDU-015) with informed consent forms and anonymized data (student IDs/names were replaced with random codes).

Multi-phase collection:

• Baseline data: these included pre-experiment FIBA basketball skill test scores and initial tactical execution accuracy (TEA) rates.
• Training data:
  • Experimental group: motion trajectories (10 Hz) and physiological signals (130 Hz) were recorded via a VR system.
  • Control group: on-court training was tracked using the SportVU system.
  • The 8-week intervention comprised 12 sessions (90 min each, 2×/week) following FIBA’s tactical progression: weeks 1–2 (man-to-man defense), 3–4 (zone defense), 5–6 (pick-and-roll variations), and 7–8 (full-court press).
• Post-test data: tactical application accuracy, team efficiency index, and NASA-TLX scores were collected 8 weeks post-intervention.

This study follows the guidelines of the Declaration of Helsinki and has been approved by the Ethics Committee of Beijing University of Posts and Telecommunications (No. IRB-2024-EDU-015).

2.1.2. Instruments

Multimodal devices:

• Motion capture: this was conducted using the Azure Kinect DK (Microsoft, Redmond, WA, USA; 4 units/court) + UWB positioning (Pozyx, Ghent, Belgium; accuracy ±10 cm).
• Physiological monitoring: this was conducted using a Polar H10 chest strap (Polar Electro, Kempele, Finland; HRV) + Myo armband (Thalmic Labs, Kitchener, ON, Canada; sEMG).
  Questionnaires:
  • NASA-TLX: this employed a 6-dimension, 7-point Likert scale (Cronbach’s α = 0.85).
  • Semi-structured teacher interviews: these covered system usability, pedagogical impact, and improvement suggestions.
  Incentives:
  • Participation rewards: we provided VR training credits (value of CNY 200) for the experimental group.
  • Academic credit: we provided 10% course grade weighting for compliance.

2.1.3. Sample Characteristics

Stratified random sampling: A total of 120 students from three tiers of universities (elite research oriented/comprehensive/teaching focused) stratified by FIBA scores (beginner/intermediate/advanced) were randomly assigned to experimental/control groups. The G Power 3.1 calculation shows that to detect an effect size of d = 0.8 (α = 0.05; power = 0.95), at least 54 participants are required per group, and the current sample size met this requirement (see

Table 1. Demographic characteristics table.

Variable	Experimental (n = 59)	Control (n = 58)	p-Value
Age (years)	19.8 ± 1.2	20.1 ± 1.3	0.228
Male (%)	73%	68%	0.532
FIBA baseline	72.4 ± 8.1	71.9 ± 7.8	0.726

).

Potential biases:

• Gender distribution (70% male) was incorporated as a covariate in mixed-effects modeling (β = 0.12, p = 0.241), confirming that system effectiveness remained stable across subgroups (ΔTEA < 3.2%).
• UWB positioning errors (3.2% anomalies) were corrected through filtering.

2.2. Data Preprocessing

2.2.1. Reliability and Validity

Reliability was assessed as follows:

• Internal consistency: NASA-TLX α = 0.85;
• Inter-rater reliability: control group tactical annotations (Kappa = 0.78).
• Validity was assessed as follows:
• Content validity: 5-expert review (CVR = 0.82);
• Construct validity: EFA extracted 3 factors (mental demand, time pressure, and frustration), explaining a 78.3% variance.

2.2.2. Bias Mitigation

Missing data: here, we used EM algorithm imputation (4.2% missing rate), with Little’s MCAR test (p = 0.12).

Outliers: here, we used Tukey’s fences (IQR × 1.5) with Winsorization (3.1% of the data was affected).

For multimodal alignment, we used the following:

• Timestamp synchronization: NTP protocol (<5 ms error);
• Trajectory filtering: Kalman filter (62% noise reduction);
• Physiological signals: 4th-order Butterworth low-pass (50 Hz cutoff).

2.2.3. Multi-Agent Reinforcement Learning Framework

The MARL architecture employs centralized training with decentralized execution (CTDE), utilizing twin-delayed DDPG (TD3) with prioritized experience replay (α = 0.6, β = 0.4). Policy networks (256-128-64 units) update every 50 episodes through gradient clipping (max norm = 0.5), achieving Nash equilibrium in 87% of defensive scenarios.

2.3. Three-Dimensional Evaluation System

2.3.1. Framework Design

Building upon OECD’s competency-based assessment framework, we propose a triaxial evaluation model (Figure 1) comprising the following:

• Skill Execution Accuracy (SEA): quantified through trajectory deviation (Euclidean distance) between the actual and ideal tactical paths;
• Tactical Creativity Index (TCI): evaluated via expert scoring (7-point Likert) on originality and adaptability;
• Physiological Load Index (PLI): calculated as HRV/sEMG composite scores normalized by individual baselines.

Figure 1. Architecture of “Skill–Creativity–Load” evaluation system.

Figure 1. Architecture of “Skill–Creativity–Load” evaluation system.

2.3.2. Measurement Protocol

Validation: we conducted confirmatory factor analysis (CFA) with RMSEA = 0.06 and CFI = 0.93, meeting psychometric standards (see

Table 2. Measurement protocol and psychometric validation of the Three-Dimensional Evaluation System.

Dimension	Data Source	Weight	Validation Method
SEA	Azure Kinect trajectory data	40%	Inter-rater ICC = 0.89
TCI	Coach evaluations + MARL log	35%	CVR = 0.81 (5 experts)
PLI	Polar H10 + Myo armband	25%	Cronbach’s α = 0.79

).

2.4. Data Analysis

2.4.1. Analytical Methods

The triaxial evaluation metrics were applied in both experimental (VR-based) and control (on-court) groups through blinded assessment protocols. Two certified FIBA instructors independently rated the TCI scores, with disagreements resolved by third-party arbitration.

Mauchly’s test indicated the violation of sphericity assumptions (W = 0.62, p < 0.05), necessitating Greenhouse–Geisser adjustments for all repeated-measures ANOVA results (see

Table 3. Overview of data analysis methods.

Objective	Method	Tools/Parameters	Rationale
Pedagogical effect comparison	Mixed-design ANOVA	SPSS27, Mauchly’s test (W = 0.62, p < 0.05), with Greenhouse–Geisser correction (ε = 0.85)	Handles within/between-group interactions
Privacy–performance tradeoff	Federated learning evaluation	PySyft, ε = 0.3/0.8 differential privacy	Quantifies accuracy loss vs. communication cost
Cognitive load mechanisms	Grounded theory coding	NVivo12, Cohen’s κ = 0.82	Reveals teacher–student–AI interaction patterns
Long-term skill transfer	ANCOVA	Baseline-adjusted R2 = 0.67	Controls individual differences

).

All line charts display error bars (±1 SEM) and significance markers (* p < 0.05, ** p < 0.01) based on post hoc Bonferroni tests.

2.4.2. Workflow

Federated training: this included 50 FedAvg rounds (5 local epochs/round, cosine annealing lr = 0.001).

Mixed-effects modeling: this was performed for between-group (experimental/control) × within-group (pre/post) interactions with covariates (baseline skills and age).

Qualitative analysis: this involved open coding (28 concepts) → axial coding (5 categories) → selective coding (core theme: human–AI responsibility allocation) (see Figure 2).

2.4.3. Robustness Verification

Bootstrap resampling (1000 iterations): we verified β-coefficient stability (95% CI variation < 5%).

Sensitivity analysis: we obtained consistent results after outlier removal (Δ < 2%).

2.5. Hypotheses and Variable Definition

The key variables were as follows:

• Independent: virtual–physical interaction intensity (X₁), privacy budget hierarchy (X₂), and assessment dimensionality (X₃);
• Mediating: spatial perception (M₁) and collaborative awareness (M₂);
• Dependent: tactical execution accuracy (TEA) (Y₁), decision time (Y₂), and tactical creativity (Y₃).

Our hypotheses were as follows:

• H₁: X₁ positively influences M₁ (β₁ = 0.68, p < 0.01), enhancing Y₁ and reducing Y₂.
• H₂: X₂ improves model generalization, indirectly boosting M₂ (β₂ = 0.42, p < 0.05), thereby elevating Y₂ and Y₃.
• H₃: X₃ positively moderates outcome sustainability: the3D assessment groups show 15% lower skill decay than 1D groups (Δ = 0.15, p = 0.013).

Experimental validation: we compared high/low feedback frequencies (X₁), ε = 0.3/0.8 privacy configurations (X₂), and 1D/3D assessment modes (X₃) to test theoretical pathways.

2.6. Dual-Drive Triaxial Evaluation Model

Mathematical formulation:

$$Y=f⟨X_1,X_2,X_3|M_1,M_2⟩$$

$$M_1=a_1{X}_1+{\gamma }_1 \left(\mathrm{NeRF}\mathrm{Fidelity}\right)$$

$$M_2=a_2{X}_2+{\gamma }_2 \left(\mathrm{Federated}\mathrm{Communication}\mathrm{Efficiency}\right)$$

Constraints:

$${ϵ}_{trajectory}\le 0.8,{ϵ}_{physiology}\le 0.3$$

Model schematic (see Figure 3):

Model implications:

• Dual-drive synergy: digital twins (cognitive enhancement) and federated learning (data security) jointly propel pedagogical efficacy.
• Triaxial assessment: dynamic feedback from skill (Y₁), efficiency (Y₂), and creativity (Y₃) guides system optimization.
• Ethical thresholds: privacy-intensity vs. model-accuracy tradeoffs define technological empowerment boundaries.

Theoretical advancements:

• Dynamic embodiment: this proposes virtual simulation–cognitive evolution interaction mechanisms, revealing technology-mediated cognitive pathways.
• Context-specific data governance: this establishes hierarchical privacy paradigms for educational multimodal data.
• Competency-oriented evaluation: this aligns with the “New Curriculum Standards” core literacy objectives through 3D metrics.

This framework provides an extensible theoretical foundation for intelligent sports education, with its dual-drive dynamic regulation logic being applicable to cross-disciplinary contexts (e.g., physics experiments and medical simulations), advancing the ecological development of educational digital transformation.

2.7. Methodological Innovations and Limitations

The method’s innovations are as follows:

• Multimodal federated alignment: the ST-FedAlign algorithm reduced communication overhead by 40%.
• Dynamic cognitive monitoring: HRV-behavior fusion predicted the cognitive load (AUC = 0.79).

The method’s limitations are as follows:

• Sample bias: this study presents gender imbalance; future studies should include under-represented groups.
• Hardware dependency: this study presented high-end VR requirements; lightweight mobile AR solutions are under development.

This methodology balances technical feasibility and pedagogical validity through rigorous mixed-methods design, offering a reproducible paradigm for intelligent sports education research.

3. Results

3.1. Overview and Key Findings

This study addresses three core challenges in collegiate basketball tactical education under educational digital transformation: dynamic adversarial scenario modeling distortion, privacy–performance tradeoffs in cross-institutional collaboration, and limitations of unidimensional assessment systems. Through a dual-driven immersive system integrating digital twins and federated learning, the following key results were achieved:

Technical efficacy: the experimental group demonstrated 35.2% higher TEA (Cohen’s d = 1.21, 95% CI [1.04, 1.38], p < 0.001), exceeding the 0.8 threshold for large effect sizes in educational interventions, 1.8 s faster decision making (p < 0.05), and 47% improved team efficiency.

Privacy–performance balance: hierarchical federated learning (trajectory ε = 0.8, physiology ε = 0.3) limited cross-institutional model accuracy loss to 2.4% while reducing communication overhead by 23%.

Competency advancement: triaxial assessment (Skill–Creativity–Load) explained 75% variance, with experimental groups demonstrating 22% more unconventional tactical applications.

3.2. Specific Findings

3.2.1. Core Discovery: Cognitive Enhancement via Virtual–Physical Interaction

Dynamic modeling superiority:

The digital twin system reduced virtual simulation error rates from the literature-reported value of 42% to 5% through Multi-Agent Reinforcement Learning (MARL) and neural radiance fields (NeRFs). Virtual–physical interactions enhanced spatial perception (r = 0.68, p < 0.01) by strengthening action-space mapping (23% increased parietal cortex activation).

Real-time feedback value:

Experimental groups received 7.3 virtual prompts per session, achieving 3× higher error correction efficiency than traditional video reviews (average teacher feedback delay: 2.1 min).

3.2.2. Paradoxical Insight: Ethical Dilemmas of Technological Empowerment

System dependency:

A total of 12% of students showed stagnant autonomous decision making (real-game tactical transfer: +9%, p = 0.083) due to excessive prompt intervention.

Non-linear privacy–performance relationship:

Physiological data privacy budgets below ε = 0.3 caused abrupt 12% model accuracy degradation (Δ = 0.12, p = 0.004).

3.2.3. Extended Contribution: Competency-Oriented Assessment Reform

Tactical Creativity Index (TCI) validity:

TCI metrics (e.g., feint maneuvers and unconventional passes) strongly correlated with team win rates (r = 0.65, p < 0.01).

Long-term efficacy warning:

Skill retention analysis revealed that a 15% TEA decay (λ = 0.28/month, SE = 0.03) significantly correlated with a decreased training frequency (r = −0.67, p = 0.008), fitting an exponential forgetting curve. Monte Carlo simulations suggested that booster sessions every 21 ± 3 days could maintain ≥90% peak proficiency.

4. Discussion

4.1. Theoretical Contributions

Cognitive enhancement: The reduction in simulation error rates (42% → 5%) supports the technological medium embodiment hypothesis, demonstrating how virtual–physical interactions can enhance spatial cognition. This phenomenon has been verified in multiple studies. For example, one study explored the application of virtual reality (VR) in skill training and found that high-immersion VR simulations may assist in improving skill learning [16]. Another study indicated that motion cues in virtual environments could improve users’ spatial perception by adjusting monocular depth cues, thereby reducing interception errors [17]. In large-scale virtual city exploration, users’ familiarity with interactive maps increased task accuracy [18].

Ethical paradoxes: System dependency challenges the “technology enhances autonomy” hypothesis, prompting a re-evaluation of the boundaries of human–AI collaboration. The intervention of AI may delay or complicate decision-making processes, leading to confusion about roles and responsibilities [19]. AI automation may also cause skill degradation and a reduction in task diversity, thereby affecting employees’ intrinsic motivation and engagement [20]. The application of AI in political decision making may challenge traditional democratic participation models. While AI excels in data analysis and strategic reasoning, it may also lead to a lack of transparency and accountability [21]. In summary, system dependency not only challenges the “technology enhances autonomy” hypothesis but also forces us to redefine the boundaries of human–AI collaboration. When designing and applying AI systems, it is essential to strike a balance between technological advantages and human autonomy for more effective collaboration [22]. Therefore, the non-linear relationship between privacy and performance (ε = 0.3 threshold) contradicts the unified federated learning strategy and advocates for tiered data classification standards.

4.2. Practical Implications

Dynamic modeling: The introduction of the MARL + NeRF framework provides an effective method for resource-constrained schools, enabling them to achieve educational outcomes close to those of top schools (87%). A study suggests that schools can measure and improve their environmental performance using sustainability indicators, thereby achieving higher educational effectiveness even with limited resources [23]. Additionally, student engagement and self-reflection play a key role in enhancing educational outcomes. A randomized field experiment showed that when students reflect on their behavior at school, they can improve their academic performance and participation [24]. Furthermore, the selection of schools and the rational allocation of resources are also crucial factors in enhancing educational outcomes. Research indicates that increasing family and school resources can significantly improve student academic achievement, and school competition is not a primary factor in improving scores [25]. In conclusion, the MARL + NeRF framework provides a possibility for resource-limited schools to achieve top-tier educational outcomes through various innovations. This not only promotes the popularization of tactical education but also offers new perspectives on educational equity and sustainable development.

Privacy optimization: The design concept of the ST-FedAlign algorithm is based on the “data availability without visibility” principle, which enables cooperation and data sharing across schools without exposing specific data. This approach is similar to the standardization of SEND data and cross-study analysis, which emphasizes the structural representation and cross-study usability of data without exposing specific data information [26]. In the field of education, by enhancing communication between schools and parents, the International School Attendance Network (INSA) aims to improve student attendance. This cooperation also relies on data sharing and enhanced communication rather than the visibility of specific data [27]. This approach not only improves the accuracy of comparisons but also enhances the usability and reproducibility of data [28].

Evaluation reform: To achieve a shift in educational goals, many studies and practices have explored how to apply competency-based frameworks in educational practice. The implementation of these frameworks requires adaptive adjustments in different contexts to ensure their effectiveness and applicability [29]. Standardized frameworks not only promote educational innovation but also ensure the measurability and effectiveness of educational outcomes [30]. The application of these frameworks improves the consistency and reliability of evaluations and provides educators with clear guidelines to help them achieve educational objectives [31]. By putting competency-based frameworks into practice, educational institutions can find a balance between innovation and standardization, thus achieving a transformation in educational goals. These frameworks not only provide clear guidelines for educators but also promote innovation and improvement in educational practices.

4.3. Implementation Guidelines

Policy: In modern education, the application of artificial intelligence (AI) technologies is becoming increasingly widespread, especially in teacher certification and the formulation of educational standards. According to one study, the certification process can support organizational and cultural changes, promote cooperation, and encourage improvement, which may help enhance the resilience and adaptability of the educational system [32]. The establishment of hardware subsidies is crucial for promoting the widespread adoption of AI technologies in education. This strategy is similar to the process of equipment upgrades in the context of Industry 4.0, where the development and application of information processing tools enhance the overall performance and efficiency of systems [33]. By establishing a unified certification system, such as the Council for the Accreditation of Educator Preparation (CAEP), educational standards and expectations for teachers can be raised, thereby enhancing the professional status of educators [34].

Industry: When developing the SaaS platform “AI Basketball Coach Pro”, offering an on-demand tactical library and computational support is an innovative and promising direction, for example, using human–computer interaction technology and motion capture technology in table tennis training to create an autonomous and intelligent training system [35]. This technology helps basketball coaches analyze players’ technical movements, provide real-time feedback, and adjust training plans based on players’ performances. Additionally, the platform can reference research on the technical and psychological demands in small-scale basketball games, offering personalized tactical libraries and training plans [36]. Augmented reality technology can also be used to improve tactical teaching effectiveness. By displaying virtual tactical demonstrations on a tactical board, coaches can more intuitively communicate tactical intentions, helping players better understand and execute tactics [37]. This technology not only improves the efficiency of tactical teaching but also enhances players’ spatial awareness and tactical execution.

Governance: When formulating implementation guidelines for educational federated learning, multiple aspects need to be considered to ensure the standardization and effectiveness of data classification and privacy audits. First, federated learning, as a decentralized machine learning method, enables model training without sharing raw data, thereby effectively protecting data privacy [38]. This method has already demonstrated its advantages in privacy protection and data sharing, especially in cases involving sensitive data in the healthcare field [39]. Furthermore, when formulating guidelines, it is necessary to refer to existing standards and frameworks. For example, the ISO/IEC 11179 standard provides a framework for metadata registration, which can support the syntactic and semantic interoperability of data [40]. In the field of education, similar standards can help ensure the consistency and accuracy of data classification, thereby improving the usability and reusability of data. Additionally, the formulation of guidelines should consider the legal and regulatory differences across regions and institutions. For instance, in multi-state comparative efficacy research networks, the development of privacy and security policy frameworks needs to take into account the legal and regulatory differences among states as well as the specific policies of institutions [41]. This flexibility is crucial for ensuring the applicability of the guidelines in different environments.

4.4. Limitations and Future Directions

Long-term skill degradation is a concern. The application of virtual reality technology in sports training and rehabilitation has shown its potential. For example, in basketball decision-making training, virtual reality technology has been proven to improve training outcomes, and the virtual reality group outperformed the traditional training group in untrained scenarios [42]. This indicates that virtual reality technology has advantages in skill transfer and generalization.

Additionally, virtual reality technology has shown positive results in rehabilitation training. In finger and hand function rehabilitation, serious virtual reality games developed iteratively significantly improve patients’ motivation and daily exercise frequency [43]. This demonstrates that virtual reality technology not only improves training effectiveness but also enhances participants’ motivation, contributing to the long-term maintenance of skills.

Moreover, the application of virtual reality technology in high-intensity interval training has shown potential in improving training enjoyment and output power. Research indicates that gamified virtual reality training can increase the enjoyment of high-intensity interval training for untrained individuals and, in certain modes, improve training intensity [44]. This further supports the value of virtual reality technology in sports training.

In conclusion, the application of virtual reality technology in sports training and rehabilitation demonstrates its potential in enhancing skill levels and maintaining long-term motivation. However, further research into virtual reality transition protocols, such as reducing prompts by 30% weekly, is needed to optimize human–AI interaction thresholds and ensure the long-term maintenance and enhancement of skills.

5. Conclusions

5.1. Synthesis

This study addressed three key issues in university basketball education through a dual-driven immersive system: dynamic scene modeling, privacy–performance conflicts across institutions, and one-dimensional evaluation. The key findings include a 35.2% improvement in tactical execution accuracy (TEA) (p < 0.01), a 2.4% loss in model accuracy under graded privacy (ε = 0.3–0.8), and an 18.7% improvement in the interpretability of tri-axis evaluation. Multi-Agent Reinforcement Learning (MARL) reduced the simulation errors to 5%, and ST-FedAlign reduced the communication costs by 40%. A Dynamic Educational Digital Twin model was proposed, integrating cognitive science, federated learning, and a digital transformation framework.

5.2. Theoretical Advancements

A multimodal data graded protection system (ε = 0.85) was developed based on the dynamic differential privacy model, successfully achieving cross-modal fusion of MOOC platform behavioral data and sports biomechanics parameters. This fills the technological gap between digital education and traditional sports science. Finally, the innovative Tri-Capacity Index (TCI) was proposed, and through structural equation modeling, it was validated for its high alignment with the OECD 2030 Capacity Framework (CFI = 0.93, TLI = 0.91), providing a quantitative evaluation tool for capability-based reforms in smart education systems. These three breakthrough advancements promote the paradigm shift in educational technology from the perspectives of neural mechanisms, data governance, and evaluation systems.

5.3. Practical Impact and Future Directions

5.3.1. Societal Value

The systematic resource allocation mechanism significantly reduced structural effectiveness differences between educational institutions by 87%, providing a measurable pathway for achieving fairness in the Education Informatization 2.0 strategy. Moreover, technological innovation is driving the formation of emerging industry chains, with virtual reality/augmented reality (VR/AR) hardware devices and software-as-a-service (SaaS v2.0) platforms expected to generate a scale economy effect worth RMB 5 billion annually, highlighting the collaborative innovation potential of the education technology industry. Finally, at the policy regulation level, a standardized implementation framework for federated learning technology has been constructed. This guideline enables cross-institutional knowledge sharing through multimodal data circulation protocols and distributed computing standards, ensuring privacy security while providing the institutional infrastructure necessary for the digital transformation of education.

5.3.2. Limitations and Mitigation

There are three methodological limitations in this study and corresponding mitigation strategies:

• The limited sample heterogeneity: To address this, the research team will expand the sample group to include K-12 students and special education needs groups through stratified sampling. A pilot verification project covering 12 provinces and cities has already been initiated.
• The high-cost barrier of immersive equipment: An optical transparent AR solution based on mobile terminals is under development, employing cross-platform lightweight algorithms to implement core functionalities, with the goal of controlling the single-machine cost below RMB 500 (the current prototype development cost has been reduced to RMB 780).
• The lack of longitudinal tracking for skill transfer effects: The study design includes a 36-month stepwise tracking mechanism, constructing a multidimensional skill retention database that includes cognitive behavior parameters and neurophysiological indicators. Standardized assessment tools have undergone validity and reliability testing (Cronbach’s α = 0.89).

5.3.3. Emerging Frontiers

This research reveals three emerging frontiers in the field of educational technology:

• A multimodal transformer model-based heterogeneous data-processing framework (Multimodal-BERT) has been innovatively applied to study the correlations between physiological signals and behavioral performance. This technology uses cross-modal feature alignment algorithms to achieve spatiotemporal synchronization of electrocardiogram (ECG), electromyogram (EMG), and motion trajectory data, significantly improving the accuracy of human–computer interaction behavior prediction (RMSE = 0.12).
• An interdisciplinary research team used a virtual reality closed-loop training system to explore the mechanisms of neural plasticity. Through high-density fNIRS/EEG neuroimaging technology, they quantified the temporal relationship between virtual training and synaptic remodeling in the prefrontal cortex (β = 0.73, p < 0.001), providing neuroscientific evidence for cognitive enhancement technologies.
• To address the ethical dilemmas of smart sports systems, this study established a quantifiable ethical framework containing decision weight thresholds (AI decision rights ≤ 30%), a responsibility attribution tree diagram, and a dynamic evaluation mechanism. The system’s efficacy in protecting human agency was verified through Monte Carlo simulation, with Δ ≥ 87% under different decision weight scenarios.

These converging innovations are driving the research paradigm of educational technology from a single efficacy validation approach toward an exploration of neural mechanisms and collaborative ethical governance. Although technological advancements have increased teaching efficiency, their ultimate value lies in promoting holistic human development. Achieving a balance between efficiency (instrumental rationality) and fairness, ethics, and capability (value rationality) requires interdisciplinary cooperation, forward-thinking policies, and societal collective efforts. Only through such collaboration can intelligent technologies illuminate, rather than obscure, the humanistic essence of education in the digital era.