This study proposes a graph-based emotional curriculum intelligence framework for AI-driven visual teaching in college music education. Using a simulation-based dataset, the framework models knowledge acquisition, emotional dynamics, and engagement. Results indicate improved learning efficiency and stress reduction compared to conventional models, demonstrating potential for adaptive, mental health-aware educational systems.
A subscription to JoVE is required to view this content. Sign in or start your free trial.
Research Article
Simulation-Based Evaluation of Artificial Intelligence-Integrated Visual Teaching Design for Mental Health Promotion in College Music Courses
In This Article
Summary
Abstract
Visual teaching design in college music education has attracted increasing attention because of its potential to enhance student engagement and promote mental well-being. However, conventional instructional approaches often lack the adaptability needed to respond to learners' evolving cognitive and emotional states, limiting their effectiveness in supporting both academic achievement and psychological balance. The main aim of this simulation-based study is to develop and evaluate an artificial intelligence-driven framework that integrates visual teaching strategies with graph-based emotional modeling to optimize learning outcomes in college music courses. A novel graph-based emotional curriculum intelligence framework (GECIF) is proposed, representing the learning environment as an interconnected graph of knowledge, activity, and emotional nodes. The framework incorporates graph-based representation learning, dynamic state modeling, and adaptive learning mechanisms to simulate the evolution of knowledge acquisition, engagement, emotional stability, and stress over time. A synthetic dataset comprising interaction logs, emotional indicators, and learning performance variables was generated through a controlled MATLAB-based simulation pipeline to ensure reproducibility. GECIF was evaluated against a conventional non-adaptive baseline model under identical experimental conditions across normalized simulation steps. Performance was assessed using normalized simulation metrics, including knowledge gain, emotional stability, stress reduction, engagement level, and robustness. The proposed framework consistently outperformed the baseline, demonstrating higher simulated knowledge acquisition, improved emotional regulation, greater engagement, reduced simulated stress, and enhanced robustness. Statistical analysis based on mean values, standard deviations, and 95% confidence intervals confirmed consistent improvements across all evaluation metrics. These findings demonstrate the potential of AI-driven, emotion-aware visual teaching strategies for adaptive music education while providing a scalable and reproducible computational framework to support future research in intelligent educational systems.
Introduction
The integration of artificial intelligence (AI) into educational systems has significantly transformed traditional teaching paradigms, enabling more adaptive, personalized, and data-driven learning experiences1,2,3. In recent years, visual teaching design has emerged as an effective pedagogical approach, particularly in disciplines such as music education, where abstract concepts, emotional expression, and perceptual understanding play a central role4,5,6,7. Visual representations, including graphical interfaces, interactive simulations, and multimedia elements, facilitate deeper cognitive processing and enhance learners’ engagement8,9,10,11. However, despite these advancements, existing visual teaching methods often remain static and lack the capability to dynamically respond to learners’ evolving cognitive and emotional states12,13,14. Music education, especially at the college level, presents unique challenges due to its inherently multidimensional nature15,16,17. Learning outcomes are influenced not only by cognitive understanding of musical theory and performance techniques but also by emotional factors such as motivation, stress, engagement, and psychological well-being18. Previous studies have highlighted the strong relationship between emotional states and learning effectiveness, indicating that students experiencing high levels of stress or low engagement tend to exhibit reduced academic performance19,20,21. While several AI-based educational systems have been proposed to enhance learning personalization, most of them primarily focus on cognitive performance metrics and overlook the critical role of emotional dynamics in shaping the learning process22.
Recent developments in intelligent learning systems have introduced graph-based representations and deep learning techniques to model complex relationships among educational components23,24,25. Graph-based learning frameworks, including graph neural networks (GNNs), have demonstrated strong capabilities in capturing structured dependencies between knowledge elements, learner interactions, and contextual factors26. Additionally, emotion-aware computing has gained attention for its ability to incorporate psychological signals into adaptive learning environments27. Nevertheless, the integration of these approaches within a unified framework for music education remains limited28,29,30. Existing works often treat emotional variables as auxiliary features rather than core components, and they rarely provide mechanisms for continuous adaptation based on the interplay between cognition and emotion31.
Furthermore, the application of AI in mental health-aware education has predominantly relied on empirical or sensor-based data collected from real participants32. However, such approaches require extensive ethical approvals, standardized measurement instruments, and controlled experimental conditions, which may not always be feasible in early-stage research33. As a result, simulation-based methodologies have emerged as an alternative for exploring complex educational dynamics in a controlled and reproducible manner34. By generating synthetic datasets that mimic interaction logs, emotional indicators, and learning performance metrics, researchers can evaluate the behavior of proposed models under various scenarios while maintaining full control over system parameters and assumptions35.
Despite these advancements, several research gaps remain36. First, there is a lack of unified frameworks that simultaneously model knowledge acquisition, emotional evolution, and learning engagement within a structured representation37. Second, current visual teaching systems do not effectively leverage graph-based intelligence to capture the interdependencies between musical concepts, learning activities, and psychological states38. Third, limited attention has been given to the development of scalable and reproducible simulation pipelines that allow systematic evaluation of AI-driven educational models39,40,41. Finally, existing approaches often fail to provide a holistic view of learning that integrates both cognitive and emotional dimensions in a dynamically adaptive manner42.
To address these challenges, this study proposes a graph-based emotional curriculum intelligence framework (GECIF) for AI-driven visual teaching design in college music education. Table 1 provides essential definitions for graph components, node states, and model parameters. This table ensures the methodology is both clear and reproducible. The proposed framework models the educational environment as an interconnected graph consisting of knowledge nodes, activity nodes, and emotional nodes, enabling a comprehensive representation of learning dynamics. A simulation-based dataset is generated using a controlled computational pipeline that includes interaction logs, emotional indicators, and performance metrics, ensuring reproducibility and scalability. The framework incorporates adaptive learning mechanisms, emotional state modeling, and graph-based optimization to simulate the evolution of learning outcomes and psychological conditions over time. Comparative evaluation against a conventional learning model is conducted using multiple performance metrics, including knowledge gain, emotional stability, stress reduction, engagement, and robustness.
The main aim of this study is to develop and evaluate a simulation-based, AI-driven visual teaching framework that integrates graph-based modeling and emotional intelligence to enhance learning efficiency and support mental health-aware education in college music courses.
Access restricted. Please log in or start a trial to view this content.
Protocol
This study is a computational and simulation-based investigation that does not involve human participants, clinical data, or personal information. All data used in this research were synthetically generated through controlled simulation processes. Therefore, ethical approval from an institutional review board and informed consent were not required. The study adheres to standard guidelines for computational research and ensures full reproducibility and transparency of the methodology.
Theoretical framework and rationale
The proposed graph-based emotional curriculum intelligence framework (GECIF) is grounded in the integration of artificial intelligence, graph-based learning, emotional computing, and visual pedagogy, aiming to model learning as a dynamic and multi-dimensional process that simultaneously captures cognitive development, emotional evolution, and behavioral engagement. Traditional instructional approaches often assume a linear progression of knowledge acquisition and do not adequately reflect the complex interactions between cognitive performance and psychological states. This limitation is particularly critical in college music education, where emotional expression, motivation, and creativity are inseparable from technical and theoretical learning. Within this context, the framework conceptualizes the educational process as an interconnected system in which knowledge elements, learning activities, and emotional states continuously influence one another. Graph-based modeling enables a structured and scalable representation of these relationships, allowing both direct and indirect dependencies to be captured effectively. Emotional dynamics are incorporated as intrinsic components rather than auxiliary variables, enabling the simulation of how psychological conditions evolve and propagate during the learning process. The integration of visual teaching design further enhances interpretability by transforming abstract computational interactions into intuitive graphical representations.
Methodology and simulation study design
This study adopts a computational simulation-based design in which no human participants are involved, and all data are synthetically generated through a controlled and reproducible pipeline. The outcomes reported, therefore, represent model-based evaluations rather than empirical observations derived from real learners. Improvements in learning performance, engagement, emotional stability, and stress reduction are interpreted as simulated or potential effects of the proposed framework. The simulation environment approximates realistic learning conditions by incorporating temporal dependencies, variability in interaction patterns, and evolving emotional states. A comparative experimental configuration is implemented in which the proposed GECIF framework is evaluated against a conventional baseline model that represents a non-adaptive learning system where knowledge evolves solely based on interaction intensity without graph-based representation, emotional modeling, or curriculum optimization. Both models are executed under identical simulation conditions to ensure a consistent and unbiased comparison.
Dataset generation and structure
A large-scale dataset was constructed to simulate learning dynamics in college music education by integrating interaction logs, emotional indicators, and learning performance metrics within a unified temporal framework. The dataset was derived and extended from the publicly available student performance dataset (UCI Machine Learning Repository), which provides academic performance data along with demographic and behavioral attributes. This dataset served as a foundational reference for modeling realistic learning patterns. Interaction logs represent simulated engagement with learning activities such as practice sessions, listening exercises, and visual learning tasks. Emotional indicators capture psychological states, including stress, engagement, and motivation, while learning performance reflects knowledge acquisition and progression over time. To enrich the dataset and incorporate temporal dependencies, a MATLAB-based simulation pipeline was employed with fixed random seed initialization to ensure reproducibility. Temporal continuity was introduced through autoregressive processes, enabling interaction behavior and emotional states to evolve gradually over time. The final dataset includes one thousand simulated learners observed over five hundred time steps, with multiple interaction features representing diverse learning behaviors. All variables are normalized within the range [0,1] to ensure consistency and comparability. The dataset is partitioned into training, validation, and testing subsets using a 70%–15%–15% split to ensure unbiased model evaluation.
Graph construction and representation
The learning environment is modeled as a weighted graph defined as:
G = (V,E,W) (1)
where denotes the set of nodes, represents the set of edges, and is the weighted adjacency matrix describing the strength of relationships between nodes. The node set is decomposed as:
V = Vk∪Va∪Ve (2)
where Vk, Va, and Ve correspond to knowledge nodes, activity nodes, and emotional nodes, respectively. The edge weights are computed as:
Wij = α Sc(i,j) + βSe(i,j) (3)
where Sc(i,j) represents cognitive similarity between nodes, Se(i,j) represents emotional correlation, and α and β are weighting coefficients controlling their relative influence. Equation (3) defines how structural and emotional relationships jointly determine the connectivity of the graph.
Node state representation and graph learning
Each node is associated with a dynamic state vector defined as:
xi(t) = [ki(t),ei(t),ai(t)] (4)
where ki(t), ei(t), and ai(t) denote knowledge level, emotional state, and engagement level at the time t, respectively. Node representations are updated using graph-based aggregation:
(5)
where 
is the embedding of the node i at layer l, σ is a nonlinear activation function, and N(i) denotes the set of neighboring nodes. Equation (5) describes how each node integrates information from its neighbors. An attention mechanism is further introduced:
(6)
where αij is the attention coefficient and ∅(·) is a similarity function. Equation (6) enables adaptive weighting of neighboring node contributions.
Emotional dynamics modeling
The temporal evolution of emotional states is modeled as:
(7)
where γ is the emotional retention coefficient. Equation (7) captures both persistence and propagation of emotional influence. Emotional stability is quantified as:
(8)
where E+(t) and E-(t) denote positive and negative emotional components, and 
is a small constant preventing division by zero. Stress accumulation is defined as:
(9)
where λ(τ) represents instantaneous stress intensity. Equation (9) models cumulative stress over time.
Learning optimization and adaptive curriculum design
The learning process is governed by a multi-objective optimization function:
J = λ1K + λ2K - λ3S (10)
where K denotes knowledge gain, E denotes emotional stability, and S denotes stress level, while λ1, λ2, λ3 are weighting coefficients. Equation (10) balances cognitive and emotional objectives. Adaptive curriculum rewiring is defined as:
Wij(t + 1) = Wij(t) + ηΔij(t) (11)
where η is the learning rate and Δij(t) represents update adjustments driven by learning dynamics. Equation (11) allows the graph structure to evolve over time.
Temporal scale and simulation duration
All simulations are conducted using normalized time steps rather than real-time units. Short intervals correspond to scaled simulation steps representing system evolution, while extended simulations over five hundred steps are used to evaluate long-term behavior, convergence, and robustness.
Evaluation metrics and statistical analysis
The framework is evaluated using normalized metrics, including knowledge gain, emotional stability, stress level, engagement level, and robustness. Statistical analysis is performed using MATLAB statistical functions, with mean values, standard deviations, and 95% confidence intervals computed for each metric. Normality assumptions are verified prior to applying paired t-tests. Comparative evaluation between the proposed framework and the baseline model is conducted across n = 1000 simulated learners, with statistical significance defined as p < 0.05.
Reproducibility and implementation details
All simulations, data processing pipelines, and evaluation procedures are fully reproducible. The dataset generation process, graph construction parameters, model configurations, and optimization settings are explicitly defined. Random seed initialization is fixed to ensure consistent outputs across repeated runs. All hyperparameters, scripts, and dataset descriptions are provided in the supplementary materials, enabling full replication of the study using MATLAB or equivalent computational tools.
All simulations, data processing pipelines, and evaluation scripts are provided in Supplementary File 1. The dataset includes raw interaction logs, emotional indicators, and learning performance metrics. Graph construction parameters and optimization settings are fully documented. The system can be reproduced using standard machine learning frameworks. Random seed initialization is fixed to ensure consistency. All hyperparameters are reported explicitly. The protocol ensures full transparency and reproducibility.
Access restricted. Please log in or start a trial to view this content.
Results
The performance of the proposed Graph-based emotional curriculum intelligence framework (GECIF) was evaluated using a simulation-based dataset consisting of n = 1000 learners over T = 500 normalized time steps. Figure 1 illustrates the conceptual architecture of the GECIF for AI-driven visual music education and mental health promotion. All metrics are reported on normalized scales within the range [0,1]. Statistical evaluation includes mean values, standard deviations (SD), and 95% confiden...
Access restricted. Please log in or start a trial to view this content.
Discussion
The present study proposed a graph-based emotional curriculum intelligence framework (GECIF) that integrates graph-based representation learning, emotional state modeling, adaptive curriculum optimization, and AI-driven visual teaching into a unified computational framework for college music education. Unlike conventional AI-supported educational models that primarily optimize academic performance, the proposed framework simultaneously models interactions among knowledge acquisition, emotional regulation, learner engagem...
Access restricted. Please log in or start a trial to view this content.
Disclosures
The author has no conflict of interest.
Acknowledgements
The author would like to express sincere gratitude to the Music Department at Anqing Normal University for providing the academic environment and institutional support necessary for conducting this research. Special appreciation is extended to colleagues and students who contributed valuable insights and feedback during the development and evaluation of the proposed framework. The author also acknowledges the support of Anqing Normal University in facilitating access to resources and research infrastructure.
Access restricted. Please log in or start a trial to view this content.
Materials
| Name | Company | Catalog Number | Comments |
|---|---|---|---|
| 32 GB DDR4 RAM Module | Kingston Technology | NA | |
| CUDA Toolkit for GPU Acceleration (v11.8) | NVIDIA | version 1.18 | |
| Curriculum Adaptation Engine | Custom Implementation | CAE003 | |
| Dataset: Emotional Indicators | Generated Dataset | DEI006 | |
| Dataset: Learning Performance Metrics | Generated Dataset | DLPM007 | |
| Dataset: Student Interaction Logs | Generated Dataset | DIL005 | |
| Emotional State Simulation Module | Custom Implementation | ESM002 | |
| GPU Workstation (NVIDIA RTX 3080) | NVIDIA | NA | |
| Graph Neural Network Implementation Module | Custom Implementation | NA | |
| Intel Core i7 Processor (3.6 GHz) | Intel Corporation | NA | |
| Jupyter Notebook Environment (v6.5) | Project Jupyter | version 6.5 | |
| MATLAB Software (R2023a) | MathWorks | R2023a | |
| Matplotlib Visualization Library (v3.7) | Matplotlib | version 3.7 | |
| NetworkX Graph Processing Library (v3.1) | NetworkX Developers | version 3.1 | |
| NumPy Numerical Computing Library (v1.24) | NumPy Developers | version 1.24 | |
| Optimization Module (Multi-objective Solver) | Custom Implementation | MOS008 | |
| Pandas Data Processing Library (v2.0) | Pandas Development Team | version 2.0 | |
| Python Programming Environment (v3.10) | Python Software Foundation | version 3.10 | |
| PyTorch Deep Learning Framework (v2.0) | Meta AI | version 2.0 | |
| Random Seed Initialization Script | Custom Implementation | RSI009 | |
| Scikit-learn Machine Learning Library (v1.3) | Scikit-learn | version 1.3 | |
| Seaborn Statistical Visualization Library (v0.12) | Seaborn | version 0.12 | |
| TensorFlow Library (v2.12) | version 2.12 | ||
| Visual Learning Interface Toolkit | Custom Implementation | VLI004 | |
| Windows 11 Operating System | Microsoft | W11 |
References
- Li Y. Music aesthetic teaching and emotional visualization under emotional teaching theory and Deep learning. Front Psychol. 2022;13.
- Yang J. Retraction notice to: 'research on music education: Integrating synaesthesia theory and colour psychology', HTS Teologiese Studies/Theological Studies 79(4), A8848. HTS Teol Stud. 2024;80(1).
- Zhao Y. Cognitive mapping-based audiovisual and singing music therapy as a cross-intervention for college students' anxiety. Appl Math Nonlinear Sci. 2024;9(1).
- Iyendo TO, Uwajeh PC, Oseke BN, Dong L, Adejumo A, Umar I, et al. Effect of audio-visual-based art and music therapy in reducing Adolescents Post Traumatic Stress disorder. J Adolesc Health. 2024;74(2):283–291.
- Sones AC, Fogelson DL, Glick ID, Shader RI. Untreated mental illness has created a national tragedy: A pandemic of homelessness. J Clin Psychopharmacol. 2022;42(2):115–117.
- Omelchenko M, Migalina V, Kaleda V. The effect of untreated illness in youth depression: A cross-sectional study. Consort Psychiatr. 2022;3(4):8.
- Vargas-Huicochea I, Álvarez-del-Río A, Rodríguez-Machain AC, Aguirre-Benítez EL, Kelsall N. Seeking psychiatric attention among university students with mental health problems: The influence of disease perception. Int J Ment Health Addict. 2022;20(1):505–521.
- Shim YR, Eaker R, Park J. Mental health education, awareness and stigma regarding mental illness among college students. J Ment Health Clin Psychol. 2022;6(2):6–15.
- Bantjes J, Kessler MJ, Hunt X, Stein DJ, Kessler RC. Treatment rates and barriers to mental health service utilisation among university students in South Africa. Int J Ment Health Syst. 2023;17(1):38.
- McLafferty M, Brown N, Brady J, McLaughlin J, McHugh R, Ward C, et al. Variations in psychological disorders, suicidality, and help-seeking behaviour among college students from different academic disciplines. PLoS One. 2022;17(12):e0279618.
- Liu Z. Music teaching curriculum integrates physical exercise to regulate Students' mental health. Rev Psicol Deporte. 2024;33(1):426–435.
- Bian C, Wang X, Huang Y, Zhou S, Lu W. Effects of AI-generated images in visual art education on students' classroom engagement, self-efficacy and cognitive load. Humanit Soc Sci Commun. 2025;12(1):1–14.
- Yu H, Zou Z. The music education and teaching innovation using blockchain technology supported by artificial intelligence. Int J Grid Util Comput. 2023;14(2-3):278–296.
- Mulvogue J, Ryan C, Hunt S, Cross M, Cleary M. Promoting positive outcomes in higher education: Supporting undergraduate student mental health and well-being. Issues Ment Health Nurs. 2023;44(7):673–677.
- Sholichatun Y, Aziz R, Baharuddin B, Mulyadi M, Muhid A. Building resilience to promote mental health in university students. Health Educ Health Promot. 2025;13(2):257–263.
- Martínez-García A, Valverde-Montesino S, García-García M. Promoting mental health in higher education: towards a model of well-being factors in emerging adulthood. Int J Qual Stud Health Well-Being. 2024;19(1):2408831.
- Ahluwalia M, Shillington KJ, Irwin JD. The relationship between resilience and mental health of undergraduate students: A scoping review. J Am Coll Health. 2025;73(3):1266–1279.
- Aggarwal D. Integration of innovative technological developments and AI with education for an adaptive learning pedagogy. China Pet Process Petrochem Technol. 2023;23(2):709–714.
- Ullah RS, Hashim F, Bandeali MM, Akbar A. Artificial Intelligence in Curriculum Design a Roadmap for Adaptive and Personalized Learning in Higher Education. Crit Rev Soc Sci Stud. 2025;3(3):304–322.
- Strielkowski W, Grebennikova V, Lisovskiy A, Rakhimova G, Vasileva T. AI‐driven adaptive learning for sustainable educational transformation. Sustain Dev. 2025;33(2):1921–1947.
- Hooda M, Rana C, Dahiya O, Rizwan A, Hossain MS. Artificial intelligence for assessment and feedback to enhance student success in higher education. Math Probl Eng. 2022;2022(1):5215722.
- Caspari-Sadeghi S. Artificial intelligence in technology-enhanced assessment: A survey of machine learning. J Educ Technol Syst. 2023;51(3):372–386.
- Topping KJ, Gehringer E, Khosravi H, Gudipati S, Jadhav K, Susarla S. Enhancing peer assessment with artificial intelligence. Int J Educ Technol High Educ. 2025;22(1):3.
- Hammoudi Halat D, Soltani A, Dalli R, Alsarraj L, Malki A. Understanding and fostering mental health and well-being among university faculty: A narrative review. J Clin Med. 2023;12(13):4425.
- Jääskeläinen T, López-Íñiguez G, Phillips M. Music students' experienced workload in higher education: A systematic review and recommendations for good practice. Music Sci. 2023;27(3):541–567.
- Ghatpande AC, Anand SK. Mental health and stress management among college students: a critical analysis. Int J Sci Res Eng Manag. 2025;9(1):1–9.
- Amanvermez Y, Rahmadiana M, Karyotaki E, de Wit L, Ebert DD, Kessler RC, et al. Stress management interventions for college students: A systematic review and meta-analysis. Clin Psychol Sci Pract. 2023;30(4):423.
- Córdova Olivera P, Gasser Gordillo P, Naranjo Mejía H, La Fuente Taborga I, Grajeda Chacón A, Sanjinés Unzueta A. Academic stress as a predictor of mental health in university students. Cogent Educ. 2023;10(2):2232686.
- Alhasani M, Orji R. Promoting stress management among students in higher education: Evaluating the effectiveness of a persuasive time management mobile app. Int J Hum-Comput Interact. 2025;41(1):219–241.
- Campbell F, Blank L, Cantrell A, Baxter S, Blackmore C, Dixon J, et al. Factors that influence mental health of university and college students in the UK: a systematic review. BMC Public Health. 2022;22(1):1778.
- Nepal S, Liu W, Pillai A, Wang W, Vojdanovski V, Huckins JF, et al. Capturing the college experience: a four-year mobile sensing study of mental health, resilience and behavior of college students during the pandemic. Proc ACM Interact Mob Wearable Ubiquitous Technol. 2024;8(1):1–37.
- Menefee DS, Ledoux T, Johnston CA. The importance of emotional regulation in mental health. Am J Lifestyle Med. 2022;16(1):28–31.
- Sattar G, Rustemova S, Nebessayeva Z. Socially Interactive Approaches and Digital Technologies in Art Education: Developing Creative Thinking in Students During Art Classes. Open Educ Stud. 2025;7(1):20250096.
- Johnson R, Cantrell K, Cutcliffe K, Batorowicz B, McLean T. Expanding creative communities in the visual arts: Using Padlet to support student engagement and belonging in stressful contexts. Art Educ. 2023;76(4):33–39.
- Cowand A, Amarsaikhan U, Ricks RF, Cash ED, Sephton SE. Self-compassion is associated with improved well-being and healthier cortisol profiles in undergraduate students. Mindfulness. 2024;15(7):1831–1845.
- Brovchak LS, Starovoit LV, Likhitska LM, Todosiienko NL, Shvets IB. Integrating music, drama and visual arts in extracurricular programs: enhancing psychological development in early school-aged children. Sapienza Int J Interdiscip Stud. 2024;5(3):e24052.
- Salomon-Gimmon M, Orkibi H, Elefant C. The contribution of a music and arts rehabilitation program to the creative identity, well-being, and community integration of people with mental health conditions. J Humanist Psychol. 2026;66(2):518–551.
- Shkembi F, Sauku VH, Ibrahimi S, Ibrahimi E. The psychological impact of online education on student well-being and motivation. Int Soc Sci Educ J. 2024;2(2):1–8.
- Pointon-Haas J, Waqar L, Upsher R, Foster J, Byrom N, Oates J. A systematic review of peer support interventions for student mental health and well-being in higher education. BJPsych Open. 2024;10(1):e12.
- Reist C, Petiwala I, Latimer J, Raffaelli SB, Chiang M, Eisenberg D, et al. Collaborative mental health care: A narrative review. Medicine. 2022;101(52):e32554.
- Peng C. Integrating computational intelligence in music teaching: A decision-making approach for personalised education. Int J Inf Commun Technol. 2025;26(27):38–51.
- Chi M, Wang N, Wu Q, Cheng M, Zhu C, Wang X, et al. Implementation of the flipped classroom combined with problem-based learning in a medical nursing course: A quasi-experimental design. Healthcare. 2022;10(12):2572.
Access restricted. Please log in or start a trial to view this content.