AI Empowers Traditional Physical Education Teaching in Higher Vocational Education: Exploring New Paths for the Integration of Health Education

Summary

This protocol describes the integration of an AI-guided atomization sensing system that monitors respiration, temperature, and motion to adapt health education content in real-time during vocational physical education, and evaluates its effects through a six-week quasi-experiment.

Abstract

This study evaluates an AI-guided atomization sensing system designed to integrate adaptive health education into vocational physical education. A quasi-experimental design was conducted with 356 participants allocated to either an intervention group (n = 178) or a control group (n = 178). The system used wearable atomization sensors (monitoring respiratory patterns, ambient temperature, and motion) connected to an AI engine that applied rule-based and machine-learning logic to adjust instructional content in real time-for example, reducing complexity or switching to audiovisual demonstrations when signs of fatigue were detected. Sessions were delivered in classroom settings twice per week for six weeks, with surveys administered immediately before and after the intervention. Five outcomes were assessed using validated instruments: knowledge retention, engagement, behavioral intention, self-efficacy, and technology acceptance. Statistical analyses included ANOVA, paired-sample t-tests, Pearson correlations, and χ² tests, with results reported as F(df1, df2), p, η², and 95% confidence intervals where applicable. The intervention group achieved significantly greater improvements than the control group in knowledge retention (Δ = +21.7 vs. +10.6; F(1, 354) = 46.21, p < 0.001, η² = 0.21), engagement (F(1, 354) = 39.87, p < 0.001, η² = 0.18), behavioral intention (F(1, 354) = 42.55, p < 0.001, η² = 0.19), self-efficacy (F(1, 354) = 27.63, p < 0.001, η² = 0.13), and technology acceptance (F(1, 354) = 35.12, p < 0.001, η² = 0.17). These findings demonstrate that combining real-time sensing with AI-guided decision support provides a reproducible, adaptive framework for enhancing health education outcomes in vocational PE settings.

Introduction

Health education is a cornerstone of modern public health, improving individuals' knowledge, attitudes, and health-related behaviors¹. Strengthening self-management skills enables people to make informed decisions, adopt preventive practices, and engage in healthier lifestyles². However, conventional delivery methods, such as classroom lectures, printed materials, and generic online modules, often provide limited interactivity and personalization³. These approaches typically fail to respond to learners' real-time needs, resulting in modest engagement and constrained behavioral change⁴.

In recent years, technology-enhanced models have sought to address these limitations. Mobile e-learning platforms can expand access but usually deliver standardized content without physiological adaptivity⁵. Wearable-only feedback systems capture motion or attention levels but rarely connect sensor data to instructional design⁶. Existing AI-based education tools demonstrate value in analyzing engagement patterns and providing automated feedback, yet most implementations rely on static resources and do not adapt dynamically to learners' states⁷. As a result, there remains a gap in integrating real-time sensing with adaptive instructional methods that can tailor both content and delivery to the learner context.

To bridge this gap, we introduce an AI-guided atomization sensing system that integrates multi-parameter physiological and environmental monitoring into vocational physical education. The system employs wearable sensors capable of detecting respiratory patterns, ambient temperature, and motion signals at a sampling rate of 50 Hz, with a latency of approximately 200 ms, embedded in a lightweight wearable device⁸. Data streams are processed by an AI engine that combines rule-based decision logic with machine-learning models to detect indicators of fatigue or stress⁹. When such triggers occur, the system adapts instructional delivery by, for example, simplifying content complexity or switching to audiovisual demonstrations, thereby sustaining motivation and comprehension¹⁰. Unlike isolated mobile platforms or sensor-only frameworks, this approach unifies sensing, decision-making, and adaptive teaching workflows within a reproducible protocol.

Evidence from related domains supports the potential of this integration. Studies in medical and vocational education show that AI-based analytics improve knowledge retention and engagement when adaptive feedback is provided^11,12. Sensor-enabled learning environments and IoT-driven healthcare frameworks have achieved real-time prediction accuracy improvements of up to 36.9%, highlighting the benefits of multi-parameter data streams for personalized interventions^13,14. Research on wearable and intelligent monitoring networks further demonstrates improved engagement and spatial awareness by capturing behavioral and contextual indicators^15,16. In the context of vocational physical education, personalized instructional models have significantly outperformed conventional teaching approaches (80% vs. 32.5%) in fostering health-promoting behaviors¹⁷. Meanwhile, atomization sensing technologies, initially developed for aerosol therapies and environmental monitoring, have recently been adapted for educational use, enabling high-resolution physiological data capture in real time^18,19. Despite these promising directions, most existing systems remain limited to static e-learning or isolated wearable applications, leaving the integration of atomization sensing and AI-driven adaptivity underexplored.

The present study addresses this gap by proposing a reproducible protocol that combines wearable atomization sensing with AI-guided instructional adaptation in vocational physical education. Specifically, the protocol evaluates its impact on five key outcomes: knowledge retention, engagement, behavioral intention, self-efficacy, and technology acceptance.

Protocol

This study was reviewed and approved by the Institutional Review Board of Shijiazhuang Institute of Railway Technology. All procedures complied with the Declaration of Helsinki and relevant local regulations. The equipment and software used are listed in the Table of Materials.

1. Methodology

This study used a quasi-experimental design to evaluate an AI-guided atomization sensing system for digital health education in higher-education settings. Data were collected before and after the intervention to assess knowledge retention, engagement, behavioral intention, self-efficacy, and technology acceptance using validated instruments adapted from prior work. The overall workflow of sensing, AI decision-making, and adaptive instructional delivery is illustrated in Figure 1, which outlines how physiological and environmental signals are integrated into the rule-based and machine-learning components to adjust content in real-time.

2. Statistical analyses

Data analysis was performed using SPSS version 25, with significance set at p < 0.05. Four statistical approaches were applied:

1. ANOVA was used to compare between-group differences across the five outcome variables, calculated using the formula:
   
   where MS represents the mean square values derived from sums of squares divided by their degrees of freedom.
2. Paired-sample t-tests assessed within-group improvements from pre- to post-test using:
   
   where d is the mean difference, s_d the standard deviation of differences, and n the sample size.
3. Chi-square tests were applied to examine associations between categorical outcomes and group allocation:
   
   where O is the observed frequency and E the expected frequency.
4. Pearson correlation coefficients were computed to determine the strength and direction of associations among continuous variables:
   
   ​with values ranging from -1 to 1, where coefficients above 0.5 were interpreted as moderate to strong positive correlations.

3. Participants

A total of 356 participants aged 18-45 years (M = 26.7, SD = 5.4) were recruited from higher education institutions, met the inclusion criteria for age and basic technological literacy, and provided informed consent. Participants were assigned by random-number table to an intervention group (n = 178) receiving adaptive, AI-guided instruction or a control group (n = 178) receiving conventional modules without personalization. Table 1 summarizes baseline characteristics; the distribution of demographic characteristics across participant subgroups is visualized in Figure 2, which complements the tabulated statistics and confirms group comparability at baseline.

4. Apparatus and materials

The platform integrated wearable sensing of respiratory patterns, ambient temperature, and motion with an AI engine combining rule-based logic and SVM classification to trigger adaptive delivery upon signs of fatigue or reduced activity. Sessions were conducted in standardized multimedia classrooms (~50 m²) equipped with projectors and sensor base stations, lasted 45 min, and were delivered twice weekly over six weeks.

5. Measurement instruments

All self-report instruments were administered on a 5-point Likert scale (1 = strongly disagree to 5 = strongly agree), with higher scores indicating stronger endorsement of the target construct. Items were positively keyed unless otherwise specified, and scale scores were computed as the mean of their constituent items.

6. Knowledge retention

A 15-item multiple-choice test aligned with the instructional content was used to assess knowledge (total score range 0-15). Higher scores reflected greater knowledge acquisition. An example item was: "What is the primary energy source during aerobic exercise?"

7. Behavioral intention

Behavioral intention was measured with a 3-item instrument adapted from the Theory of Planned Behavior²⁰. A representative item was: "I intend to adopt the health practices introduced in class during the following week."

8. Engagement

Engagement was evaluated with a 5-item instrument adapted from a validated framework of learning engagement capturing attention, interaction, emotional involvement, persistence, and learning satisfaction²¹. An illustrative statement was: "I was able to stay focused during the learning process."

9. Self-efficacy

Self-efficacy was assessed with a 4-item instrument grounded in the established conceptualization of perceived capability. A sample item was: "I am confident that I can independently complete the health exercises demonstrated in class."

10. Technology acceptance

Technology acceptance was measured with a 6-item adaptation of the Unified Theory of Acceptance and Use of Technology, covering performance expectancy, effort expectancy, social influence, and facilitating conditions. A representative item was: "Using this system can enhance my learning performance."

All instruments demonstrated acceptable to strong internal consistency in the present study: α = 0.87 (Knowledge), 0.82 (Behavioral Intention), 0.85 (Engagement), 0.84 (Self-efficacy), and 0.89 (Technology Acceptance).

Results

Experimental results

To assess the effectiveness of the AI-driven atomization sensing system, multiple statistical analyses were conducted.

The ANOVA findings revealed clear group differences across all five outcomes, with participants in the intervention group achieving consistently higher post-test scores than those in the control group (Table 2; Figure 3).

When within-grou...

Discussion

The findings of this study provide supportive evidence that integrating AI-powered intelligent atomization sensing technology into health education can enhance learning outcomes across multiple dimensions. Compared with the control group, participants in the intervention group exhibited statistically significant improvements in all five measured variables, with the largest gains observed in knowledge retention (F = 18.73, SS = 62.47, p < 0.001), followed by behavioral intention (F = 16.95, SS = 54.32, p < 0.001),...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
AtomWear X100 wearable sensors	Shenzhen AtomTech Co., Ltd., China	X100	Wearable sensor for monitoring respiratory frequency, ambient temperature, and motion signals (sampling rate: 100 Hz, accuracy: ±0.1).
AI Engine (rule-based + SVM classifier)	Custom implementation	Python 3.9 with TensorFlow 2.10 and scikit-learn 0.24	Algorithm for adaptive decision support: integrates sensor data and adjusts instructional delivery.
Behavioral Intention Scale	Adapted from Ajzen (1991), Theory of Planned Behavior	3 items	Assesses intention to adopt health practices.
Engagement Scale	Adapted from Sun et al. (2019)	5 items	Measures attention, interaction, emotional involvement, persistence, and satisfaction.
Knowledge Retention Test (15-item MCQ)	Custom, aligned with instructional content		15-item multiple-choice test on aerobic exercise and health knowledge.
Multimedia Classroom Setup	Standard higher education classrooms		Classroom (~50 m²), projector, base stations for sensor connectivity.
Python	Python Software Foundation	Version 3.9	Programming environment for AI engine and preprocessing.
scikit-learn	Open source	Version 0.24	Machine learning library for SVM classification.
Self-Efficacy Scale	Adapted from Bandura (1997)	4 items	Assesses confidence in performing health exercises.
SPSS Statistics Software	IBM Corp., Armonk, NY, USA	Version 25	Statistical analysis (ANOVA, t-tests, correlation, χ² tests).
Technology Acceptance Scale	Adapted from UTAUT (Venkatesh et al., 2003)	6 items	Covers performance expectancy, effort expectancy, social influence, and facilitating conditions.
TensorFlow	Google Brain	Version 2.10	Deep learning library used in AI engine.