Reshaping Teachers' Roles in Intelligent Physical Education and Job Crafting: A Multimethod Approach

Summary

This protocol details a mixed-methods approach to investigate the cognitive, task-based, and relational dilemmas physical education teachers face when adapting their roles within Intelligent Physical Education environments and proposes strategic relief paths.

Abstract

This study examines the dilemmas physical education teachers face in reshaping their professional roles within Intelligent Physical Education (IPE) and proposes evidence-based relief strategies. Using a mixed-methods approach, a simple random sampling technique was employed for the quantitative phase, resulting in a usable sample size of N = 126 respondents. Moreover, for the qualitative phase, a purposive sampling strategy was employed, and five physical education teachers were selected for interviews. We integrated quantitative survey data with in-depth qualitative interviews to explore dimensions of job crafting. Our findings reveal significant cognitive, task-based, and relational challenges in adapting to innovative educational environments. Quantitative analysis reveals crucial predictive factors influencing these adaptation challenges, while qualitative data highlight insufficient understanding of AI applications, inadequate institutional support, and deficient technological infrastructure as key barriers. Teachers also expressed concerns about maintaining meaningful teacher-student relationships amid technological transformation. The study demonstrates that teachers' successful adaptation depends on systemic support rather than individual effort alone. We propose comprehensive strategies, including conceptual training, infrastructure development, and ethical guidelines, to facilitate effective job crafting. These findings contribute to understanding educator adaptation in technologically transforming educational landscapes and offer practical guidance for implementing intelligent physical education reforms.

Introduction

The development of artificial intelligence is radically transforming education because of its rapid development¹. The national plans promote this change, including the plans of China by New Generation Artificial Intelligence Development Plan and China Education Modernization 2035². This change is not the use of technology as a mere tool in the field of physical education (PE). Its objective is to develop intelligent solutions that allow individual training of students, feedback in real-time, and health monitoring³. This new area is called Intelligent Physical Education (IPE), where data provided by AI and the Internet of Things⁴ helps create dynamic and personalized learning environments^5,6. This technological focus, however, tends to ignore one of the central human factors: the teacher. It is evident that the existing debate on IPE places excessive focus on technical systems and student outcomes, and the role of the teacher in this new environment remains uncertain⁷. This brings a serious gap. Teachers are not just the gears of new technology: their professionalism and pedagogical considerations, as well as their motivational relations with students, cannot be substituted. Thus, the issue this study will focus on is not entirely technical⁸.

It is a human-related issue: How are PE teachers adjusting to and being influenced by the introduction of intelligent systems? What are their new skills required and what are their challenges? The implementation of IPE without awareness of the experience of the teacher will pose a risk of developing friction, resistance or a deprofessionalizing effect where technology will override teacher authority⁹. In the current research, the author suggests that to become successful and create a really high-quality system of education¹⁰, IPE should proactively embrace and assist the teachers at the core of the system¹¹. The study will thus aim at addressing the pedagogical, psychological, and professional aspects of the role of the teacher in the intelligent PE ecosystem¹². As learning institutions all over the world move fast in the purchase and implementation of such innovative technologies, the emphasis has been more on the technical base and student performance¹³. However, what is critical and yet is often neglected is the human participant in this reformation: the physical education teacher¹⁴.

There has also been extensive literature on the technological aspects of innovative education¹⁵. The possibilities of wearable technology and virtual reality, as well as online environments, to improve the student engagement and learning of Motor skills were well explored by scholars¹⁶. In parallel, a related line of inquiry has concentrated more generally on the challenges of teacher professional development in the digital era, with frequent reference to change resistance and digital literacy¹⁷. Nevertheless, there is a sizeable gap between these disciplines¹⁸. On the one hand, we are familiar with the technology and its overall effects on pedagogy; yet, on the other, we do not have a detailed understanding of how the very nature of the work and role identity of a physical educator is changing in the context of IPE (in terms of the specifics of reconfiguration)^19,20.

This study applies job crafting theory to Intelligent Physical Education (IPE) to examine how teachers actively reshape their roles in response to technological change. It explores the cognitive, task-based, and relational dilemmas that constrain teachers' job crafting and identifies pathways to ease them, emphasizing teacher agency rather than passive adoption or resistance. The research extends job crafting beyond business to the hands-on context of IPE, offering a more nuanced framework for understanding role reshaping and providing practical strategies to support teachers' transitions. Using a multi-method approach, it develops an empirically grounded typology of dilemmas and relief paths, advancing understanding of the human factors that determine technology's success. These insights are valuable for administrators, policymakers, and teacher educators seeking to protect teacher well-being and sustain professional identity during the digital shift.

Research objectives:
1. To diagnose the primary types of job-crafting dilemmas (cognitive, task-related, and relational) faced by PE teachers in the context of IPE implementation.

2. To identify the key predictive factors (e.g., technological self-efficacy, institutional support, teaching experience) that influence the intensity of these dilemmas.

3. To explore teachers' lived experiences and adaptive strategies in response to the challenges and opportunities presented by AI-driven educational tools.

Protocol

This study was conducted in accordance with recognized ethical guidelines for social science research. Prior to commencement, the research protocol, including the informed consent forms, survey instrument, and interview questions, was reviewed. All participants were provided with a detailed information sheet outlining the study's purpose, the voluntary nature of their participation, their right to withdraw at any time without penalty, and the measures in place to ensure anonymity and confidentiality. Written informed consent was obtained from every participant before data collection began.

Ensuring the best ethical standards possible was the priority in this study. The rule of anonymity was followed; all data were anonymized at the transcription and analysis stages, and any point of identification included was eliminated. Pseudonyms refer to participants in this manuscript. To address confidentiality, all digital data was stored on a password-protected secure server. In contrast, physical records were stored in a locked cabinet to which only the primary researchers had access. The interview was conducted in a confidential setting where the participants felt comfortable and were free to express their opinions. Moreover, the researchers also took the stance of reflexivity, whereby their views were also taken into consideration, and the voices and experiences of the respondents were depicted without bias or compromise of their viewpoints.

Research design
To achieve a comprehensive understanding of the research problem, this study employed a sequential mixed-methods research design. This approach integrates quantitative and qualitative data collection and analysis within a single study to provide a more complete and nuanced analysis than either method could alone²¹. The quantitative phase involved a cross-sectional survey to gather broad, generalizable data on the prevalence and nature of job-crafting dilemmas among physical education teachers. This was followed by a qualitative phase consisting of in-depth, semi-structured interviews. The purpose of this second phase was to elaborate, clarify, and contextualise the statistical findings, exploring the underlying reasons and personal experiences behind the quantitative trends. The combination of these methods allowed for triangulation, where the convergence of findings from different data sources strengthens the validity and depth of the conclusions.

Population and sampling
The target population for this study was in-service physical education (PE) teachers in secondary schools and universities within the Zhejiang province of China, who are involved in or exposed to initiatives in intelligent physical education. A sequential mixed-methods sampling strategy was employed. For the quantitative phase, a stratified random sampling technique was used to enhance representativeness. The sampling frame was constructed by first identifying major urban and suburban school districts within Zhejiang, as well as universities with physical education programs. Schools were then randomly selected from these lists. Within each selected institution, PE teachers were invited to participate. This approach ensured a broad and varied cross-section of the provincial teaching workforce. A total of 150 questionnaires were distributed, yielding 126 valid responses, resulting in a high response rate of 84% (Table 1). The demographic profile (presented in Table 2) reflects a sample with varied experience; notably, the largest group (41.3%) had 6-10 years of teaching experience. The sample was predominantly male (72.2%), reflecting a gender distribution common in the field within the region. It is essential to clarify that the reported proportion of respondents holding doctoral degrees (20.8%) pertains specifically to the university-level instructors and senior academic staff within the sample, which included faculty from teacher training programs at institutions such as Zhejiang Normal University and Hangzhou Normal University. This is not representative of frontline schoolteachers, among whom such qualifications are less common. For the subsequent qualitative phase, a purposive sampling strategy was used to select information-rich cases from the survey respondents who agreed to further contact. Five PE teachers were selected to ensure maximum variation in gender, teaching experience, and institutional level (secondary vs. higher education) (Table 3). This strategic selection aimed to capture diverse perspectives, from early-career schoolteachers adapting to new technologies to experienced university educators navigating role transformation²².

Data collection procedure
First, quantitative data were gathered using a self-designed questionnaire titled "Survey on the Influence of Artificial Intelligence on Job Crafting of Physical Education Teachers." Before widespread distribution, the instrument was piloted with 36 teachers (not included in the main study) to assess clarity, reliability, and validity. The finalized questionnaire was then administered to the stratified random sample of 150 teachers. The survey was designed to measure key dimensions of job-crafting dilemmas cognitive, task-related, and relational, within the context of intelligent sports education. Following the preliminary analysis of the survey data, the second, qualitative stage was initiated. Following the quantitative analysis, the second stage involved in-depth, semi-structured interviews designed to explore and contextualize the specific job-crafting dilemmas identified in the survey. The primary researcher, who had no prior relationship with any participants, contacted five respondents from the survey sample who had consented to follow-up contact. Initial contact was made via the official email or institutional communication channel provided during the survey, extending a formal invitation that reiterated the study's purpose and ethical assurances.

All interviews were conducted formally, following a predesigned protocol. They were held in a private, neutral setting such as a reserved meeting room at the participant's institution to ensure confidentiality and comfort. The interviews were conducted in Mandarin Chinese, the participants' native language. Each session lasted between 45 and 70 min, with an average duration of approximately 55 min. The conversations were guided by an open-ended question schedule but allowed for flexibility to probe emerging themes. All interviews were audio-recorded using a dedicated digital recorder, with the participant's written consent obtained again at the start of each session. No video recording was used to minimize participant discomfort. To ensure accuracy, the audio files were transcribed verbatim in Mandarin by the researcher shortly after each interview. The transcribed texts were then thematically analyzed, with key illustrative quotes selected for the final manuscript. These quotes were translated into English by the research team, employing a back-translation check with a bilingual expert to verify semantic equivalence.

Data analysis methods
The collected data were analyzed using specialized software to ensure rigor (see Figure 1). The quantitative data from the 126 valid questionnaires were imported into statistical analysis software. Descriptive statistics (frequencies, percentages, means, and standard deviations) were calculated to summarize the demographic characteristics and the general trends in the responses. Reliability analysis (Cronbach's Alpha) was conducted on the scales to confirm internal consistency, and inferential analyses were likely performed to examine relationships between variables. The qualitative data from the interview transcripts were analyzed to facilitate a structured and systematic thematic analysis. This process involved repeatedly reading the transcripts to achieve familiarity, generating initial codes, and then collating these codes into potential themes. The themes were then reviewed, refined, and defined to ensure they accurately represented the data collected. This inductive process allowed the core dilemmas, cognitive, task-based, and relational, to emerge directly from the participants' narratives, providing rich, qualitative evidence to support and explain the quantitative findings²³.

Results

Quantitative findings
Descriptive statistics
Descriptive statistics can be described as the introductory phase to the quantitative data analysis process, a quick overview of the fundamental characteristics of the information in the study. They are vital in estimating the normative nature (variation or distribution), central tendency, and variability of the sample, which enables readers to understand how applicable the research results are and to find the context within w...

Discussion

Discussion on quantitative findings
The insightful quantitative results of this research provide empirical evidence that sheds light on the multifaceted nature of the job-crafting dilemma among physical education teachers in intelligent education. The regression analysis provides the most robust and nuanced insights, revealing that perceived institutional support and technological self-efficacy emerge as the most statistically significant and influential predictors of teachers' cognitive dilemm...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Computer with AI Support Tools		—	Required for data analysis and AI-based lesson design
Digital Audio Recorder			Used to record teacher interviews
Intelligent Assessment System		—	Preferred AI application mentioned by teachers
Intelligent Education Platform		—	Mentioned as part of AI teaching tools used by teachers
Interview Protocol Document	Self-developed	—	Semi-structured guide for interviews
Learning Analytics Platform		—	Referenced as an AI-enabled teaching analysis tool
NVivo	QSR International	Version 12	Used for qualitative thematic coding
Online Questionnaire	Self-developed	—	Used to collect responses from 126 PE teachers
SPSS (Statistical Software)	IBM	Version 27	Used for quantitative data analysis