Teachers’ AR Readiness AR Integration in Science AR Readiness in Primary Education.-Dr. Mohamed Fahmy Mansour.Egypt.
“The Reality of Science Teachers’ Readiness to Integrate Augmented Reality (AR) Technologies in Primary Stage Instruction”
Prepared by:
Dr. Mohamed Fahmy Rashad Mansour
Associate Professor
2026
- Full Research Abstract
Title: The Reality of Science Teachers’ Readiness to Integrate Augmented Reality (AR) Technologies in Primary Stage Instruction
Author: Dr. Mohamed Fahmy Rashad Mansour
Abstract:
The rapid evolution of Educational Technology (EdTech) demands a paradigm shift in pedagogical practices, particularly in primary science education
Where abstract concepts require concrete visual representation. This study investigates the reality of primary school science teachers’ readiness to integrate Augmented Reality (AR) technologies into their instructional practices. Utilizing a descriptive-analytical survey methodology, the research assesses readiness across three primary dimensions: technological skills, pedagogical belief and attitude, and infrastructural availability.
The sample comprises randomly selected primary science teachers who responded to a validated, multi-section questionnaire alongside semi-structured interviews to capture qualitative nuances. Quantitative data were analyzed using descriptive statistics ($t$-tests, ANOVA, and mean scores), while thematic analysis was applied to qualitative responses.The preliminary conceptual framework
Indicates that while teachers exhibit highly positive attitudes toward the motivational value of AR, their actual integration readiness is significantly constrained by a lack of specialized professional development, limited technical support, and structural deficiencies in digital infrastructure. Furthermore, the study explores potential statistically significant differences in readiness attributed to gender, teaching experience, and previous ICT training
The findings yield critical insights for educational policymakers, curriculum designers, and professional development providers, offering a strategic roadmap to bridge the gap between technological potential and classroom reality in early science education.Keywords: Augmented Reality ($AR$), Teacher Readiness, Science Instruction, Primary Education, Educational Technology.
- Comprehensive Research Plan)Section 1: Introduction and Backgroun IntroductionThe contemporary educational landscape is undergoing unprecedented digital transformation, accelerated by advancements in immersive technologies. Among these innovations, Augmented Reality ($AR$) has emerged as a powerful pedagogical tool capable of superimposing digital information—such as 3D models, animations, and interactive data—onto the physical environment
. In the domain of science education, where conceptual understanding often depends on visualizing sub-microscopic, cosmic, or abstract phenomena (e.g., molecular structures, planetary orbits, or anatomical systems), $AR$ offers an experiential learning bridge
However, the pedagogical efficacy of any educational technology is fundamentally contingent upon the human element: the classroom teacher. The transition from traditional instructional methods to technology-enhanced learning environments requires holistic “teacher readiness.” Readiness is not merely the possession of technical skills; it is a complex construct encompassing technological literacy, pedagogical beliefs, psychological acceptance, and institutional support. In the primary educational stage, where foundational cognitive schemas are formed, evaluating teachers’ readiness to deploy
AR
Is critical to ensuring that technology serves as a cognitive catalyst rather than a
Classroom distraction.
1.2 Statement of the Problem
Despite the theoretical affordances of Augmented Reality in enhancing student engagement and spatial visualization in science, its systemic integration within primary school classrooms remains fragmented. Millions are invested globally in purchasing educational software and hardware, yet many tools remain underutilized. Preliminary observations and literature indicate a substantial gap between the availability of advanced EdTech and teachers’ actual capacity or willingness to implement it.
Many primary science teachers face a dual challenge: they must decode complex scientific curricula for young minds while simultaneously navigating complex digital interfaces. Without a clear diagnostic understanding of where teachers stand regarding their technological, psychological, and logistical readiness, structural investments in $AR$ are highly susceptible to failure. Therefore, this study addresses this gap by empirically investigating the current reality of primary science teachers’ readiness to integrate $AR$ in their teaching.
1.3 Research QuestionsThis study seeks to answer the following main overarching question:What is the reality of primary school science teachers’ readiness to integrate Augmented Reality ($AR$) technologies in their instruction?From this main question, the following sub-questions branch out:What is the level of technological capability among primary science teachers regarding the operation of $AR$ applications?
What are the attitudes and pedagogical beliefs of primary science teachers toward utilizing $AR$ in science classrooms?To what extent does the current school infrastructure support the seamless integration of $AR$ tools?Are there statistically significant differences ($\alpha \le 0.05$) in teachers’ readiness levels attributable to demographic variables (gender, years of experience, and prior technology training)?
1.4 Research ObjectivesTo diagnose the actual levels of technical and pedagogical readiness among primary science teachers regarding $AR$ integration.To uncover the underlying psychological attitudes and beliefs that influence teachers’ adoption or rejection of immersive technologies.To evaluate the systemic and infrastructural challenges that impede the deployment of $AR$ in primary schools.
To provide data-driven recommendations for policymakers and professional development designers to optimize teacher training paradigms.
Practical Value: Provides educational administrators with an empirical diagnostic tool to assess workforce readiness before launching large-scale digital curricula. It also assists curriculum developers in designing
AR
-compatible science textbooks that align with teachers’ actual digital competencies.
Section 2: Theoretical Framework & Literature Review2.1 Theoretical FrameworkThis study is theoretically grounded in two primary models of technology adoption:The TPACK Framework (Technological Pedagogical Content Knowledge): This framework asserts that effective technology integration requires an intersection of three core forms of knowledge: Content Knowledge ($CK$), Pedagogical Knowledge ($PK$), and Technological Knowledge ($TK$). Investigating $AR$ readiness means examining how teachers
Blend their scientific knowledge with $AR$ tools to create superior pedagogical experiences ($TPACK$).The Technology Acceptance Model (TAM): Developed by Davis, $TAM$ posits that Perceived Usefulness ($PU$) and Perceived Ease of Use ($PEU$) are the primary determinants of an individual’s behavioral intention to use a new technology. This study utilizes $TAM$ to evaluate how teachers’ attitudes govern their actual classroom readiness
Teacher Readiness Barriers: Extrinsic barriers (First-Order barriers) such as lack of devices, poor internet bandwidth, and rigid schedules frequently conflict with intrinsic barriers (Second-Order barriers) such as low technological self-efficacy and technophobia.
Demographic Variables in EdTech Adoption: The literature presents contradictory findings regarding age and gender; some studies show younger educators adapt faster to immersive tech, while others indicate that targeted professional development neutralizes age-based differences.
Section 3: Methodology and Research Design
3.1 Research Design
This study adopts a descriptive-analytical mixed-methods design. The quantitative approach is prioritized to establish broad trends and levels of readiness across a large sample size, while qualitative data gathered via interviews provide deep contextual insights into the institutional and psychological barriers faced by teachers
3.2 Population and Sample
The target population consists of all primary school science teachers operating within the designated educational directorates during the 2025/2026 academic year. A stratified random sample of approximately 200 teachers will be selected for the quantitative survey. From this cohort, 15 teachers will be purposely selected for the qualitative semi-structured interviews based on varying levels of experience and tech-savviness.
3.3 Data Collection InstrumentsTo ensure holistic measurement, two distinct tools are utilized:The AR Readiness Questionnaire (Quantitative): A 5-point Likert scale instrument divided into four main domains:Domain A: Technological Literacy and $AR$ Familiarity (10 items)Domain B: Pedagogical Beliefs and Attitudes (10 items)Domain C: Perceived
Institutional & Infrastructural Support (8 items)
Semi-Structured Interview Protocol (Qualitative): Consisting of open-ended questions designed to explore teachers’ personal anxieties, perceived systemic constraints, and success stories regarding interactive technology
3.4 Validity and ReliabilityValidity: The questionnaire will be reviewed by a panel of seven experts in educational technology, curriculum design, and methods of teaching science. Items will be adjusted based on their Content Validity Index ($CVI$).Reliability: A pilot study involving 30 non-sample teachers will be conducted to compute Cronbach’s Alpha ($\alpha$) for internal consistency. A reliability coefficient of $\alpha \ge 0.80$ will be considered acceptable
Section 4: Data Analysis and Proposed Procedures4.1 Statistical AnalysisQuantitative data will be processed using statistical software (e.g., SPSS). The following mathematical and statistical tests will be executed:Descriptive Statistics: Calculation of arithmetic means ($M$), standard deviations ($SD$), frequencies, and percentages to rank readiness dimensions.Inferential Statistics:Independent Samples $t$-test to determine variations based on gende
One-Way Analysis of Variance ($ANOVA$) followed by Post-Hoc tests (e.g., Scheffé or Tukey) to explore variations across experience levels and levels of training
4.2 Qualitative Analysis
The qualitative transcripts from the semi-structured interviews will undergo thematic analysis. The process will involve generating initial codes, grouping codes into broader categories, and extracting definitive themes that explain the “why” behind the quantitative metrics.
5.2 Key References (Indicative Selection)
Akçayır, M., & Akçayır, G. (2017). Advantages and challenges associated with augmented reality for education: A systematic review of the literature. Educational Research Review, 20, 1-11.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.
.
Koehler, M. J., & Mishra, P. (2009). What is technological pedagogical content knowledge (TPACK)?. Contemporary Issues in Technology and Teacher Education, 9(1), 60-70.
Radu, I. (2014). Augmented reality in education: a meta-review and cross-media analysis. Personal and Ubiquitous Computing, 18(6), 1533-1543.