Artificial Intelligence Applications in Primary Education: A Quantitatively Complemented Mixed-Meta-Method Study
Abstract
:1. Introduction
1.1. Artificial Intelligence in Education
1.2. Artificial Intelligence in Elementary Schools
1.3. Purpose and Significance of This Study
- What are teachers’ perspectives within the scope of this meta-analysis (quantitative dimension)?
- What are teachers’ perspectives within the scope of this meta-thematic analysis (qualitative dimension)?
- What are teachers’ perspectives within the scope of the Rasch measurement model (quantitative dimension)?
- Determining the overall effect size of different variables on AI applications;
- Assessing the effect size of different variables on AI use based on subject area, the duration of implementation, and sample size.Within the scope of the meta-thematic analysis, the outlined was performed:
- Identifying the impact of AI applications on learning environments and determining potential challenges and solutions in AI implementation;
- Conducting a general analysis of teachers’ opinions on AI applications;
- Analyzing the leniency or strictness of evaluators (jury members);
- Performing a difficulty analysis of AI-related assessment items (criteria).
2. Methods
- Meta-analysis: a quantitative synthesis of data to determine the effect size of AI applications;
- Meta-thematic analysis: a qualitative examination of recurring themes in the literature, focusing on the effects of AI applications in educational contexts;
- The Rasch measurement model: a quantitative analysis of participant opinions, providing insights into teacher perspectives and evaluating response consistency.
2.1. Meta-Analysis Process
2.1.1. Data Collection and Analysis
2.1.2. Effect Size and Model Selection
2.1.3. Moderator Analysis
2.1.4. Publication Bias
2.2. Meta-Thematic Analysis Process
2.2.1. Data Collection and Review
2.2.2. Coding Process
2.2.3. Reliability in the Meta-Thematic Analysis Process
2.3. Rasch Measurement Model Analysis Process
2.3.1. Study Group
2.3.2. Research Data and Analysis
3. Findings
3.1. Meta-Analysis Findings on AI Applications
3.2. Meta-Thematic Findings Regarding Artificial Intelligence Applications
3.3. Findings Related to the Rasch Measurement Model for Artificial Intelligence Applications
4. Discussion and Conclusions
4.1. Results of the Meta-Analysis Process
4.2. Results of the Meta-Thematic Analysis Process
4.3. Results Related to the Rasch Measurement Model Process
4.4. Integrative Results of This Study
4.5. Limitations and Future Research
4.6. Recommendations
- The application duration, subject areas, and sample sizes in AI-related research have significant effects on academic success and the impact of AI on educational environments. The use of the mixed-meta method, supported by the Rasch measurement model, has provided a more holistic perspective, allowing for a deeper exploration of the topic. Based on the limitations and findings of this study, the following points are recommended:
- Research on AI applications in primary school subject areas such as art, music, and physical education can be conducted. In addition to quantitative methods, qualitative methods could be employed to explore the effectiveness and applicability of survey questions;
- The meta-analysis phase of this study could include investigations into the impact of AI applications on attitudes and long-term retention;
- Studies could explore teachers’ information and technology competencies [86] within other professional practice areas;
- This study focused on perspectives from classroom teachers. Including evaluators from different expertise levels could broaden the scope of the study;
- Despite teachers’ positive expectations regarding AI, it is essential that they first familiarize themselves with the technology and learn how to integrate it into their classrooms. Many teachers may regard AI as an advanced technological product without prior experience. In this regard, in-service training could increase teachers’ knowledge about AI and improve their integration of this technology, significantly enhancing student success and the learning experience [86];
- Given the methodological diversity, the use of a mixed-meta method combined with quantitative analyses has allowed for a comprehensive examination of the findings, with detailed insights into how various variables affect the use of AI applications. Therefore, it is recommended to apply the mixed-meta method integrated with either qualitative or quantitative analyses in other areas to achieve comprehensive research findings;
- Policymakers should take necessary measures to address concerns related to ethics, data security, and human rights as AI becomes more integrated into education;
- Artificial-intelligence-supported assessment tools are highly effective in monitoring student performance and providing immediate feedback. Educational institutions can make these systems more widespread to reduce teachers’ workload and track students’ progress in more detail;
- For students to succeed in AI-supported learning environments, they need to possess critical thinking, problem-solving, and digital literacy skills. Curriculum adjustments should be made to equip students with these skills.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A. Agreement Value Ranges of Themes Related to Artificial Intelligence Applications
Effect on Learning Environments | Problems Encountered | Related Solution Suggestions | Problems Encountered and Solution Suggestions | ||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
K2 | K2 | K2 | K2 | ||||||||||||||||
K1 | + | - | Σ | K1 | + | - | Σ | K1 | + | - | Σ | K1 | + | - | Σ | ||||
+ | 26 | 2 | 28 | + | 14 | 1 | 15 | + | 12 | 1 | 13 | + | 26 | 2 | 28 | ||||
- | 3 | 18 | 21 | - | 0 | 9 | 9 | - | 1 | 7 | 8 | - | 1 | 16 | 17 | ||||
Σ | 29 | 20 | 49 | Σ | 14 | 10 | 24 | Σ | 13 | 8 | 21 | Σ | 27 | 18 | 45 |
Appendix B. Primary School Teachers’ Artificial Intelligence Applications Evaluation Form
Appendix C. The Content Validity Ratios of the Artificial Intelligence Application Evaluation Items
Item No | Items | Necessary | Useful/Insufficient | Unnecessary | CVC * |
---|---|---|---|---|---|
1 | I can integrate AI-based resources into the curriculum to support course content. | 12 | - | - | 100% |
2 | I use AI applications to assess students’ progress. | 12 | - | - | 100% |
3 | I can update and enrich the curriculum using AI applications. | 11 | 1 | - | 83% |
4 | I can use Al tools to evaluate students’ performance in digital environments. | 12 | - | - | 100% |
5 | When assessing lessons with AI tools, I can better analyze students’ individual learning levels. | 10 | 1 | 1 | 67% |
6 | I can provide individualized feedback to students using AI-based assessment tools. | 11 | 1 | - | 83% |
7 | I can integrate AI applications into pedagogical methods and techniques to support students’ learning processes. | 10 | 1 | 1 | 67% |
8 | I understand how AI applications can support teaching activities in the classroom environment. | 12 | - | - | 100% |
9 | I can effectively use AI tools during lesson planning to provide students with richer learning experiences. | 11 | -- | 1 | 83% |
10 | I can create AI-supported digital content (such as interactive lesson notes, videos) to help students better understand the subject. | 10 | - | 2 | 67% |
11 | I can use Al tools to encourage students’ active participation. | 12 | - | - | 100% |
12 | I can encourage students to collaborate and develop using Al tools. | 10 | 2 | - | 67% |
13 | I can guide students in using AI tools to create projects (such as drawing pictures, creating stories). | 10 | 1 | 1 | 67% |
14 | I understand how to effectively use Al applications in classroom activities. | 10 | 1 | 1 | 67% |
15 | I can use Al tools to facilitate students’ collaborative work. | 10 | - | 2 | 67% |
16 | I can ensure that students use digital tools safely and effectively while conducting research. | 12 | - | - | 100% |
17 | I can help students prepare a presentation describing their surroundings using Al tools. | 10 | - | 2 | 67% |
18 | I can guide students in creating simple designs using Al tools (such as drawing pictures, creating stories). | 10 | 1 | 1 | 67% |
Appendix D. Demographic Information of the Participants
Participant Number | Participant Gender | Professional Seniority | Educational Status |
---|---|---|---|
1 | Female | 19 years | Major |
2 | Male | 18 years | Major |
3 | Female | 24 years | Major |
4 | Male | 23 years | Major |
5 | Female | 19 years | Major |
6 | Female | 22 years | Major |
7 | Male | 36 years | Minor |
8 | Male | 28 years | Major |
9 | Female | 25 years | Major |
10 | Female | 22 years | Major |
11 | Female | 35 years | Minor |
12 | Male | 23 years | Master’s |
13 | Female | 25 years | Major |
14 | Male | 19 years | Major |
15 | Female | 14 years | Major |
16 | Male | 26 years | Master’s |
17 | Male | 34 years | Minor |
18 | Male | 28 years | Major |
19 | Female | 22 years | Major |
20 | Female | 32 years | Major |
21 | Male | 24 years | Major |
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Criteria | Description |
---|---|
Time Period | 2005–2025 |
Publication Language | English and Turkish |
Appropriateness of Teaching Method | Experimental and/or quasi-experimental designed studies with pre-test–post-test control groups using artificial intelligence applications |
Statistical Data | Sample size (n), arithmetic mean (X), and standard deviation (ss) for effect size calculation |
Test Type | Model | 95% + Confidence Intervals | Heterogeneity | |||||
---|---|---|---|---|---|---|---|---|
n | g | Lower | Upper | Q | p | I2 | ||
AA | FEM | 24 | 0.59 | 0.47 | 0.64 | 163.11 | 0.00 | 85.90 |
REM | 24 | 0.51 | 0.28 | 0.74 |
Items | Groups | Effect Size and 95% Confidence Intervals | Null Test | Heterogeneity | ||||||
---|---|---|---|---|---|---|---|---|---|---|
n | g | Lower Limit | Upper Limit | Z-Value | p-Value | Q-Value | df | p-Value | ||
Application duration | 1–4 | 0.59 | 0.59 | 0.30 | 0.88 | 4.01 | 0.00 | |||
5+ | 0.09 | 0.09 | −0.15 | 0.33 | 0.75 | 0.45 | ||||
Unspecified | 0.58 | 0.58 | −0.02 | 1.19 | 1.89 | 0.06 | ||||
Total | 0.32 | 0.32 | 0.14 | 0.50 | 3.54 | 0.00 | 7.69 | 2 | 0.02 | |
Subjects | Maths | 19 | 0.44 | 0.18 | 0.71 | 3.25 | 0.01 | |||
AI | 3 | 0.80 | 0.07 | 1.53 | 2.15 | 0.03 | ||||
Others | 2 | 0.81 | 0.19 | 1.44 | 2.54 | 0.01 | ||||
Total | 24 | 0.53 | 0.30 | 0.76 | 4.47 | 0.00 | 1.7 | 2 | 0.43 | |
Sample size | Small | 6 | 0.50 | 0.09 | 0.90 | 2.40 | 0.02 | |||
Medium | 9 | 0.37 | 0.18 | 0.55 | 3.93 | 0.00 | ||||
Large | 6 | 0.63 | 0.18 | 1.08 | 2.75 | 0.01 | ||||
Total | 24 | 0.42 | 0.26 | 0.57 | 5.24 | 0.00 | 1.29 | 2 | 0.52 |
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Topkaya, Y.; Doğan, Y.; Batdı, V.; Aydın, S. Artificial Intelligence Applications in Primary Education: A Quantitatively Complemented Mixed-Meta-Method Study. Sustainability 2025, 17, 3015. https://doi.org/10.3390/su17073015
Topkaya Y, Doğan Y, Batdı V, Aydın S. Artificial Intelligence Applications in Primary Education: A Quantitatively Complemented Mixed-Meta-Method Study. Sustainability. 2025; 17(7):3015. https://doi.org/10.3390/su17073015
Chicago/Turabian StyleTopkaya, Yavuz, Yunus Doğan, Veli Batdı, and Sami Aydın. 2025. "Artificial Intelligence Applications in Primary Education: A Quantitatively Complemented Mixed-Meta-Method Study" Sustainability 17, no. 7: 3015. https://doi.org/10.3390/su17073015
APA StyleTopkaya, Y., Doğan, Y., Batdı, V., & Aydın, S. (2025). Artificial Intelligence Applications in Primary Education: A Quantitatively Complemented Mixed-Meta-Method Study. Sustainability, 17(7), 3015. https://doi.org/10.3390/su17073015