Integrating AI and EdTech into Inclusive Learning
A Cross-Regional Study of Russia and Kazakhstan
Bibliographic Data
| ID | 19483400 |
|---|---|
| Authors | Olga Ergunova (0000-0002-1714-7784, Peter the Great St. Petersburg Polytechnic University, corresponding author), Gaini Mukhanova (0000-0002-6953-2756, Astana Medical University), Aruzhan Abdybayeva (State Intellectual Property Office), Andrei Somov (0009-0004-2592-9198, Peter the Great St. Petersburg Polytechnic University) |
| Year | 2026 |
| Volume | 15 |
| Issue | 3 |
| Pages | 199 |
| Publication date | 2026-03-18 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Social Sciences (JOURNAL) |
| Journal identifiers | ISSN: 2076-0760 • E-ISSN: 2076-0760 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/socsci15030199 |
| OpenAlex | W7138957523 |
| Language | EN |
| References cited | 14 |
This article evaluates how artificial intelligence (AI) and educational technology (EdTech) support inclusive learning in Russia and Kazakhstan, two Eurasian countries that share post-Soviet educational legacies but differ in their levels of digital infrastructure and teacher preparedness. Using an asymmetric mixed-methods design, the study draws on a primary survey of N = 2570 educators and staff in four Russian cities (Moscow, Saint Petersburg, Yekaterinburg, Novosibirsk; October–December 2024; response rate 59.8%) and secondary policy/indicator analysis for Kazakhstan. Russia exhibits higher broadband access, AI/EdTech platform adoption, and teacher digital skill levels compared with Kazakhstan. Structural equation modeling (SEM; SmartPLS 4.1) tested four latent constructs—learning environment (LE), general digital competencies (HCg), specialized AI skills (HCs), and inclusion (I)—with satisfactory validity (AVE > 0.5; HTMT ≤ 0.85). A three-stage Measurement Invariance of Composite Models (MICOM) procedure confirmed configural, compositional, and full mean/variance invariance across Russian city subgroups, enabling pooled path analysis. Kazakhstan indicators from secondary sources are discussed as a descriptive benchmark. Semi-structured interviews with 24 stakeholders (12 Russia, 12 Kazakhstan; March 2025; analyzed with NVivo 14) revealed four themes: policy coherence, teacher readiness, infrastructure access, and ethical AI governance. Key SEM paths were LE → HCg (β = 0.278), HCg → HCs (β = 0.652), and HCs → I (β = 0.188), all p
Descriptive statistics · Mediation · Structural equation modeling · Digital literacy in education · Educational Leadership and Innovation · Global Educational Policies and Reforms
| Citation velocity | historical |
|---|---|
| Highly cited | No |