Technical Challenges of Implementing Artificial Intelligence Tools in Face-to-Face and Virtual Education: A Systematic Review

Document Type : Original Article

Author

Department of Educational Management, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan,

10.22091/jrim.2026.13803.1371

Abstract

The digital transformation of educational systems has positioned Artificial Intelligence (AI) as a cornerstone of innovation. Despite its considerable potential in personalizing learning, optimizing assessment processes, and empowering teachers, the practical implementation of AI faces complex technical barriers. These barriers vary in nature and intensity across face-to-face and virtual learning environments. This study aims to identify and critically analyze the technical challenges of integrating AI tools in both contexts and to propose evidence-based strategies to overcome them. The research employs a Systematic Literature Review (SLR). Reputable databases, including IEEE Xplore, ScienceDirect, and Springer, were searched using specialized keywords and Boolean operators. Studies published between 2020 and 2025 that specifically addressed technical barriers to AI adoption in education were screened according to inclusion and exclusion criteria. The selection process was documented using the PRISMA flow diagram. The analysis revealed six main categories of technical challenges: (1) data-related issues, including privacy, security, quality, and bias; (2) infrastructural challenges, such as high computational power and stable bandwidth requirements; (3) algorithmic and modeling issues, including the opacity of models (black-box problem) and reliability concerns; (4) integration and interoperability challenges with existing Learning Management Systems (LMS); (5) user experience (UX) and user interface (UI) issues for non-technical stakeholders; and (6) challenges of long-term maintenance, scalability, and technical sustainability. Results further indicated that, in virtual settings, network latency and data security are more critical, whereas in face-to-face settings, integration with classroom hardware poses greater challenges.
Successful implementation of AI in education requires a comprehensive and multidimensional approach that extends beyond tool selection. Key strategies include investing in robust infrastructures, establishing data standards, developing explainable AI (XAI) models, designing modular platforms with high interoperability, and providing technical training for teachers. By offering an analytical framework of technical barriers, this study supports IT managers, educational policymakers, and software developers in designing more efficient and realistic AI implementation strategies

Keywords

References

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History

  • Receive Date: 04 September 2025
  • Revise Date: 01 January 2026
  • Accept Date: 10 January 2026

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