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Artificial Intelligence in Smart Classrooms: A Systematic Literature Review of Applications, Dimensions, and Teacher Roles: Comparison
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Please note this is a comparison between Version 2 by Abigail Zou and Version 1 by Cèlia Llurba.

The integration of Artificial Intelligence (AI) into smart classrooms (SCs) has accelerated in recent years, fostering new forms of interaction, personalization, and data-driven educational decision-making. Despite this growing interest, the literature remains conceptually fragmented, particularly regarding how AI is integrated across the technological, pedagogical, and environmental dimensions of SCs. This systematic literature review aims to provide a structured synthesis of how AI is integrated into SC contexts, their main functions, their relation to these three dimensions, and the teacher’s role in the system. Following PRISMA guidelines, peer-reviewed studies published between 2021 and 2026 were selected from Web of Science and Scopus, yielding a final corpus of 29 studies. The findings showed that AI integration is mostly concentrated in the technological dimension. The pedagogical dimension is linked to personalization, active learning, formative assessment, and instructional adaptation, while the environmental dimension is less developed. Teachers remain central actors who integrate technological tools, interpret the generated data, and mediate pedagogical decisions. Overall, AI-supported SCs are not only defined by technology but also by pedagogical use and teacher mediation.

  • Smart Classroom
  • Smart Learning Environments
  • Artificial Intelligence
  • Artificial Intelligence in education
  • teacher role
  • learning analytics
  • Internet of Things
  • systematic literature review
  • PRISMA
The rapid integration of AI into educational settings has prompted growing scholarly interest in how intelligent systems can transform teaching and learning processes. Among the most notable developments in this field is the emergence of SCs, which have been conceptualized as learning environments that integrate digital technologies to support interaction, adaptability, and the management of educational processes. Ref. [1] defines the “S.M.A.R.T.” classroom (Showing, Manageable, Accessible, Real-time Interactive and Testing) as an environment equipped with technologies that facilitate real-time interaction and collective knowledge construction [2]. Similarly, Ref. [3] states that these environments promote greater connection and interaction between students and teachers while adapting to students’ individual needs. Ref. [4] also emphasizes that intelligent learning environments should incorporate adaptive digital devices capable of adjusting to the environment and providing continuous feedback during learning. Ref. [5] defines the SC as a combination of advanced technologies used to optimize the learning experience for both students and teachers. Ref. [6] highlights the need to differentiate between a classroom equipped with technology and an SC, arguing that the latter requires a holistic integration that is not always achieved in practice. Ref. [7] proposes that smart learning environments strategically integrate technology to improve student learning and are structured around three dimensions: environmental, pedagogical, and technological. The environmental dimension includes architecture, furniture, and indoor environmental conditions; the pedagogical dimension involves teaching–learning processes and support systems; the technological dimension includes hardware, software, devices, and emerging technologies such as AI and the Internet of Things (IoT). In this review, following [8], SCs are understood as smart learning environments that collect and analyze data from students, teachers, the physical classroom, and learning management systems to provide timely and context-aware insights that support decision-making and enhance teaching and learning.
SCs do not manifest uniformly across educational contexts. In early childhood and primary settings, implementations tend to prioritize embodied interaction and physical engagement, foregrounding the environmental and pedagogical dimensions over complex analytics [9[9][10],10], whereas secondary and higher education contexts place greater weight on AI-driven personalization and learning analytics [2,5][2][5]. Diverse learning needs—including those of students with disabilities—further shape how the three dimensions must be configured to ensure accessibility and equity [11], while teacher experience conditions the degree to which AI-generated data can be translated into effective pedagogical decisions [6,12][6][12]. The COVID-19 pandemic further accelerated hybrid and remote configurations, exposing both the potential and the limitations of AI-supported SCs when environmental conditions fall outside institutional control [13,14][13][14]. Taken together, these variables confirm that SC should be understood as a dynamic and context-sensitive construct rather than a fixed technological configuration [3,7][3][7].
Despite the growing body of empirical work on AI in education, there are currently no systematic reviews that focus specifically on the context of SC. Most of the existing literature examines AI-based tools in isolation, without situating them within the broader architecture of smart educational environments or examining how their integration affects the multiple dimensions that characterize these spaces. Furthermore, the role of the teacher in AI-enhanced SCs has received comparatively little attention, despite being a critical factor in mediating the relationship between intelligent systems and learning outcomes.
Ref. [12] describes AI as a core element supporting the interactive and adaptive use of technologies in learning environments. Similarly, Ref. [11] conceptualizes AI as the central component responsible for processing information collected by sensors to improve the educational experience. In the context of Multimodal Learning Analytics (MMLA), Ref. [15] identifies AI as the most frequently exploited technology in the reviewed literature. Ref. [12] identifies fragmentation as a major challenge, noting that SC components are often disconnected and lack a common integrated framework.
The literature remains fragmented and uses different approaches and definitions. Ref. [16] highlighted the predominance of technological approaches and the need to pay greater attention to pedagogical and human dimensions of AI-supported education. A recent review examined AI technologies in SC, mainly focusing on AI techniques, technological maturity, and security or privacy challenges [13]. This review also considers AI applications and functions, but it focuses on their integration into SC from an educational perspective, with attention to the technological, pedagogical, and environmental dimensions and the role of teachers in these settings. This review focuses particular attention on the pedagogical dimension, not as a secondary aspect of SC but as the dimension that connects AI tools with teaching, learning, feedback, and instructional decision-making. From this perspective, technologically advanced classrooms are not necessarily smart unless AI use is aligned with clear pedagogical purposes.

References

  1. Huang, L.S.; Su, J.Y.; Pao, T.-L. A Context Aware Smart Classroom Architecture for Smart Campuses. Appl. Sci. 2019, 9, 1837.
  2. Lui, M.; Slotta, J.D. Immersive Simulations for Smart Classrooms: Exploring Evolutionary Concepts in Secondary Science. Technol. Pedagog. Educ. 2014, 23, 57–80.
  3. Spector, J.M. Conceptualizing the Emerging Field of Smart Learning Environments. Smart Learn. Environ. 2014, 1, 2.
  4. Koper, R. Conditions for Effective Smart Learning Environments. Smart Learn. Environ. 2014, 1, 5.
  5. Mircea, M.; Stoica, M.; Ghilic-Micu, B. Investigating the Impact of the Internet of Things in Higher Education Environment. IEEE Access 2021, 9, 33396–33409.
  6. Ferreira, A.; Lima, D.A.; Oliveira, W.; Bittencourt, I.I.; Dermeval, D.; Reimers, F.; Isotani, S. Exploring Brazilian teachers’ perceptions and a priori needs to design smart classrooms. Int. J. Artif. Intell. Educ. 2024, 35, 914–965.
  7. Palau, R.; Mogas, J. Systematic Literature Review for a Characterization of the Smart Learning Environments. In Propuestas Multidisciplinares de Innovación e Intervención Educativa; Cruz, A.M., Aguilar, A.I., Eds.; Universidad Internacional de Valencia: Valencia, Spain, 2019; pp. 55–71.
  8. Martínez-Ballesté, A.; Batista, E.; Figueroa, E.; Torruella, G.F.; Llurba, C.; Quiles-Rodríguez, J.; Unciti, O.; Palau, R. A proposal for the smart classroom infrastructure using IoT and artificial intelligence. In Proceedings of the 2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC), Tarragona, Spain, 2–4 July 2024.
  9. Han, X.; Liang, Y.; Li, H.; Li, Z. Design of classroom teaching model based on artificial intelligence technology optimization for promoting students’ deep learning in convergent environments. Int. J. Comput. Inf. Syst. Ind. Manag. Appl. 2026, 18, 16.
  10. Alonso-Prieto, V.; Dimitriadis, Y.; Villagrá-Sobrino, S.L.; Ortega-Arranz, A.; Topali, P.; Martínez-Monés, A. Exploring how teacher agency unfolds within the co-design of a smart learning environment-supported learning activity: A case study. J. Inf. Technol. Educ. Res. 2025, 24, 034.
  11. Zhang, H.; Wang, Z.; Zong, S.; Wu, H.; Jiang, R.; Cui, Y.; Li, S.; Luo, H. Impact of intelligent learning environments on perception and presence of hearing-impaired college students: Findings of design-based research. Educ. Technol. Soc. 2024, 27, 352–374.
  12. Dimitriadou, E.; Lanitis, A. A critical evaluation, challenges, and future perspectives of using artificial intelligence and emerging technologies in smart classrooms. Smart Learn. Environ. 2023, 10, 12.
  13. Figueroa, E.; Batista, E.; Palau, R.; Unciti, O.; Ferre, M.; Martínez-Ballesté, A. The Use of Artificial Intelligence Techniques in Smart Classrooms Is in Its Infancy. IEEE Access 2024, 12, 125179–125193.
  14. Tabuenca, B.; Uche-Soria, M.; Greller, W.; Hernández-Leo, D.; Balcells-Falgueras, P.; Gloor, P.; Garbajosa, J. Greening smart learning environments with artificial intelligence of things. Internet Things 2024, 25, 101051.
  15. Ouhaichi, H.; Spikol, D.; Vogel, B. Research trends in multimodal learning analytics: A systematic mapping study. Comput. Educ. Artif. Intell. 2023, 4, 100136.
  16. Zawacki-Richter, O.; Marín, V.I.; Bond, M.; Gouverneur, F. Systematic Review of Research on Artificial Intelligence Applications in Higher Education—Where Are the Educators? Int. J. Educ. Technol. High. Educ. 2019, 16, 39.
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