February 19, 2026

Artificial Intelligence (AI) is increasingly shaping education not only through learning tools, but also through systems that are able to track attendance, monitor classroom behaviour and assess student engagement. These applications are tied to classroom management, meaning the actions teachers take to create an orderly, supportive learning environment, e.g. by arranging the physical environment, establishing rules, and managing student behaviour. At the same time, AI in this area sits at the center of a dilemma. On the one hand, it faces criticism for possibly causing feelings of constant surveillance and intrusion of privacy. On the other hand, the automation of time-wasting tasks, such as monitoring student attendance, as well as the availability of data driven insights, could support teachers to focus more on pedagogical aspects. 

A cross-institutional team, led by Tim Fütterer (University of Tübingen) with our EdTech Fellow Dr. Babette Bühler and EdTech Director Prof. Dr. Enkelejda Kasneci (Technical University of Munich), conducted a comprehensive synthesis of research on AI for classroom management, highlighting a striking gap between technical innovation and ethical responsibility.

Systematic Review

The authors conducted a PRISMA-guided systematic review of 104 empirical studies (2000–2022) on AI applications intended for classroom management, analyzing:

• Educational purposes (what classroom management aspects AI is used for),
• Technical implementations (what is currently possible from a technical point),
• Ethical considerations (what are privacy, data security, and algorithmic bias issues) 

Results

What AI is used for
Most systems target students and mainly focus on analysing their classroom behaviour by monitoring their attendance, hand raising, sitting, writing and sleeping, as well as cognitive aspects such as their attention and engagement by tracking gaze direction, head pose and facial expressions. 

What are employed methods
Deep learning dominates (especially in more recent work), with many studies feeding raw image/audio from the classroom directly into models. 

Readiness and real-world deployment
Just 49% of approaches are described as scalable beyond a specific scenario. Moreover, while 46% of studies presented offline applications, only 29% reported online approaches for real-time classroom use.

Ethics and Privacy Lag behind
Despite the sensitivity of classroom data, only 22% of studies explicitly discuss ethical/data security issues, and just 13% report any privacy-preserving measures. 

Takeaway Message

While many AI classroom-management systems are reported as technically effective for monitoring and enhancing student engagement, ethical mindfulness, privacy, and data security are not keeping pace. The authors therefore emphasise the need of balancing innovation with responsible, transparent practice. 

Developers need greater awareness that data protection should be built into software from the very beginning. At the same time, researchers should raise standards by securing ethical and data-collection permits and by defining clear policies for data storage and anonymisation. Beyond technical and procedural safeguards, the authors stress the role of education: teachers, parents, and students should be supported in developing critical thinking about AI, including its privacy implications, the risk of bias, and how AI outputs should be interpreted and used. To strengthen accountability, the review also suggests that journals require ethical permits and data-security statements and calls for further research into AI’s efficiency, acceptance, and impact on learning outcomes. Finally, it underlines the importance of transparency by avoiding “black-box” evaluation and carefully considering options such as opt-in or opt-out mechanisms, and local versus server-based processing.

References

• Fütterer, T., Goldberg, P., Bühler, B., Sikimić, V., Trautwein, U., Gerjets, P., Stürmer, K., Kasneci, E. (2025). Artificial intelligence in classroom management: A systematic review on educational purposes, technical implementations, and ethical considerations. Computers and Education: Artificial Intelligence, Volume 9, https://doi.org/10.1016/j.caeai.2025.100483