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On the development of an AI performance and behavioural measures for teaching and classroom management

Authors
Andreea I.Niculescu,
Jochen Ehnes,
Chen Yi,
Du Jiawei,
Tay Chiat Pin,
Joey Tianyi Zhou,
Vigneshwaran Subbaraju,
Teh Kah Kuan,
Tran Huy Dat,
Gi Soong Chee,
Kenneth Kwok
Date
Publisher
arXiv
Link
http://arxiv.org/pdf/2506.11143v2
This paper presents a two-year research project focused on developing AI-driven measures to analyze classroom dynamics, with particular emphasis on teacher actions captured through multimodal sensor data. We applied real-time data from classroom sensors and AI techniques to extract meaningful insights and support teacher development. Key outcomes include a curated audio-visual dataset, novel behavioral measures, and a proof-of-concept teaching review dashboard. An initial evaluation with eight researchers from the National Institute for Education (NIE) highlighted the system's clarity, usability, and its non-judgmental, automated analysis approach -- which reduces manual workloads and encourages constructive reflection. Although the current version does not assign performance ratings, it provides an objective snapshot of in-class interactions, helping teachers recognize and improve their instructional strategies. Designed and tested in an Asian educational context, this work also contributes a culturally grounded methodology to the growing field of AI-based educational analytics.
Study at a glance
  • Question: How can AI use classroom sensors to measure teacher behaviors and support teacher development?
  • Setting and data: Multimodal classroom sensor data from an Asian educational context, evaluated with eight education researchers across a two-year project.
  • Method: Tool development and an initial evaluation with eight researchers.
Why it matters

Extracting meaningful insights from classroom dynamics and teacher actions is needed to support teacher development. Developing automated measures addresses the challenge of manual review workloads while providing culturally grounded methods for AI-based educational analytics.

Key findings
  • In an Asian educational setting, eight institute researchers found that an AI teaching review dashboard was clear, usable, and non-judgmental.
  • The automated classroom analysis reduced manual evaluation workloads and encouraged constructive reflection.
  • The tool provides an objective snapshot of classroom interactions without assigning performance ratings, helping teachers recognize and improve instructional strategies.

Summary drafted with AI from the paper’s abstract and may contain errors. See the full paper for details.

What is the application?
Teaching – Professional Learning
Who is the user?
Educator
Who age?
Post-Secondary
Why use AI?
Efficiency,
Other
Study design
Descriptive – Product Development,
Technical – Computational
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