Article: A long-term system for enhancing teachers' AI application skills based on causal inference and reinforcement learning Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.82 pp.56 - 78 Abstract: Against the backdrop of educational digital transformation, traditional short-term technical training for teachers' AI application skills suffers from poor teaching scenario transferability and diminishing long-term effectiveness due to unclear internal skill formation mechanisms. This study constructs an intervention system integrating causal inference (to identify key influencing factors) and reinforcement learning (to deliver dynamic personalised interventions). A 4-week experiment shows the intervention group achieved an 11.5-point comprehensive improvement (vs. 4.2 points in the control group), with significant gains across all skill dimensions and higher subjective satisfaction. This dual-driven model overcomes static training limitations, providing methodological support for sustainable teacher AI skill development and educational digital transformation. Inderscience Publishers - linking academia, business and industry through research

Title: A long-term system for enhancing teachers' AI application skills based on causal inference and reinforcement learning

Authors: Yuke Zhang

Addresses: Zhengzhou Health College, Zhengzhou 450064, China

Abstract: Against the backdrop of educational digital transformation, traditional short-term technical training for teachers' AI application skills suffers from poor teaching scenario transferability and diminishing long-term effectiveness due to unclear internal skill formation mechanisms. This study constructs an intervention system integrating causal inference (to identify key influencing factors) and reinforcement learning (to deliver dynamic personalised interventions). A 4-week experiment shows the intervention group achieved an 11.5-point comprehensive improvement (vs. 4.2 points in the control group), with significant gains across all skill dimensions and higher subjective satisfaction. This dual-driven model overcomes static training limitations, providing methodological support for sustainable teacher AI skill development and educational digital transformation.

Keywords: causal inference; reinforcement learning; teachers' AI application abilities; dynamic intervention; long-term improvement.

DOI: 10.1504/IJICT.2026.155117

International Journal of Information and Communication Technology, 2026 Vol.27 No.82, pp.56 - 78

Received: 02 Mar 2026
Accepted: 22 Apr 2026
Published online: 27 Jul 2026 *

Title: A long-term system for enhancing teachers' AI application skills based on causal inference and reinforcement learning

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