# Artificial intelligence and Chinese university teachers’ work performance: a synergistic or adversarial relationship

## TL;DR

This study explores how AI adoption affects Chinese university teachers' work performance and relationships with leaders, finding both challenges and opportunities.

## Contribution

The study introduces a novel analysis of AI's impact on leadership and performance in Chinese higher education.

## Key findings

- AI awareness among teachers is negatively correlated with leader-member exchange (LMX).
- Servant leadership positively influences LMX, which in turn reduces turnover intention and improves work performance.

## Abstract

With the widespread adoption of AI by Chinese university teachers in their work processes, an increasing number of complexes work this study aims to examine are being handled by AI. Under these circumstances, as traditional knowledge workers, Chinese university teachers may develop concerns about their career prospects, leading to negative work attitudes and pessimism, which could ultimately affect their work performance.

Based on the knowledge worker’s perspective of relationship between leader and subordinates and through a self-administered survey, valid questionnaires were collected from 423 Chinese university teachers working in 64 Chinese universities, and partial least squares structural equation modeling (PLS-SEM) was employed for data analysis.

In the result, the study reveals a negative correlation between Chinese university teachers’ AI awareness and LMX, as well as a positive association between servant leadership and LMX. Furthermore, it demonstrates that Chinese university teachers’ LMX is negatively related to turnover intention, which in turn shows a negative relationship with work performance.

Against the background of widespread AI adoption in China, this research provides both theoretical implications and practical suggestions for managing, motivating, and inspiring Chinese university teachers to enhance their work performance and thereby improve organizational performance.