Study Links AI Technostress to Lower Professional Identity Among Chinese University Teachers, Organizational Support Buffers Path

An original research article in Frontiers in Psychology surveyed 486 full-time teachers from five public universities in Guangxi, China, all of whom had used at least one generative AI tool in teaching or course preparation. The study measured AI technostress, teaching self-efficacy, professional identity, and perceived organizational support, using confirmatory factor analysis and a moderated mediation path analysis with 5,000 bootstrap resamples. AI technostress was negatively associated with professional identity, teaching self-efficacy partially mediated this association, and perceived organizational support moderated the first link of the pathway.

The study provides a testable mechanism for how universities can support teachers as generative AI becomes embedded in teaching: professional identity may be vulnerable to technology-related strain, and institutional support and efficacy-oriented professional development may attenuate this vulnerability. For school administrators and teacher training designers, this means AI teaching initiatives need organizational support and teacher efficacy alongside tool deployment. The cross-sectional convenience sample limits causal or long-term inferences. Universities advancing generative AI teaching need to include teacher technostress and professional identity in support systems, not just tool training. Teacher professional development programs can prioritize building teaching self-efficacy alongside visible institutional support measures.