TY - JOUR
T1 - Navigating AI Integration Into Higher Education
T2 - A Study of the Dynamics Influencing Early-Career University Teachers’ Readiness to Integrate AI in Education
AU - Wang, Xinghua
AU - Jiang, Xinyu
AU - Wei, Wei
AU - Tan, Seng Chee
AU - Sun, Daner
AU - Zhang, Zaipeng
N1 - Publisher Copyright:
© The Author(s) 2026
PY - 2026
Y1 - 2026
N2 - Despite growing interest in AI in higher education, little is known about how early-career university teachers perceive and engage with it. This study addresses this gap by examining their perspectives and readiness to integrate AI into academic work. Fifty-seven teachers with less than one year of experience participated in semi-structured interviews and were classified into four readiness groups: Ready, Preparing, Not Ready, and Neutral/Indifferent. Epistemic Network Analysis was used to uncover the cognitive and relational dynamics shaping their strategies. Teachers in the Ready group demonstrated a balanced understanding of AI and regarded its adoption as both necessary and inevitable. The Preparing group recognized its complexities and actively considered technical and ethical implications. The Not Ready group lacked experience and expressed skepticism, often shaped by ethical concerns. The Neutral/Indifferent group showed uncertainty, heavy reliance on external support, and a limited sense of AI’s relevance. By highlighting these differentiated perspectives, this study reveals how early-career teachers navigate AI integration, pointing to diverse needs across readiness levels. Given AI’s transformative role in higher education, the findings stress the urgency of tailored support and professional development to help new educators develop the confidence, literacy, and ethical awareness needed for effective AI use.
AB - Despite growing interest in AI in higher education, little is known about how early-career university teachers perceive and engage with it. This study addresses this gap by examining their perspectives and readiness to integrate AI into academic work. Fifty-seven teachers with less than one year of experience participated in semi-structured interviews and were classified into four readiness groups: Ready, Preparing, Not Ready, and Neutral/Indifferent. Epistemic Network Analysis was used to uncover the cognitive and relational dynamics shaping their strategies. Teachers in the Ready group demonstrated a balanced understanding of AI and regarded its adoption as both necessary and inevitable. The Preparing group recognized its complexities and actively considered technical and ethical implications. The Not Ready group lacked experience and expressed skepticism, often shaped by ethical concerns. The Neutral/Indifferent group showed uncertainty, heavy reliance on external support, and a limited sense of AI’s relevance. By highlighting these differentiated perspectives, this study reveals how early-career teachers navigate AI integration, pointing to diverse needs across readiness levels. Given AI’s transformative role in higher education, the findings stress the urgency of tailored support and professional development to help new educators develop the confidence, literacy, and ethical awareness needed for effective AI use.
KW - AI readiness
KW - early-career teachers
KW - epistemic network analysis
KW - higher education
KW - professional development
UR - https://www.scopus.com/pages/publications/105040389562
U2 - 10.1177/07356331261456128
DO - 10.1177/07356331261456128
M3 - Article
AN - SCOPUS:105040389562
SN - 0735-6331
JO - Journal of Educational Computing Research
JF - Journal of Educational Computing Research
ER -