The Perception Towards the Application of AI-Integrated Teaching Among Lecturers in Jiujiang University, Jiangxi Province

Abstract

This research aimed to (1) examine lecturers’ attitudes, perceived benefits and core concerns towards AI-integrated teaching at Jiujiang University (JJU), a provincial applicationoriented university in inland China; (2) analyze the institutional and individual factors influencing lecturers’ readiness to adopt AI-integrated teaching practices; (3) explore the variations in perceptions and intended AI usage across different academic disciplines and professional ranks; and (4) propose evidence-based institutional support mechanisms and pedagogical frameworks for effective and responsible AI integration in regional higher education contexts. A mixed-methods explanatory sequential design was employed, combining a cross-sectional quantitative survey of 338 full-time lecturers and in-depth semi-structured interviews with 28 purposively selected participants. The research adopted statistical analyses (structural equation modeling, ANOVA, regression) and thematic analysis to process data, with validated scales for construct measurement (Cronbach’s α > 0.80, KMO = 0.876). Major Findings: (1) JJU lecturers exhibit cautious optimism towards AI-integrated teaching, with 76.3% recognizing its educational potential but only 34.9% actively integrating AI into core pedagogical activities, forming a significant attitude-practice gap; (2) professional development (β=0.42, p<.001) and digital literacy (β=0.39, p<.001) are the strongest predictors of adoption readiness, followed by ICT infrastructure quality (β=0.28, p<.01) and policy clarity (β=0.18, p<.05); (3) profound disciplinary disparities exist in adoption readiness, with Engineering (M=4.05) and Medicine (M=3.92) as high-adoption clusters, and Law (M=2.88) and Arts (M=2.95) as low-adoption clusters, driven by epistemological incompatibility; (4) the primary barriers to adoption are not fear of professional replacement (M=2.50 for perceived role threat), but data privacy risks (M=4.05), increased workload (M=3.91), and AI output accuracy/bias concerns (M=3.84); (5) lecturers demand discipline-sensitive professional development, integrated technical-pedagogical support, clear co-created policies, and formal incentives for AI innovation. The study recommends targeted strategies for JJU and similar regional universities to bridge the attitude-practice gap, including establishing a dedicated Digital Pedagogy and AI Center, implementing tiered disciplinary AI training, and building an AI-as-augmented-pedagogy framework with human-in-the-loop pedagogical principles.