Relationships Between Trust in Artificial Intelligence, Teacher-AI Collaboration and Innovative Working Behavior among Teachers

In the context of modern education, Artificial Intelligence (AI) stands as a pivotal force reshaping the landscape of teaching and learning. The importance of AI for teachers in educational contexts cannot be overstated, as it offers a myriad of opportunities to enhance pedagogical practices, personalize learning experiences, and optimize student outcomes (Li & Wong, 2023). By leveraging AI-powered tools and technologies, teachers can streamline administrative tasks, deliver personalized instruction, analyze student data, and foster innovation in the classroom (Ho et al., 2019). AI empowers educators to adapt to the evolving demands of the digital age, equipping them with the resources and insights needed to create dynamic and engaging learning environments that cater to the diverse needs of today's learners. However, embedded within the realm of AI's importance for teachers lies a crucial element that shapes the dynamics of human-AI collaboration: trust. Trust in AI is foundational for educators, serving as the bedrock upon which successful integration and utilization of AI technologies rest (Choung et al., 2023). Building trust in AI engenders a sense of reliability and confidence, empowering teachers to leverage the full potential of AI tools to enrich their instructional strategies and promote student engagement (Choung et al., 2023). This trust enables educators to explore innovative teaching approaches, harness data-driven insights for informed decision-making, and cultivate a collaborative partnership between human teachers and AI systems. Trust in AI not only enhances teachers' confidence in incorporating technology into their pedagogical practices but also fosters a symbiotic relationship of mutual support between educators and AI technologies (Nazaretsky et al., 2022). By nurturing trust in AI, teachers can navigate the complexities of AI integration with assurance, viewing AI as a valuable ally that complements their expertise and augments their teaching efficacy. This collaborative synergy empowers educators to leverage the strengths of AI systems while drawing upon their own unique insights and experiences, fostering a culture of continuous learning, professional growth, and innovation in educational settings (Nazaretsky et al., 2022). However, a notable research gap exists in understanding the intricate associations between trust in AI, teacher-AI collaboration, and innovative working behavior among educators. While studies have begun to explore the significance of trust in AI for teachers and the outcomes of teacher-AI collaboration or innovative practices independently, there is a lack of comprehensive research that examines how trust in AI influences collaborative dynamics with AI systems and its direct link to promoting innovative working behaviors in educational contexts. Understanding the mechanisms through which trust impacts collaborative engagement and drives innovative behaviors is essential for uncovering the pathways through which trust in AI contributes to transformative changes in educational practices. Moreover, a research gap exists in examining the mediating role of teacher-AI collaboration in the relationship between trust in AI and innovative working behavior among teachers. While trust is recognized as a fundamental element in facilitating technology adoption and utilization, the extent to which teacher-AI collaboration mediates the link between trust in AI and innovative practices remains underexplored. Thus, the current study aimed to investigate the roles of trust in AI in teacher-AI collaboration and innovative working behavior, as well as the mediating role of teacher-AI collaboration in the link between trust in AI and innovative practices. By addressing the research gaps, scholars can advance theoretical knowledge in the field of educational technology. This research can offer practical implications for designing effective interventions that enhance teacher-AI interactions, promote a culture of collaboration and innovation, and optimize learning outcomes for students in the digital age. The study involved 768 teachers from diverse educational backgrounds and teaching specialties. Trust in AI was assessed using a validated scale developed by Nazaretsky et al. (2022). The scale consists of 25 items measuring teachers' attitudes and beliefs regarding the potential benefits of AI in education, obstacles related to using AI in education, and working alongside AI to improve pedagogy. Teacher-AI collaboration was measured using the employee-AI collaboration scale (Kong et al., 2023), designed to assess collaboration regarding co-work content between employees and AI technology. Innovative working behavior was assessed using a scale developed based on established frameworks of innovation development in social settings (Messmann & Mulder, 2012). The scale consists of 20 items assessing the work activities necessary to accomplish four innovation tasks: opportunity exploration, idea generation, idea promotion, and reflection. Socio-demographic information (i.e., age, gender, years of teaching experience, years of experience using technology in pedagogy, education level, teaching discipline) was collected and utilized as control variables. A structural equation model was specified to investigate the direct and indirect effects of trust in AI on innovative working behavior, with teacher-AI collaboration serving as a potential mediating factor. Model fit indices such as the Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), Root Mean Square Error of Approximation (RMSEA), and Standardized Root Mean Square Residual (SRMR) were employed to evaluate the goodness of fit of the SEM model, providing insights into the validity of the proposed relationships and the overall model adequacy, where CFI and TLI > 0.95, and RMSEA = 0.06 demonstrate good fit (Hu & Bentler, 1999). The study revealed significant positive associations among trust in AI, teacher-AI collaboration, and innovative working behavior. The results underscore the pivotal role of trust in fostering effective collaboration between teachers and AI systems. The positive relationship between trust in AI and innovative working behavior highlights the transformative potential of trust in driving educators towards adopting innovative teaching practices. The results also emphasized the positive association between teacher-AI collaboration and innovative working behavior. Furthermore, the results illuminated the mediating role of teacher-AI collaboration in the relationship between trust in AI and innovative working behavior. This suggests that effective collaboration with AI systems serves as a bridge that enables educators to harness the potential of AI tools, translate trust into action, and drive innovation in educational practices. The findings underscore the importance of fostering trust in AI technologies and promoting collaborative partnerships between teachers and AI systems to enhance innovative practices in teaching and learning environments.