Exploring STEAM teachers’ trust in AI-based educational technologies: a structural equation modelling approach

In the rapidly evolving landscape of education, Artificial Intelligence (AI) has emerged as a transformative tool with the potential to revolutionize teaching and learning processes. However, the successful integration of AI in education depends on the trust and acceptance of teachers. This study addresses a significant gap in research by investigating the trust dynamics of 677 in-service Science, Technology, Engineering, Arts, and Mathematics (STEAM) teachers in Nigeria towards AI-based educational technologies. Employing structural equation modelling for data analysis, our findings reveal that anxiety, preferred methods to increase trust, and perceived benefits significantly influence teachers' trust in AI-based edtech. Notably, the lack of human characteristics in AI does not impact trust among STEAM teachers. Additionally, our study reports a significant gender moderation effect on STEAM teachers' trust in AI. These insights are valuable for educational policymakers and stakeholders aiming to create an inclusive, AI-enriched instructional environment. The results underscore the importance of continuous professional development programs for STEAM teachers, emphasizing hands-on experiences to build and sustain confidence in integrating AI tools effectively, thus fostering trust in the transformative potentials of AI in STEAM education

A Probe into Spoken English Recognition in English Education Based on Computer-Aided Comprehensive Analysis

At present, computer-aided spoken English learning is becoming increasingly popular among learners. The computer-aided comprehensive analysis tech-nology can evaluate and correct learner's spoken pronunciation, thereby im-proving their pronunciation. Based on computer-aided comprehensive analy-sis, this paper aims to explore the automatic recognition and scoring methods of spoken English in English education. For this, it studies the effective matching of the feedback information with the known pronunciation scoring results, and then develops a computer evaluation plug-in consisting of dif-ferent modules such as user login, English spoken speech acquisition and recognition, voice evaluation, speech broadcast, and spoken dialogue. The research results show that the computer evaluation plug-in matches and compares the extracted feature parameters of input speech with the standard features, scores the spoken language input by the learner, and gives the cor-rect pronunciation so that the learner can get feedback in time. For different stages of English learning, the focus of recognition technology and the spo-ken recognition algorithms applied also vary. The research findings provide theoretical and technical support for oral English recognition, error correction and scoring