Although professional development (PD) programs often use video, extraneous information during video viewing can distract teachers. We developed a discourse visualization tool, the Classroom Discou...

Deep neural networks are increasingly employed to model classroom dialogue and provide teachers with prompt and valuable feedback on their teaching practices. However, these deep learning models often have intricate structures with numerous unknown parameters, functioning as black boxes. The lack of clear explanations regarding their classroom dialogue analysis likely leads teachers to distrust and underutilize these AI‐powered models. To tackle this issue, we leveraged explainable AI to unravel classroom dialogue analysis and conducted an experiment to evaluate the effects of explanations. Fifty‐nine pre‐service teachers were recruited and randomly assigned to either a treatment ( n = 30) or control ( n = 29) group. Initially, both groups learned to analyse classroom dialogue using AI‐powered models without explanations. Subsequently, the treatment group received both AI analysis and explanations, while the control group continued to receive only AI predictions. The results demonstrated that teachers in the treatment group exhibited significantly higher levels of trust in and technology acceptance of AI‐powered models for classroom dialogue analysis compared to those in the control group. Notably, there were no significant differences in cognitive load between the two groups. Furthermore, teachers in the treatment group expressed high satisfaction with the explanations. During interviews, they also elucidated how the explanations changed their perceptions of model features and attitudes towards the models. This study is among the pioneering works to propose and validate the use of explainable AI to address interpretability challenges within deep learning‐based models in the context of classroom dialogue analysis. Practitioner notes: What is already known about this topic: Classroom dialogue is recognized as a crucial element in the teaching and learning process. Researchers have increasingly utilized AI techniques, particularly deep learning methods, to analyse classroom dialogue. Deep learning‐based models, characterized by their intricate structures, often function as black boxes, lacking the ability to provide transparent explanations regarding their analysis. This limitation can result in teachers harbouring distrust and underutilizing these models. What this paper adds: This paper highlights the importance of incorporating explainable AI approaches to tackle the interpretability issues associated with deep learning‐based models utilized for classroom dialogue analysis. Through an experimental study, this paper demonstrates that providing model explanations enhances teachers' trust in and technology acceptance of AI‐powered classroom dialogue models, without increasing their cognitive load. Teachers express satisfaction with the model explanations provided by explainable AI. Implications for practice and/or policy: The integration of explainable AI can effectively address the challenge of interpretability in complex AI‐powered models used for analysing classroom dialogue. Intelligent teaching systems designed for classroom dialogue can benefit from advanced AI models and explainable AI approaches, which offer users both automated analysis and clear explanations. By enabling users to understand the underlying rationale behind the analysis, the explanations can contribute to fostering trust and acceptance of the AI models among users.

Purpose: The purpose of this paper is to review identified key features of teacher support and its influence on students’ career decision making and development. It also examines the types of research approaches to data collection and analysis in order to provide a more in-depth evaluation of this field of study. Design/methodology/approach: The review examined 18 studies, the majority being quantitative in design. Cross-sectional and longitudinal effects of teacher support were investigated, and differences in teacher support relative to grade level and gender were considered. Findings: Results indicated that since 2000, there has been an increase in the number of empirical studies of teacher support in the career development field, the majority involving middle and high school students. The most commonly cited frameworks that underpin the studies are the social cognitive career theory, the career construction theory and Super’s career development theory. The most frequently used instrument for data collection was the teacher support scale (Metheny et al., 2008). Originality/value: The review will be beneficial to researchers, teachers and policy makers seeking to optimize support systems for students’ career development. The findings contribute to further theory development and have practical implications for the career guidance field. Recommendations for future research and practice are suggested.