Robert Holmgren and David Sjöberg
The purpose of this study is to explore Swedish police education teachers’ informal workplace learning and its perceived value for their professional development. Two categories…
Abstract
Purpose
The purpose of this study is to explore Swedish police education teachers’ informal workplace learning and its perceived value for their professional development. Two categories of teachers, police teachers and university teachers, with different professional knowledge and experience, work together at the police education unit.
Design/methodology/approach
The method used was in-depth interviews with teachers working at a Swedish police education unit.
Findings
Informal workplace learning was perceived by both teacher groups to be of great value for gaining knowledge about the local practice and for their professional development. Their learning emerged in discussions, observations and practically oriented activities in their daily work. Four conclusions: firstly, the teachers’ informal workplace learning was socially and practice-oriented and learning emerged in a collaborative, reciprocal and active process. Secondly, the embodied nature of the learning is evident in the teachers’ joint activities in the teaching practice. Thirdly, it takes time and active involvement in the local practice to become a professional teacher in this kind of education. Fourthly, an educational structure where academic knowledge and experience can be integrated with police knowledge and experience constitutes an important basis for teachers’ professional development in police education and training.
Originality/value
The study’s focus on police education and the professional development of teachers in this specific practice contributes to increased knowledge of the social, practice-oriented and embodied nature of informal workplace learning.
Details
Keywords
Mingye Li, Alemayehu Molla and Sophia Xiaoxia Duan
Artificial intelligence (AI) has been touted as one of the viable solutions to address urban mobility issues. Despite a growing body of research on AI across various sectors, its…
Abstract
Purpose
Artificial intelligence (AI) has been touted as one of the viable solutions to address urban mobility issues. Despite a growing body of research on AI across various sectors, its use in the mobility sector remains underexplored. This study addresses this limitation by investigating AI applications and identifying the AI material properties and use cases that offer mobility-specific affordances.
Design/methodology/approach
Although AI applications in mobility are growing, academic research on the subject has yet to catch up. Therefore, we follow a systematic review and analysis of practitioner literature. We conducted a comprehensive search for relevant documents through Advanced Google and OECD databases and identified 173 sources. We selected 40 sources published between 2015 and 2022 and analysed the corpus of evidence through abductive qualitative analysis technique.
Findings
The analysis reveals that mobility organisations are implementing various AI technologies and systems such as cameras, sensors, IoT, computer vision, natural language processing, robotic process automation, machine learning, deep learning and neural networks. These technologies offer material properties for sensing mobility objects and events, comprehending mobility data, automating mobility activities and learning from mobility data. By exploiting these material properties, mobility organisations are integrating urban mobility management, personalising and automating urban mobility, enabling the smartification of infrastructure and asset management, developing better urban transport planning and management, and enabling automatic driving.
Originality/value
The study contributes a mid-range theory of the affordances of AI for mobility (AI4M) at the infrastructure, operation and service levels. This contribution extends the existing understanding of AI and offers an interconnected perspective of AI affordances for further research. For practitioners, the study provides insights on how to explore AI in alignment with organisational goals to collectively transform urban mobility to be affordable, efficient and sustainable.