Integrating AI tools in teacher professional learning: a conceptual model and illustrative case.

This conceptual paper aims to explore the complex nature of integrating AI technologies in teacher professional learning, highlighting the potential for AI to synergize teacher noticing and decision-making processes, support adaptive teaching, foster alignment with competence frameworks, and cultivate professional vision, thereby framing teacher practices within the framework of professional vision. We argue that rather than looking at the process of adopting AI solutions by teachers from a technology perspective or how teachers contribute to designing and developing such tools, we take the perspective of the teacher and ask how such tools are meaningfully integrated into teacher practices. In our conceptual paper, we illustrate the case of a novel approach to the teacher training model where the development of teacher' professional vision and professional learning is combined with the design of the AI solutions. We argue the importance of involving teachers into the design of AI solutions through professional learning models to support teachers to develop knowledge-based reasoning skills and at the same time to learn about pedagogical concepts and develop new mental models.

Dynamics of human categorization in a collaborative tagging system: How social processes of semantic stabilization shape individual sensemaking.

We study how categories form and develop over time in a sensemaking task by groups of students employing a collaborative tagging system. In line with distributed cognition theories, we look at both the tags students use and their strength of representation in memory. We hypothesize that categories get more differentiated over time as students learn, and that semantic stabilization on the group level (i.e. the convergence in the use of tags) mediates this relationship. Results of a field experiment that tested the impact of topic study duration on the specificity of tags confirms these hypotheses, although it was not study duration that produced this effect, but rather the effectiveness of the collaborative taxonomy the groups built. In the groups with higher levels of semantic stabilization, we found use of more specific tags and better representation in memory. We discuss these findings with regard to the important role of the information value of tags that would drive both the convergence on the group level as well as a shift to more specific levels of categorization. We also discuss the implication for cognitive science research by highlighting the importance of collaboratively built artefacts in the process of how knowledge is acquired, and implications for educational applications of collaborative tagging environments.