Abstract:

The rapid advancement of Artificial Intelligence (AI) is driving innovation across various economic sectors, including education, in our increasingly digital society. Despite this trend, the adoption of AI in science education remains at an early stage, characterized by superficial use. This is largely due to a lack of competence among educators. This paper argues that advancing AI in science education requires mastering the digitalization of science content, thereby providing a foundational base for effective AI integration.

To facilitate this digital transformation, it is essential to upgrade the professional knowledge of science teachers, equipping them with the competences necessary for the pedagogical transformation of science content on digital platforms. This need arises from the widely accepted understanding in Science Education that the evidence-based theoretical framework of Pedagogical Content Knowledge (PCK) is powerful for building teacher knowledge in traditional classroom settings, enabling content to be pedagogically transformed into versions that are easily understood by learners.

In response to the need for upgrading science teacher knowledge for digital contexts, a theoretical construct called Digital-Topic Specific Pedagogical Content Knowledge (digital-TSPCK) was developed in a separate study. Digital-TSPCK presents PCK-based teacher knowledge at the level of a specific topic in the digital realm of teaching. The purpose of this specific study is to highlight the fundamental differences between the digital-TSPCK as the construct refined for digital contexts and its traditional counterpart, the TSPCK theoretical construct.

The study employed a qualitative methodology in a methodology class of Physical Science pre-service teachers in their fourth year of study. Primary data were collected from video recorded teaching practices on the topic of Chemical Equilibrium during a practicum in traditional School classrooms, and purposefully self-built instructional videos targeting asynchronous digital teaching on the University’s Learning Management System. Analysis involved in-depth qualitative examination of teaching segments extracted from the recorded traditional science lessons (referred to as ‘TSPCK episodes’) and the instructional videos (referred to as ‘digital-TSPCK episodes’).

The findings highlight a novel requirement for teacher knowledge, which comprises a blend of multiple but distinct knowledge categories, enabling complex interactions among the internal elements of these categories and their interdependent interactions. By focusing on this foundational aspect, the paper aims to contribute to the attainment of high-quality digitization of science content based on an evidence-based theoretical rationale, to ultimately pave the way for a more comprehensive adoption of AI in science classrooms, enhancing the educational experience and outcomes for students.

Keywords:

Professional teacher knowledge, Digital-TSPCK, digital pedagogic principles.