Will robots ever be able to learn like humans? If learning is considered posthuman, there are no 'context'-free activities. The intra-active workings of an instructor explaining some rules to a novice are already entangled with a material world that gradually has become meaningful through prior learning. What is meaningful is settled within phenomena. The phenomena of driving create novices and experts from within as the new driver begins to experience how actually driving as an 'advanced beginner' (Dreyfus and Dreyfus 1986, 23) takes shape.

Though brains definitely matter-as when a driver loses the ability to drive after developing Alzheimer's-the mental cannot be reduced to the brain. The mental is not 'subjective'-as our individual perceptions of the colour 'red'. From the posthuman learning perspective that I propose (see also Hasse 2015), the posthuman is not an individual 'subjective' human, but a collective 'subject'. Drivers can disregard a red light, but they have all collectively learned to recognise it as a sign to stop-if they belong to a driving community. Some humans, even today, live in areas without cars and will not know a 'red' light means stop in the way experienced drivers do. When the novices move towards expertise, their world changes in cultural ways that gradually align their experiences with a whole community of drivers. It is through this cultural learning process we become engaged.

Following Kathrine Hayles (1999), the posthuman could simply be a new conception of the humans we always were. What is 'post' is how we perceive these humans-no longer as individual, stand-alone, rational and privileged creatures of a free-floating information-based intelligence

Dreyfus used their work on artificial intelligence to nail down what a human was not: a human was not learning, knowing and perceiving like a machine. The machines at the MIT laboratories for artificial intelligence operated according to symbolic rules find solutions to purely formal tasks-whereas human intelligence, Dreyfus argued-is embodied and situated (Dreyfus 1965). In Alchemy and Artificial Intelligence, Dreyfus argued that AI was like modern alchemy for two reasons, as summarised by Margaret Boden:

Where the first text from 1980 focussed on airplane pilots, chess players and second language learners, the brothers also introduced car drivers in their 1986 version. I will stick to this example in my presentation of the model-and even though the brothers continued to work on it-I will here present the model in the version found in the brothers' work from 1986 and in the summary by Stuart Dreyfus in 2004.

The main objective of this research is to integrate AI technologies to form the teacher’s AI-TPACK framework and evaluate the knowledge elements described within the teacher’s AI-TPACK framework. When developing and validating this tool, recommendations from recent publications [25,98] and analyzed data from various perspectives were considered.

To guarantee the reliability and validity of the measurement instrument, a consultative process was undertaken involving ten experts in the field of educational technology. These experts were selected from five reputable universities

The teacher’s AI-TPACK concept is complex, as it includes integrating knowledge pertaining to AI technology, subject matter expertise, pedagogical knowledge, and the intersection of these three domains. In the process of incorporating AI technology into a teacher’s TPACK, it is important to clarify which specific teacher’s AI-TPACK can be effectively combined with the particular subject matter and pedagogical knowledge to yield favorable educational outcomes [25]. However, current research often lacks an in-depth exploration of this interdisciplinary integration, thereby complicating the establishment of an AI-TPACK assessment framework.

pecifically, Technological Pedagogical Knowledge (TPK) would evolve into AI-TPK, Technological Content Knowledge (TCK) into AI-TCK, and eventually, TPACK tended to transition into AI-TPACK, which comprised the cognitive aspects of AI education, named AI literacy.

Teachers, as central figures in the educational system, are presently called upon to improve their competencies, particularly in the use of artificial intelligence for pedagogical purposes, in this digital age. Existing research states that a common strategy for advancing the AI literacy of pre-service teachers requires the implementation of courses focused on AI [10,11]. An essential factor influencing the use of technology by novice educators is the quality of AI and experiences embedded within teacher education programs [12,13].

AI is considered one of the most effective tools, both within and outside the school environment [4,5,6]. The gradual integration of technology into education has triggered higher demands for students’ AI literacy and capabilities.