n°020_Vol.1_28

Introduction: Artificial intelligence (AI) has now become a key topic in higher education, both for its technological dimension and its societal challenges. The growing widespread adoption of AI in higher education is accompanied by a major challenge: how to incorporate these subtle concepts into an accessible, critical, and relevant presentation? This is where didactics, considered the science of teaching and learning knowledge, becomes essential. At the heart of this training, the Python language is quickly establishing itself as an essential reference tool. Highly prized for its accessible syntax, readability, and abundance of specialized AI library elements (to name a few: NumPy, Scikit-learn, TensorFlow, PyTorch), Python has become a real asset in artificial intelligence universities. It therefore seems essential to examine the pedagogical approaches implemented to teach AI through the Python framework. What are the most relevant teaching methods? How can we combine scientific rigor, student engagement, and content accessibility? What challenges are teachers likely to face, and in what forms? How can we educate academics in the critical and ethical use of these tools? How can didactics support the teaching of artificial intelligence at university level with regard to the Python language, while addressing the pedagogical, technical, and ethical issues raised by this rapidly growing field? Theoretical foundations of didactics are examined in this chapter in relation to the teaching of artificial intelligence, with an emphasis on the aspects related to the technical and interdisciplinary nature of the field. Besides, it approaches the different uses of the Python language as the most preferred medium in the academic contexts owing to its flexibility, readability, and wide adoption in AI research. The research has the goal of pinpointing the best educational methods used by the teachers and the major difficulties faced by both teachers and learners in the application of these methods. The concluding chapter likewise puts forward a series of recommendations that, on the basis of didactic perspective encompassing the technical, cognitive and pedagogical dimensions of learning, are meant to enhance the quality of AI teaching with Python.

Conclusion

The teaching of artificial intelligence at the university level, with the help of the Python programming language, is a strategic necessity for the professional training of the future and the establishment of a responsible digital society. Indeed, Python is becoming more and more popular as a learning tool that allows students to not only understand the basics of algorithms but also apply the tools to real-life problems. This chapter dealt with this issue from the points of view of the technology, the method of teaching, the learning process, and the institution. It pointed out the difficulties that both teachers and students were facing, such as: need for training, lack of material resources, and misunderstanding of the concepts. It even suggested areas where improvements could be made which would facilitate the development of more robust and uniform training programs, encourage the interdisciplinary method and increase the critical and ethical comprehension of AI. The core purpose is to produce a generation that will not only be technically capable but also well aware of the implications, limitations, and the impact on society of these technologies.