This work analyzes the students' perception of artificial intelligence after having carried out a teamwork activity in which they were able to use artificial intelligence programs-software. It includes the generation of training educational material. Specifically, in Physics subjects in the first university year of engineering degrees. It has been detected that the degree of prior knowledge that students have about artificial intelligence is diverse, as found in the literature [1,2]. Most have used it at the user level to try to do complex jobs simply, generate text, or perform a translation. They usually know a few programs and use them in a usually uncritical way. One of the objectives of the activity has been for them to have a critical vision of AI. That they are aware that the information provided, even if it is scientific or technical in nature, may be erroneous. Also, the references may be false or not related to the text. The professor's supervision is essential in this AI learning process. Obviously, it is necessary to train artificial intelligence [3]. The interaction with AI must be designed correctly. For example, if you want to generate questions or problems, it is better to give correct examples from which to generate new exercises. Regarding references, it must be indicated that if the AI is not sure of a reference, AI must recognize so.

On the other hand, it is observed that students are more concerned about how AI will affect their employability (if it will take jobs away from human beings), than about erroneous or biased AI information.

However, they assume that they must learn to use it, since it will be essential for them to do so throughout their professional lives, just as is the case with computing today. It should be noted that current students are users of AI, in the coming years they will be native AI, although mostly only as users.

References:
[1] A. Subaveerapendiyan, S. Sunanthini, M. Amees, “A study of the knowledge and perception of artificial intelligence,” IFLA Journal, vol. 49, 2023.
[2] C.K.Y. Chan, W. Hu, “Voices on generative AI: perceptions, benefits, and challenges in higher education,” Int. J. Educ. Techno. High. Educ., vol. 20, 43, 2023.
[3] J. Boone, C. Goodin, L. Dabbiru, C. Hudson, L. Cagle, D. Carruth, “Training artificial intelligence with automatically labelled UAV data from Physics-based simulation software,” Appl. Sci., vol. 13, 131, 2023.

Keywords: Artificial intelligence, training, teamwork, Physics.