Frederic Guerrero-Solé
Professor of the Department of Communication

There is a general consensus that nothing has ever before caused such a seismic upheaval so quickly in the world of education as generative artificial intelligence (GenAI). In less than a year, this technology has shown us how easy it is to create a nearly infinite amount of content of any kind, with an unquestionable level of quality and verisimilitude, in a negligible amount of time. 

This ability to generate content has a revolutionary potential in the field of education. Tools such as ChatGPT (it almost hurts to write, given that, in a matter of months, it may be rendered obsolete or surpassed by the tools promised by the major tech players, such as Meta, Alphabet, Apple or Amazon) enable the writing of texts, computer programs, websites or other types of content, which can supplement and even replace the work done by students, as well as profoundly impact the materials used by teachers. 

The educational world – and the institutional world in general – has reacted in unprecedented fashion, with proposals running the gamut from banning their use (as in the case of Italy) to providing training in their use and transforming assessment methods

The educational world – and the institutional world in general – has reacted in unprecedented fashion, with proposals running the gamut from banning their use (as in the case of Italy) to providing training in their use and transforming assessment methods. This response is particularly urgent in the discipline of communication, in which a large share of students’ competencies are related to their ability to create texts, in a general sense. If we accept that the use of these tools will not be limited in any way, the main questions become: 

—How does this transform the competencies that students will have to acquire?

—How do we add competencies in the use of GenAI?

—How do we transform our assessment methods to ensure that we are really assessing the competencies and knowledge acquired?

—How are universities equipping themselves with regard to GenAI?

—How does it transform teaching as a whole and how are teaching staff adapting to it?

—How does it transform some of the commitments between students and teachers?

Some possible, albeit tentative, responses to this change could be: 

To delve deeper into competencies that cannot be replaced by GenAI, as well as competencies that enable better outcomes in the use of GenAI. In an environment in which superficial results can be more optimally reproduced by a machine, deep, critical, complex thinking emerges as a first-order capability – if it was not one already. The capacity for critical thinking also becomes fundamental, especially when it comes to questioning or deciphering how GenAI tools and models work. 

Obviously, competencies in the use of GenAI have to be introduced at all levels of creation and must improve the outcomes of that use. To this end, specific and continuous teacher training is and will be needed, so that teachers can gradually incorporate the GenAI tools involved in the learning and practice processes of the subjects they teach. 

Assessment methods can be transformed in, at least, two different ways. The first is in processes that involve GenAI in which the competencies required for its use and the assessment of its outputs are measured. The second is in processes that do not involve GenAI and in which competencies are assessed without students having access to support tools. This opens up what are often viewed as secondary options for assessing competencies, such as presentations, oral exams or other more direct and, in general, in-person methods, which can be key to students’ training process. To this end, teaching innovation processes take on added importance in the university environment. Here, it is worth highlighting the fundamental role that research in educational innovation has played to date at Pompeu Fabra University, where it has and continues to help us face with guarantees a transformation of the education model that will, in some ways, be radical. 

The fourth and fifth points refer to the need for universities to equip themselves with the best GenAI tools available on the market, preferably open-source, and to make them available to students and teaching and research staff. It is another urgent measure that will mark the institution’s adaptation to a world of knowledge generation that is diametrically different from the one we have known to date. This adaptation of tools can be done at the institutional level, or at a higher level, with a demand for equipment, including both software and hardware, to make the Catalan university system as a whole a hub for AI-based content generation. 

Finally, there is the issue of commitments, accountability, transparency, ethics and the veracity of everything that students produce and teachers certify and assess. This is also a debate with cultural aspects that we will have to deal with in the immediate future. Given the impossibility of exercising absolute control over the sources of text generation, we must guarantee complete and absolute transparency in all creative processes, as well as a traceability of sources that allows us to identify their origin and reconstruct the creation process as a whole. The cases of research papers, at all levels, in which GenAI has already been used should lead us to redouble our commitment and responsibility with regard to our practices as researchers and research validators. 

Law of performative balance

The balance between creation and reading times is, and will be, one of the key aspects in the acceptance and assessment of the uses of GenAI and that may noticeably affect the very value of the creations made with this technology. To this end, and simply to offer some provocative food for thought, this author states that his use of GenAI for the writing of this article was equivalent to 0% of the text, that he dedicated approximately 2 hours of his life to it, and that it can be read in less than 5 minutes, yielding a 1:24 ratio in favour of the reader.