Artificial intelligence (AI) is revolutionizing educational paradigms and transforming teaching practice thanks to machine learning, natural language processing and personalized tutoring systems. One of the keys is the advanced analysis of students' learning data, which allows teachers to develop individualized teaching strategies.

A team led by the Universitat Oberta de Catalunya (UOC) has analysed what leads secondary school teachers to adopt artificial intelligence systems for educational purposes. To do this, they surveyed 372 teachers in Catalonia. Having prior knowledge of AI, particularly an understanding of how AI-based content, such as images, videos, and music, is created, increases the likelihood of teachers using these tools in their day-to-day work.

Moreover, the study, published as open access in Computers and Education: Artificial Intelligence, shows that AI cannot be effectively used in education if teaching staff do not have a solid foundation in data literacy. The authors analysed how the general or applied use of data influenced the adoption of these technologies in the classroom.

"When we speak of the general use of data, we mean the extensive use of analytical practices, such as identifying learning problems or improving teaching and learning processes using data generated in digital learning environments, such as Moodle or Forms", explained Marta López Costa, lead author of the study and co-leader of the Education Research Group (GREDU), in the UOC-FuturEd Research Centre.

"The applied use of data, meanwhile, implies a more technical and specific use of data to make educational decisions, considering aspects such as privacy, ethics and institutional policies on data management", said López Costa, who is also a member of the Faculty of Psychology and Education Sciences. On behalf of the UOC, Nati Cabrera and Marcelo Maina —from the same centre and faculty— are also taking part, as members of the Education and ICT (Edul@b) research group.

The study also involved the Ramon Llull University and showed that, together with practical knowledge of AI, it is the general use of data by teachers that has a significant direct effect on the adoption of these technologies, and not a more technical and advanced applied use of data. "This suggests that, rather than a high technical level, more basic and applied skills are needed," said López Costa.


STEM training does not influence their adoption

For their research, the team used two validated instruments: one to examine the elements associated with artificial intelligence, and the other to assess the data literacy skills of the teachers participating.

In addition to previous knowledge of artificial intelligence, general and applied use of data, the authors analysed the influence of perceptions of AI and having STEM (Science, Technology, Engineering, and Mathematics) knowledge. Interestingly, having a scientific background did not lead to greater adoption of these tools in the classroom.

"Although STEM competencies are closely related to computational thinking and problem-solving, they don't necessarily explain higher adoption rates of AI. This finding highlights the need for interdisciplinary approaches that examine how AI literacy is integrated into different areas of teaching," the authors noted.

The study showed a weak negative relationship between the perception of these technologies, particularly teachers' concerns when implementing them, and their adoption. "While the scale of the effect was negligible, addressing these concerns through transparent communication and ethical guidelines remains essential to building trust among teachers," the authors said.


Common frameworks in digital competency

As the research shows, key competencies in data literacy significantly influence the adoption of AI. "At an international and national level, there are already frameworks of reference for teachers' digital competency in artificial intelligence," said López Costa, who mentioned three of them: the UNESCO AI competency framework for teachers, the Supplement to the DigCompEDU Framework and the guidelines and recommendations published by the Government of Catalonia's Ministry of Education in 2024.

"These common criteria and regulatory frameworks for digital competency are necessary to train, regulate, promote and guide the use of AI in education," she stressed. The research also emphasizes the need for practical, contextual and cooperative training to improve AI literacy among Catalan teachers.

López Costa stressed how the following stages in the project – case studies and focus groups – have shown that the training of teachers in AI should be mainly among peers, so that teachers share experiences with their colleagues.

In the future, the team also plans to expand the model with new variables, include positive perceptions of these technologies, and conduct comparative studies with other regions and countries to make the results more generally applicable.


This project is aligned with the UOC's research missions: Lifelong education, Ethical and human-centred technology, Digital transition and sustainability, and Culture for a critical society. It also contributes to the following Sustainable Development Goals (SDGs): 4, Quality Education, 9, Industry, Innovation and Infrastructure, 10, Reduced Inequalities and 16, Peace, Justice and strong institutions.


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The UOC’s over 500 researchers and more than 50 research groups are working in five research units focusing on five missions: lifelong learning; ethical and human-centred technology; digital transition and sustainability; culture for a critical society, and digital health and planetary well-being.

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More information: www.uoc.edu/en/research