Critical digital literacy: An approach that expands the concept of digital literacy. In addition to knowing how to use technology, it involves understanding and questioning the power structures, biases, and social dynamics behind digital technologies (Pangrazio, 2016; Pangrazio & Selwyn, 2020).

Agency: In an educational context, this refers to the capacity of students and teachers to make decisions and act autonomously in teaching and learning within classroom environments (Holmes, 2025).

A creation by Fundación Vía Libre in collaboration with FAMAF – UNC.

With the support of: Mozilla, FAIR, and Data Empowerment Fund.

Machine Learning (ML): A branch of artificial intelligence and computer science that focuses on using data and algorithms to enable AI to imitate the way humans learn, gradually improving its accuracy.

Example: In machine learning, an algorithm can predict the price of a house by analyzing historical sales data, such as size and location. As the model receives more data, its ability to make accurate predictions improves.

Training Dataset: A collection of data containing examples of solutions to a problem, such as chess moves with their outcomes or classified photos of dogs and cats. For the system to perform well, these datasets usually need to be massive.

Graphics Processing Unit (GPU): Designed for parallel processing, the GPU is used in a wide range of applications, including graphics and video rendering. While best known for their capabilities in gaming, GPUs are gaining popularity in creative production and artificial intelligence (AI). GPUs were originally designed to accelerate 3D graphics rendering. Other developers also began leveraging GPU power to dramatically speed up other workloads in high-performance computing (HPC), deep learning, and more.

Opt-in (Active Consent): Users must explicitly consent before their data is collected or shared.

Opt-out: The service comes enabled by default, requiring the user to navigate settings to disable it.

Data Extractivism: This term is inspired by natural resource extractivism (mining, oil), where a common or local resource is exploited without returning real benefits to the provider communities. Data extractivism refers to the massive collection of personal and social information without genuine consent, which is then used for commercial, surveillance, or control purposes, without users having decision-making power or benefiting from that use.

Reference: IBM. Machine Learning. Retrieved from https://www.ibm.com/mx-es/topics/machine-learning

Generative AI: A type of artificial intelligence (AI) system capable of generating text, images, or other media in response to prompts. Generative AI models learn the patterns and structure of their input training data and then generate new data that shares similar characteristics.

Example: DALL-E is a generative AI model that can create unique images from text descriptions, such as "a blue cat sitting on a moon made of cheese."

Reference: Wikipedia. Generative artificial intelligence. Retrieved from https://es.wikipedia.org/wiki/Inteligencia_artificial_generativa

Hallucination (in AI): A phenomenon in which a language model generates information that is factually false, non-existent, or unsupported by real data, but presents it with an appearance of absolute certainty and syntactic coherence.

Stereotype: An image or idea commonly accepted by a group or society as immutable.

Reference: Royal Spanish Academy (RAE). Estereotipo. In Dictionary of the Spanish Language. Retrieved from https://dle.rae.es/estereotipo

In AI, it refers to the reproduction or amplification of overgeneralized beliefs (also known as social biases) about specific groups of people, which can be incorporated into language models through training data or methods.

Example: A language model that consistently associates certain professions with specific genders, such as "nurse" with women and "engineer" with men.

Out-group stereotype: Perceptions, often simplified or distorted, that members of a group hold about another group to which they do not belong.

In-group stereotype: The beliefs, images, and descriptions that members of a social group hold about themselves.

Automation bias: The human propensity to excessively or uncritically trust suggestions from an automated system, even ignoring one's own logic or contradictory information.

Word embedding: A technique for representing words as dense numerical vectors in a multidimensional space. This representation captures semantic and syntactic relationships between words, allowing machines to process language more effectively.

Example: In a word embedding model, semantically similar words like "king" and "queen" would be close together in the vector space, whereas "king" and "apple" would be further apart.