Artificial Intelligence Foundations for Broadening Cultural Horizons
Perfilado de sección
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This course offers a comprehensive and critical journey through the world of artificial intelligence. Across four units, it invites participants to understand how language models work, what data they are trained on, why they don't always tell the truth, and what it means for their responses to be probabilistic rather than neutral. It explores their technical limits as well as their ethical and cultural dimensions: biases, stereotypes, alignment problems, and phenomena such as algorithmic sycophancy.
🔎 Beyond the theoretical foundations, the course offers hands-on activities and experiences that help identify stereotypes, build data with a local perspective, and critically reflect on the educational use of these tools. The invitation is clear: don't accept AI as the final truth, but use it as a starting point to strengthen autonomy, critical thinking, and the collective construction of knowledge.
🔁 Some classes have a more technical component than others, but all of them include short activities to help you progress: from multiple-choice questions to open-ended prompts and hands-on exercises with the EDIA tool. In each class, you'll find the instructions needed to complete them.
The idea is that you can go through the course at your own pace, experimenting, questioning, and asking. We welcome you and hope this course will be an opportunity to learn, question, and build knowledge collectively.
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Prefer to watch in a language other than Spanish? Just turn on CC, go to Settings (⚙️) > Subtitles > Auto-translate, and select your language!
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This unit offers an initial approach to artificial intelligence, inviting reflection on the place it occupies in our teaching practices and the decisions we are—or are not—willing to delegate.
It examines AI's promises, limits, and tensions, placing pedagogical mediation, ethics, and human agency at the center. From there, it introduces the fundamental role of data in how these systems work: where it comes from, how it is collected, and what power relations it sustains.
Finally, it addresses generative AI and language models, exploring their predictive nature, their possible uses in the classroom, and the challenges they pose in terms of autonomy, technological dependence, and educational sovereignty.
We invite you to build your own critical perspective.
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LESSON 1 - ARTIFICIAL INTELLIGENCE FOUNDATIONS FOR BROADENING CULTURAL HORIZONS
Teaching in the Age of Artificial Intelligence
What role do we assign to artificial intelligence in education? This lesson introduces the concept of critical digital literacy to address key questions about teaching in the era of AI: how these systems work, what promises they bring, their limitations, and the tensions that generate when they enter the classroom.
Far from offering absolute truths, this approach invites us to consider AI through a critical and ethical lens, centering the teacher's role, pedagogical mediation, and human agency. What do we want to use AI for? Which decisions are we unwilling to delegate?
At the end of the lesson, you will be asked to answer a few questions. The answers will be graded, and completing this activity is required to finish the course.
👩🏫 Instructor: Emilia Echeveste
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Prefer to watch in a language other than Spanish? Just turn on CC, go to Settings (⚙️) > Subtitles > Auto-translate, and select your language!
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LESSON 2 - ARTIFICIAL INTELLIGENCE FOUNDATIONS FOR BROADENING CULTURAL HORIZONS
AI and the Essential Role of Data
What feeds artificial intelligence? In this lesson, we focus on the data: how AI-based systems work using inputs and outputs, where the massive amount of training information comes from, and why their development is deeply related to the availability of big data and computing power. We analyze everyday applications to understand that AI does not "think" (it recognizes patterns within large volumes of information).
At the same time, we present some questions about data collection and usage: Do we know what information we generate when using digital platforms? Who stores it, and for what purposes? Does true consent exist when we accept terms and conditions without reading them? Rather than normalizing data extractivism, this lesson invites us to question the concentration of tech power and reflect on what all of this means for our educational practices.
At the end of the lesson, you will be asked to answer a few questions.
👨🏫 Instructor: Marcos Gómez-
Prefer to watch in a language other than Spanish? Just turn on CC, go to Settings (⚙️) > Subtitles > Auto-translate, and select your language!
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LESSON 3 - ARTIFICIAL INTELLIGENCE FOUNDATIONS FOR BROADENING CULTURAL HORIZONS
Language Models: Everywhere?
In this lesson, we dive into generative artificial intelligence and language models: systems capable of producing text, images, or code from bigs amounts of data. How do these models work? What does it mean for them to be "predictive"? How do commercial models differ from open-source ones? We analyze their features, types, and the range of tasks they can perform (generating text, summarizing, translating, editing, assisting, and holding conversations).
At the same time, we open to reflection: What are the implications of these tools becoming standardized from from early stages of education? Beyond enthusiasm or rejection, the invitation is to think critically about their use: Where can they add value? What risks do they pose regarding dependency, data privacy, and teaching autonomy?
👨🏫 Instructor: Marcos Gómez-
Prefer to watch in a language other than Spanish? Just turn on CC, go to Settings (⚙️) > Subtitles > Auto-translate, and select your language!
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LESSON 4 - ARTIFICIAL INTELLIGENCE FOUNDATIONS FOR BROADENING CULTURAL HORIZONS
What Kinds of Mistakes Do AI Models Make?
In this lesson, we analyze two types of errors language models can make: classification errors and hallucinations. We also anticipate another source of error: social biases, also known as stereotypes. A classification error occurs when the model assigns an incorrect category to a text or image. Hallucinations, on the other hand, involve presenting false or generated information as if it were true, even citing non-existent sources that seem plausible at first glance. These situations can reinforce automation bias, as we tend to trust responses that sound technical or well-founded. We review complex examples, such as incorrect medical interpretations of X-rays or errors in dosage calculations, even when the model uses official sources via Retrieval-Augmented Generation (RAG) systems.As a case study on classification errors, we analyze the difficulty of distinguishing human-written text from AI-generated text, and how even detection tools can fail. Through concrete examples, we examine how models can mix up information, commit inaccuracies, or make mathematical miscalculations. This lesson invites us to present a critical mindset by recognizing the ways AI makes mistakes and why those errors are often difficult to detect.
At the end of the lesson, you will be asked to answer a few questions.
👩🏫 Instructor: Luciana Benotti-
Prefer to watch in a language other than Spanish? Just turn on CC, go to Settings (⚙️) > Subtitles > Auto-translate, and select your language!
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This unit delves into the inner workings of language models, addressing how they learn from vast amounts of data through probabilistic word prediction. It critically examines the distinction between generating plausible responses and actual understanding, as well as the associated risks: stereotypes, implicit assumptions, and errors. It also analyzes training data, its origin, and the varying levels of model openness and transparency, connecting these choices to technological power dynamics. Finally, it explores the non-deterministic nature of these systems, the impact of parameters such as temperature, and the presence of hidden instructions like the system prompt. The unit invites reflection on transparency, auditability, and pedagogical criteria for an informed and critical use of AI.
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LESSON 5 - ARTIFICIAL INTELLIGENCE FOUNDATIONS FOR BROADENING CULTURAL HORIZONS
How Do Large Language Models Learn?
In this lesson, we delve into the inner workings of language models. How do they learn the meaning of words? Through massive volumes of data and the constant prediction of the next word in a sequence, LLMs recognize patterns and semantic proximity.However, generating plausible sentences is not the same as understanding or guaranteeing their truth. What does it mean for a response to be statistically probable, but not necessarily correct?
We also analyze the risks: implicit assumptions, reproduced stereotypes, and confusion between entities. Finally, we distinguish between a base model (trained to complete text) and a model adjusted via fine-tuning, aligned to follow specific instructions. This lesson invites us to understand that behind every answer lie training, classification, and alignment processes, and that knowing these mechanisms is essential for using these tools with pedagogical judgment.
At the end of the lesson, you will be asked to answer a few questions.
👨🏫 Instructor: Guido Ivetta-
Prefer to watch in a language other than Spanish? Just turn on CC, go to Settings (⚙️) > Subtitles > Auto-translate, and select your language!
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LESSON 6 - ARTIFICIAL INTELLIGENCE FOUNDATIONS FOR BROADENING CULTURAL HORIZONS
Where Do Large Language Models Learn From?
What data are models trained on, and what do we know (or not know) about it? In this lesson, we analyze the composition of the data used to train language models. Where do those texts come from? The internet, digitized books, academic articles, forums, code repositories, and specialized databases form part of the large corpora used for their training. We review concrete cases (such as BERT, GPT-1, or GPT-3) to understand how data sources have evolved and what happens when companies stop disclosing this information in more recent versions.
We also explore how models are categorized based on their level of openness and transparency: open-source, open-weights, or closed-source. What does it mean to be able to audit a model? What differences exist in terms of costs, access, and community participation? Through current examples, this lesson invites us to reflect on the relationship between data, transparency, and technological power, and to consider what types of models we want to promote in the educational sphere.
At the end of the lesson, you will be asked to answer a few questions. The answers will be graded, and completing this activity is required to finish and earn credit for the course.
👨🏫 Instructor: Guido Ivetta-
Prefer to watch in a language other than Spanish? Just turn on CC, go to Settings (⚙️) > Subtitles > Auto-translate, and select your language!
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LESSON 7 - ARTIFICIAL INTELLIGENCE FOUNDATIONS FOR BROADENING CULTURAL HORIZONS
Biases and Ambiguity
In this lesson, we analyze the different types of biases that can emerge in artificial intelligence systems (social, emergent, and automation bias) and how they influence the responses generated. Through concrete examples, we observe how models can reinforce stereotypes, such as associating certain professions with income levels or linking "intelligence" exclusively to developed nations. These cases demonstrate that model responses are not neutral, but rather reflect cultural stereotpyes and hierarchies present in their training data.
We also examine the concept of uncertainty, distinguishing between ambiguous questions and scenarios where the model lacks sufficient knowledge to answer with certainty. When AI lacks awareness of local cultural contexts or regional knowledge, it tends to fill those gaps with generic or stereotyped responses. Therefore, recognizing the limitations of these systems and the importance of being able to state "I don't know" is essential for critical and responsible application in educational settings.
At the end of the lesson, you will be asked to answer a few questions. The answers will be graded, and completing this activity is required to finish and earn credit for the course.
👨🏫 Instructor: Guido Ivetta-
Prefer to watch in a language other than Spanish? Just turn on CC, go to Settings (⚙️) > Subtitles > Auto-translate, and select your language!
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LESSON 8 - ARTIFICIAL INTELLIGENCE FOUNDATIONS FOR BROADENING CULTURAL HORIZONS
What are stereotypes in AI models?
In this lesson, we address stereotypes, also known as social biases, in language models. What is a stereotype? They are generalizations about groups of people often built from limited information or prejudices. If AI models learn from data produced by our societies, isn't it expected that they also learn our prejudices? We analyze how biases originate in training data (which is often non-transparent) and how the strong dominance of English and European languages excludes a large portion of the world's linguistic and cultural diversity.We also distinguish between in-group and out-group stereotypes, observing concrete examples of how these can appear in automated responses. The goal is to recognize that AI is not neutral and that its outputs can reinforce existing inequalities. Through a practical activity, we propose identifying in-group stereotypes and reflecting on out-group stereotypes to understand how technology that relies on stereotypes can impact the educational practices we build.
At the end of the lesson, you will be asked to answer a few questions.
👩🏫 Instructor: Luciana Benotti-
Prefer to watch in a language other than Spanish? Just turn on CC, go to Settings (⚙️) > Subtitles > Auto-translate, and select your language!
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LESSON 9 - ARTIFICIAL INTELLIGENCE FOUNDATIONS FOR BROADENING CULTURAL HORIZONS
Stereotype Validator
In this hands-on activity, you are invited to work directly with biases in generative language models using a stereotype validation tool. The core premise is to formulate pairs of sentences or questions that differ by only one element (such as gender, nationality, profession, or any other social attribute) and observe how the AI responds in each case. By comparing the responses, you can detect whether the model assigns better results to one variant over another, revealing implicit associations or prejudices in its outputs.
The purpose is not to "prove that AI is good or bad," but to develop a critical view that allows you to identify stereotyped patterns in model outputs and reflect on their potential effects in educational and social contexts. The activity encourages recording the observed differences, discussing them with peers, and reflecting on how these biases can influence the way we use and teach with AI.
👨🏫 Instructor: Pietro Palombini
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In this unit, we analyze biases and stereotypes in artificial intelligence, showing how models can reproduce prejudices present in their training data and generate non-neutral responses. Through practical activities, students are invited to identify these patterns and reflect on uncertainty, errors, hallucinations, and system limitations. Alignment and its cultural and pedagogical challenges are also addressed, as well as algorithmic sycophancy, highlighting the importance of using AI with a critical eye, responsibility, and teacher mediation.
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LESSON 10 - ARTIFICIAL INTELLIGENCE FOUNDATIONS FOR BROADENING CULTURAL HORIZONS
Do language models always answer the same way?
In this lesson, we explore a core characteristic of language models: their non-deterministic nature. When asked the same question, they can generate different responses. Why does this happen? Because they do not operate on fixed rules, but rather on probabilities built from vast amounts of data. We analyze how variables such as "temperature" influence the degree of creativity or predictability in responses, and how certain parameters can modify a model's behavior without requiring retraining.
We also examine the so-called system prompt, that invisible initial instruction that guides tone, boundaries, and interaction rules. What are the implications of configurations hidden from the user? To what extent are these systems truly "black boxes"? The goal is to reflect on transparency, auditability, and trust: if behavior can be adjusted without our knowledge, what regulatory and pedagogical challenges arise?
At the end of the lesson, you are invited to answer a few questions.
👨🏫 Instructor: Marcos Gómez-
Prefer to watch in a language other than Spanish? Just turn on CC, go to Settings (⚙️) > Subtitles > Auto-translate, and select your language!
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LESSON 11 - ARTIFICIAL INTELLIGENCE FOUNDATIONS FOR BROADENING CULTURAL HORIZONS
Alignment: Balancing Accuracy and Responsibility
In this lesson, we work with the concept of alignment, understood as the process of fine-tuning artificial intelligence model responses so they respect human values, ethical standards, and specific goals. In the educational sphere, alignment aims to ensure safe and appropriate responses for the school context. An unaligned model might provide risky instructions when given an seemingly innocent prompt, whereas an aligned model prioritizes safety and adapts its output to the student's environment. One of the most widely used techniques for aligning models is Reinforcement Learning from Human Feedback (RLHF), where human evaluators rate different model responses, which are then used to teach the system which types of answers to prioritize.
We also analyze the challenges of alignment. The "correct" response depends on the user's context, age, and culture. A model can fail if it ignores cultural differences, automatically rejects sensitive topics without contextualizing them, or answers from a perspective centered on another country. Cultural alignment requires systems to understand local knowledge and references—such as traditions, holidays, or specific vocabulary—rather than relying solely on a limited global subset. Without research and culturally diverse data, models tend to make errors or oversimplifications. Thus, advancing alignment is not just a technical challenge, but a cultural and pedagogical one as well.
At the end of the lesson, you are invited to answer a few questions.
👩🏫 Instructor: Sofia Martinelli-
Prefer to watch in a language other than Spanish? Just turn on CC, go to Settings (⚙️) > Subtitles > Auto-translate, and select your language!
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Los estudiantes debenMarcar como hechaBelow you will find the lesson slides, designed as a reference to revisit the ideas, questions, and tensions raised in the video. (Please note: The slides are in Spanish).📚 Key Concepts
Cultural Alignment: Ensuring that a language model (AI) produces responses consistent with the values, norms, and cultural contexts of its users.
Reference: https://aclanthology.org/2025.cl-3.7/ https://aclanthology.org/2025.coling-main.567/
A creation by Fundación Vía Libre in collaboration with FAMAF – UNC.
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LESSON 12 - ARTIFICIAL INTELLIGENCE FOUNDATIONS FOR BROADENING CULTURAL HORIZONS
Algorithmic Sycophancy
In this lesson, we address key concepts related to AI design that allow us to reflect on how these systems operate and what kind of relationship we build with them. Algorithmic sycophancy, transparency, concealment of uncertainty, and anthropomorphism are some of the defining features of language models. Understanding them enables us to critically evaluate how we use these technologies and consider the socio-educational implications that arise when these features remain invisible.
We also reflect on how sycophancy can impact pedagogical relationships and trust-building. Models adopt an approachable tone, offer constant availability, and can foster feelings of closeness or trust during interactions, which may influence the teacher–student–AI dynamic. We analyze varying degrees of dependency—ranging from critical and reflective usage to a concerning substitution of human connections—and warn against using AI for emotional support without professional mediation. This lesson invites us to question not only what AI answers, but how it answers and what repercussions it may have on the autonomy and critical judgment of its users.
‼️ Important: In this lesson there is an assignment to complete before watching the lecture. It is important that you carry it out; while this preliminary activity is ungraded, completing it is a requirement to pass the course. At the end of the lesson, you will be asked to answer a few questions. These final answers are graded, and completing them is required to finish and earn credit for the course.
👩🏫 Instructor: Emilia Echeveste-
Prefer to watch in a language other than Spanish? Just turn on CC, go to Settings (⚙️) > Subtitles > Auto-translate, and select your language!
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La unidad aborda la importancia de los benchmarks para evaluar el comportamiento y los sesgos de los modelos de lenguaje, señalando que muchas métricas y datasets están centrados en el inglés y no siempre representan contextos latinoamericanos. En este marco se presenta el Proyecto HESEIA 2024, que combinó formación docente y producción colaborativa de datos mediante la herramienta EDIA, construyendo un dataset con fuerte anclaje local para detectar estereotipos regionales invisibilizados por los modelos actuales. Además, se propone pensar la IA como punto de partida para la reflexión, distinguiendo entre aprender sobre la IA y aprender con la IA, e integrándola en experiencias de aula que promuevan la reapropiación crítica de sus respuestas, la autonomía y el pensamiento situado.
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LESSON 13 - ARTIFICIAL INTELLIGENCE FOUNDATIONS FOR BROADENING CULTURAL HORIZONS
What is the purpose of data collection?
In this lesson, we analyze the importance of data collection in evaluating the behavior of language models. These evaluations are conducted using benchmarks, test sets designed to measure performance and, in particular, detect bias. Bias benchmarks function as controlled experiments containing stereotyped sentences and scenarios to observe how the model responds. However, much of the current research focuses on English, which means metrics, datasets, and mitigation strategies are rooted in specific cultural contexts and do not always represent realities like those in Latin America.
Within this framework, we present the HESEIA 2024 Project, an educational initiative involving teachers and students in Argentina that combined critical AI literacy with collaborative data production. Using the EDIA tool, participants explored biases related to social class, gender, and nationality, building a dataset of nearly 50,000 sentences grounded in local and intersectional contexts. Results showed that many regional stereotypes go undetected by current models. HESEIA thus serves as both a pedagogical and technological intervention: fostering critical thinking while contributing to the creation of fairer, more diverse, and more representative benchmarks.
At the end of the lesson, you are invited to answer a few questions.
👨🏫 Instructor: Guido Ivetta-
Prefer to watch in a language other than Spanish? Just turn on CC, go to Settings (⚙️) > Subtitles > Auto-translate, and select your language!
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LESSON 14 - ARTIFICIAL INTELLIGENCE FOUNDATIONS FOR BROADENING CULTURAL HORIZONS
Looking at Other Experiences
In this lesson, we propose viewing artificial intelligence not as an end point, but as a starting point. The closed answers offered by a model can serve to spark questions, dismantle assumptions, and demonstrate that every statement is situated within a cultural and historical context. As we saw in the lesson "Teaching in Times of Artificial Intelligence," two complementary approaches are distinguished: learning about AI, analyzing it as an object of study—its scope, limits, and biases—and learning with AI, using it as a tool to build knowledge without replacing independent thought. Within this framework, the EDIA tool is revisited as a resource to explore stereotypes without requiring advanced technical expertise.
We also present classroom experiences that integrate AI from a reflective perspective: debates on career choice and gender, analysis of myths in sex education, and historical fact-checking activities using conversational assistants. In all cases, the focus is on the critical re-appropriation of information: not accepting the model's answer as a finished product, but transforming it into an original, contextualized, and well-reasoned production. The goal is to move toward a computational literacy that strengthens autonomy, critical thinking, and the collective construction of knowledge.
At the end of the lesson, you are invited to answer a few questions.
👩🏫 Instructor: Emilia Echeveste-
Los estudiantes debenMarcar como hecha
Prefer to watch in a language other than Spanish? Just turn on CC, go to Settings (⚙️) > Subtitles > Auto-translate, and select your language!
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