Generative and Non-Generative AI

GenAI is a subset of AI focused on producing new content such as text, pictures, videos, songs, mathematical equations, computer programs, typically in response to a question or command (“prompt”). These outputs are generated based on large volumes of training data. To do this, GenAI relies on advanced machine-learning techniques, such as neural networks based on transformers (notably Generative Pre-trained Transformer (GPT)), embeddings, tokens, etc. Most people have experienced GenAI via chatbots based on large language models (LLMs) such as OpenAI’s ChatGPT, Google’s Gemini, Microsoft’s Co-pilot, Anthropic’s Claude, Mistral’s LeChat or Deepseek’s Deepseek.

In contrast, non-generative AI systems mainly produce predictions, classifications, recommendations, and ratings, for example for movies, books or other products and services. While they may use similar techniques as GenAI, their primary goal is to identify patterns and relationships in vast amounts of data, rather than create new content. Those AI systems are sometimes referred to as “rule-based”, “predictive”, or “good old-fashioned” AI. Despite often being less visible to end users, these systems are still powerful and have a variety of uses, including in education. They are embedded in assistive technologies, for example for students with special needs, used to adapt learning to personal needs within intelligent tutoring systems, to score assessments or to predict whether students are at risk of dropping out (OECD, 2021).