A New AI Literacy For The Algorithmic Age: Prompt Engineering Or Eductional Promptization?

The introduction and rapid adoption of particular Artificial Intelligence (AI) technologies such as Large Language Models, where among other, Chat Generative Pre-trained Transformer (ChatGPT) is a prominent one, have caused a significant increase in research interest for prompt engineering. Prompt engineering can be loosely described as a new emerging field where one attempts to develop particular methods, strategies, and frameworks with the main aim of organizing and structuring inputs in such a way that sophisticated AI systems can perform different tasks. These methods, techniques, and frameworks can be concepts like in-context learning, Chain-of-Thought (CoT), Retrieval Augmented Generation, ReAct prompting, and Directional Stimulus Prompting. These approaches are mainly developed so users can communicate more effectively and accurately with AI language models and enable obtaining better outputs or responses tailored to queries or goals they set. As an extension and new contribution to the mentioned research field, this conceptual paper aims to introduce, propose, and theorize the notion of ‘educational promptization’. This will be done by connecting prompting to a particular social scientific practice perspective, sociomateriality. The rationale for arguing, however, is related to a need to develop an alternative research perspective to those that already dominate the mentioned research field, a research stream this conceptual paper also aims to engage with. Educational promptization is suggested as there is a demand to develop a more student-centric media literacy, especially as students and teachers engage with AI language interpreters on a daily basis. This means that the proposed educational promptization can arguably be considered to be part of a future AI literacy. An essential component of it is that one has to encompass the relational and symmetrical engagement with AI models. This is perhaps required because students will most likely be challenged to master the knowledge and skills in designing prompts, while simultaneously being capable of critically and meaningfully assessing the output of the prompts they created. In other words, mastering the complex input-and-output engagements with an AI system will be essential in students’ future learning processes.