Custom Instructions for LLMs to Support Epistemic Awareness

A short instructional activity in which educators and learners configure custom instructions for a Large Language Model in order to develop epistemic awareness, critical reasoning, and responsible AI-supported learning under conditions of uncertainty.

DigCompEdu Competence Area(s)

Time

Preparation time minutes

Implementation time minutes

Educator Progression Level

Target groups

Minimum student digital skill level

Intermediate

Minimum student language level

🎓 Learning Objectives

Educators and learners will be able to…
Configure custom instructions that support critical reasoning, transparency, and responsible AI-supported work
Explain that Large Language Models do not possess knowledge, but generate probabilistic reasoning based on patterns in data
Reflect on their own responsibility when using AI tools in educational and learning contexts.

Answer AI‑generated quizzes to check understanding of a topic.
Reflect on question types (multiple‑choice, recall, short answer) and identify areas for improvement.
Provide feedback on the accuracy and relevance of AI‑generated questions.

Ingredients

Item / Resource	Quantity	Purpose
AI tool (e.g. ChatGPT or other LLM with custom instructions)	1 per learner	Used to configure a critical interaction mode that supports epistemological awareness and responsible AI use.
Custom instruction text (provided)	1	Establishes rules for handling uncertainty, credibility, and critical dialogue when interacting with the AI.

Utensils

Internet-connected device + access to an LLM with custom settings

🥣 Preparation – Before Implementation

The teacher learns first and then teaches the learners.
Before classroom use, the educator inserts the provided custom instructions into the AI tool and independently explores their effect by asking ordinary questions and observing changes in AI behaviour. This exploration phase allows the educator to investigate how the instructions influence responses related to uncertainty, missing information, and critical counterpoints.
Only after this exploratory phase does the educator introduce the activity to learners.

🔥 Implementation Steps

The educator briefly introduces the key idea from the shared reference text found in References & Resources: that Large Language Models do not “know”, but generate responses based on likelihood, and that AI output must therefore be treated as provisional reasoning rather than authoritative knowledge.
Students open their AI tool and insert the custom instruction text provided in References & Resources into Custom Instructions / Settings. The text (in image form) presents a structured set of rules for handling uncertainty, credibility, and dialogue.
Students ask the AI one or two ordinary questions and observe how the responses differ when the custom instructions are active, particularly in situations involving uncertainty or incomplete information.
The educator facilitates a brief discussion on how the AI’s behaviour changed and how the custom instructions support an epistemological approach to AI use.