Counter-prompting: deliberately probing AI systems for their assumptions and using responses as data about the apparatus¶

Strategy · All strategies
Evidence · no claims cited

Description¶

Counter-prompting is a Critical Interrogation practice in which users probe AI systems reflexively to illuminate their own limitations. The paper defines it as "the practice of deliberately probing AI systems for their assumptions, asking them to represent alternative perspectives, and using their responses as data about the structuring assumptions of the apparatus." This transforms the user from a recipient of AI knowledge into an analyst of AI knowledge production.

Design Implications¶

Context¶

Requirements¶

• Users must treat AI responses as data about the system's structuring assumptions rather than as transparent reports on the world

Constraints¶

• The paper presents this as a conceptual practice within its theoretical framework, not an empirically tested instructional method

Target Learners¶

• Students and adult learners developing critical AI literacy

Target Learning Goals¶

• Recognizing whose knowledge traditions are centered or marginal and seeking alternative framings of AI outputs

Affordances¶

• Three Part Ai Literacy Epistemic Agency Framework

Related Strategies¶

• Generative prompting: using AI as a thinking partner through iterative dialogue rather than an answer machine
• Socratic Questioning
• Questioning Techniques
• Pedagogy of deliberate counter-positioning: use generative AI in reverse so students interrogate generated positions
• Teach prompting and AI literacy as epistemic practice with structured guidance
• Scenario A: designing and deconstructing AI prompts as an epistemic practice

Examples¶

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Key Sources¶

• Brady D. Lund and Zoë Abbie Teel. (2026). AI Literacy: An Exercise in Power-Knowledge. University of North Texas. https://arxiv.org/abs/2607.27547