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Teach how AI models process prompts and how interactions can be exploited, to build cognitive resilience against manipulation

Design · All designs
Evidence · no claims cited

Description

The article argues that integrating explicit instruction on how AI models process prompts, recognize system boundaries, and respond to linguistic cues serves a dual purpose in higher education: it builds technical proficiency and fosters "cognitive resilience against manipulation". By teaching both effective interaction and how interactions can be exploited, institutions can cultivate a more security-aware user base capable of identifying and resisting prompt injection attempts.

Design Implications

Context

Requirements

  • Curricula or professional development that cover both legitimate prompt engineering and adversarial exploitation of the same techniques

Constraints

  • Grounded in one AI tool and one grading scenario; the article does not empirically test the educational intervention

Target Learners

  • higher education students
  • higher education faculty

Learning Goals

  • understanding AI prompt processing and system boundaries
  • identifying and resisting direct and indirect prompt injection

Claims

  • Dual Role Prompt Engineering Contextual Vulnerability [+M]

Examples

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Key Sources

  • Niklas Humble. (2026). Ethical Implications of Prompt Injection in AI-mediated Grading: An Adversarial Red-Team Evaluation. Journal of Academic Ethics. https://doi.org/10.1007/s10805-026-09758-9
Page metadata
Field Value
type design
id teach-prompt-processing-for-cognitive-resilience
title Teach how AI models process prompts and how interactions can be exploited, to build cognitive resilience against manipulation
description The article argues that integrating explicit instruction on how AI models process prompts, recognize system boundaries, and respond to linguistic cues serves a dual purpose in higher education: it builds technical pro...
status review
generated by process:settle-candidates on 2026-10-23
trust unverified — no one has confirmed this page's content
sources 1 — niklas-humble-2026

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