Machine vs Machine: Using AI to Tackle Generative AI Threats in Assessment

TL;DR

This paper proposes a comprehensive theoretical framework combining static analysis and dynamic testing to evaluate and address vulnerabilities in AI-generated assessment content, aiming to preserve academic integrity amid advanced generative AI tools.

Contribution

It introduces a novel dual strategy paradigm for assessment vulnerability evaluation, integrating eight static analysis elements with dynamic testing methods.

Findings

Framework effectively differentiates human from AI-generated work.

Combines static and dynamic analysis for comprehensive assessment.

Provides a theoretical basis for vulnerability scoring and thresholds.

Abstract

This paper presents a theoretical framework for addressing the challenges posed by generative artificial intelligence (AI) in higher education assessment through a machine-versus-machine approach. Large language models like GPT-4, Claude, and Llama increasingly demonstrate the ability to produce sophisticated academic content, traditional assessment methods face an existential threat, with surveys indicating 74-92% of students experimenting with these tools for academic purposes. Current responses, ranging from detection software to manual assessment redesign, show significant limitations: detection tools demonstrate bias against non-native English writers and can be easily circumvented, while manual frameworks rely heavily on subjective judgment and assume static AI capabilities. This paper introduces a dual strategy paradigm combining static analysis and dynamic testing to create a comprehensive theoretical framework for assessment vulnerability evaluation. The static analysis component comprises eight theoretically justified elements: specificity and contextualization, temporal relevance, process visibility requirements, personalization elements, resource accessibility, multimodal integration, ethical reasoning requirements, and collaborative elements. Each element addresses specific limitations in generative AI capabilities, creating barriers that distinguish authentic human learning from AI-generated simulation. The dynamic testing component provides a complementary approach through simulation-based vulnerability assessment, addressing limitations in pattern-based analysis. The paper presents a theoretical framework for vulnerability scoring, including the conceptual basis for quantitative assessment, weighting frameworks, and threshold determination theory.