Challenge in Applying ChatGPT in Education: Evaluating Potential Drawbacks through Failure Modes and Effects Analysis and Logarithm Methodology of Additive Weights
TL;DR
An integrated LMAW–DNMA–FMEA framework is developed to identify, prioritize, and evaluate the most critical risks associated with ChatGPT adoption in higher education, indicating that overreliance on AI-generated content, degradation of critical thinking skills, and unreliable referencing represent the most significant challenges.
Abstract
Large language models, such as ChatGPT, are transforming higher education by supporting personalized learning, content generation, and academic assistance. However, their widespread adoption also introduces educational risks that remain insufficiently prioritized in the literature. This study develops an integrated LMAW–DNMA–FMEA framework to identify, prioritize, and evaluate the most critical risks associated with ChatGPT adoption in higher education. Expert judgments are used to assess the relative importance and severity of identified risks, enabling a systematic ranking of potential failure modes. The findings indicate that overreliance on AI-generated content, degradation of critical thinking skills, and unreliable referencing represent the most significant challenges, while issues related to creativity and originality also require attention. Based on the results, targeted mitigation strategies are proposed for educators, developers, and policymakers to support the responsible integration of generative AI into educational environments. The proposed framework provides a structured decision-support approach for AI risk assessment in education and contributes to the development of evidence-based strategies for the effective adoption of large language models in higher education.