Adaptive testing, such as the model used in the GRE, exemplifies an innovative approach to computer-mediated assessment, offering personalized evaluation by adjusting question difficulty based on student performance. This method enhances precision, efficiency, and relevance, as it tailors assessments to individual ability levels while reducing testing time. Additionally, the immediate feedback provided fosters timely decision-making for students and institutions.

However, the approach also poses challenges, including potential inequities for students without reliable access to technology, heightened test anxiety due to dynamic question difficulty, and the risk of algorithmic bias if systems are not carefully designed. Adaptive testing is most effective for assessing objective knowledge, but it may struggle with more subjective or creative skills.

By addressing these challenges—ensuring accessibility, minimizing bias, and expanding its application to more diverse skill sets—adaptive testing has the potential to transform assessments in education, corporate training, and language learning. Its flexibility and precision make it a valuable tool for the future of personalized learning, provided that its limitations are managed effectively.