Computer-assisted language testing (CALT) has profoundly transformed language assessment by incorporating advanced technological innovations into the testing, measurement and evaluation process. This paper provides a brief review and analysis of the historical evolution, methodologies, benefits, challenges, and prospective developments in CALT. Initially constrained to paper-based formats, language testing has evolved with the advent of computerbased tests (CBTs) and computer-adaptive testing (CAT), which dynamically adjust item difficulty based on test-taker performance. CALT methodologies now encompass multimedia components, including audio and video, and leverage natural language processing (NLP) techniques for automatic response analysis. These advancements enhance the reliability, validity, and accessibility of language assessments, offering immediate feedback and customizable testing experiences. Despite these benefits, CALT faces significant challenges, such as technical issues, security and privacy concerns, fairness in automated scoring, and reliance on technology. Case studies, such as the studies on TOEFL iBT and Duolingo English Test, illustrate the practical efficacy of CALT. Looking forward, advancements in artificial intelligence, machine learning, virtual reality, and augmented reality hold promise for further innovation in language testing. Ethical considerations and the establishment of best practices along with developing quality standards and ethical codes will be crucial to ensure quality, fairness and inclusivity. Ultimately, balancing technological advancements with human oversight will be essential for the sustained success and equity of language assessment.
