The Vulnerability of AI-Based Scoring Systems to Gaming Strategies: A Case Study

Abstract

Recent developments in the use of large-language models have led to substantial improvements in the accuracy of content-based automated scoring of free-text responses. The reported accuracy levels suggest that automated systems could have widespread applicability in assessment. However, before they are used in operational testing, other aspects of their performance warrant examination. In this study, we explore the potential for examinees to inflate their scores by gaming the ACTA automated scoring system. We explore a range of strategies including responding with words selected from the item stem and responding with multiple answers. These responses would be easily identified as incorrect by a human rater but may result in false-positive classifications from an automated system. Our results show that the rate at which these strategies produce responses that are scored as correct varied across items and across strategies but that several vulnerabilities exist.