Test Security in Remote Testing Age: Perspectives from Process Data Analytics and AI
This work tackles the problem of ensuring test integrity in remote settings for educational and certification bodies, but it appears incremental as it builds on existing AI advancements in a specific domain.
The paper addresses test security concerns in remotely proctored high-stakes assessments by leveraging AI and process data analytics to gain deeper insights into test-taking behaviors, demonstrating through real-world examples that these methods can effectively enhance security.
The COVID-19 pandemic has accelerated the implementation and acceptance of remotely proctored high-stake assessments. While the flexible administration of the tests brings forth many values, it raises test security-related concerns. Meanwhile, artificial intelligence (AI) has witnessed tremendous advances in the last five years. Many AI tools (such as the very recent ChatGPT) can generate high-quality responses to test items. These new developments require test security research beyond the statistical analysis of scores and response time. Data analytics and AI methods based on clickstream process data can get us deeper insight into the test-taking process and hold great promise for securing remotely administered high-stakes tests. This chapter uses real-world examples to show that this is indeed the case.