To adapt the proposed evaluation framework for assessing the long-term impact of the AI-assisted proctoring system, a longitudinal study design could be implemented. This would involve collecting data over multiple testing cycles to evaluate trends in test security and user experience.
Key performance indicators (KPIs) should be established to measure the effectiveness of the proctoring system over time. These KPIs could include the frequency of detected cheating incidents, the accuracy of gaze predictions, and the consistency of proctoring decisions. Additionally, surveys and feedback mechanisms should be employed to gather qualitative data from both test takers and proctors regarding their experiences with the system.
Incorporating a control group that utilizes traditional proctoring methods would provide a comparative baseline, allowing for a clearer assessment of the AI system's impact.
Furthermore, the evaluation framework could include a feedback loop where insights gained from the data are used to iteratively improve the AI-assisted system. This could involve refining the gaze detection algorithms, enhancing user interfaces based on proctor feedback, and adjusting the training of proctors to better utilize the AI tools.
By focusing on both quantitative and qualitative measures, the adapted evaluation framework would provide a comprehensive understanding of the AI-assisted proctoring system's long-term effects on test security and the overall testing experience.