The integration of AI tools like ChatGPT into educational assessments, particularly in the context of Multivariable Calculus, represents a transformative approach to personalized and scalable learning. This study examines the Exams as a Service (EaaS)-Flipped Chatbot Test (FCT) framework, implemented through the AIQuest platform, to explore how chatbots can support assessment processes while addressing risks related to automation and academic integrity. The methodology combines static and dynamic assessment modes within a cloud-based environment that generates, evaluates, and provides feedback on student responses. Quantitative survey data and qualitative written reflections were analyzed using a mixed-methods approach, incorporating Grounded Theory to identify emerging cognitive patterns. The results reveal differences in students’ engagement, performance, and reasoning patterns between AI-assisted and non-AI assessment conditions, highlighting the role of structured AI-generated feedback in supporting reflective and metacognitive processes. Quantitative results indicate higher and more homogeneous performance under the reverse evaluation, while survey responses show generally positive perceptions of feedback usefulness and task appropriateness. This study contributes integrated quantitative and qualitative evidence on the design of AI-assisted evaluation frameworks as formative and diagnostic tools, offering guidance for educators to implement AI-based evaluation systems.
Navas et al. (2026) studied this question.