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Chatbots in Multivariable Calculus Exams: Innovative Tool or Academic Risk?

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Title: Chatbots in Multivariable Calculus Exams: Innovative Tool or Academic Risk?
Authors: Navas, Gustavo1 (AUTHOR) gnavas@ups.edu.ec, Proaño-Orellana, Julio1,2 (AUTHOR), Orizondo, Rogelio2,3 (AUTHOR), Navas-Reascos, Gabriel E.3,4 (AUTHOR), Navas-Reascos, Gustavo4,5 (AUTHOR)
Source: Education Sciences. Jan2026, Vol. 16 Issue 1, p160. 21p.
Subject Terms: *Mixed methods research, *Student engagement, *Evaluation methodology, *Education ethics, *Intelligent tutoring systems, Chatbots, Multivariable calculus
Abstract: 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. [ABSTRACT FROM AUTHOR]
Copyright of Education Sciences is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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DbLabel: Education Research Complete
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PubType: Academic Journal
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  Data: Chatbots in Multivariable Calculus Exams: Innovative Tool or Academic Risk?
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  Data: <searchLink fieldCode="JN" term="%22Education+Sciences%22">Education Sciences</searchLink>. Jan2026, Vol. 16 Issue 1, p160. 21p.
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  Data: *<searchLink fieldCode="DE" term="%22Mixed+methods+research%22">Mixed methods research</searchLink><br />*<searchLink fieldCode="DE" term="%22Student+engagement%22">Student engagement</searchLink><br />*<searchLink fieldCode="DE" term="%22Evaluation+methodology%22">Evaluation methodology</searchLink><br />*<searchLink fieldCode="DE" term="%22Education+ethics%22">Education ethics</searchLink><br />*<searchLink fieldCode="DE" term="%22Intelligent+tutoring+systems%22">Intelligent tutoring systems</searchLink><br /><searchLink fieldCode="DE" term="%22Chatbots%22">Chatbots</searchLink><br /><searchLink fieldCode="DE" term="%22Multivariable+calculus%22">Multivariable calculus</searchLink>
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  Data: 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. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Education Sciences is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3390/educsci16010160
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        Text: English
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      – SubjectFull: Multivariable calculus
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      – TitleFull: Chatbots in Multivariable Calculus Exams: Innovative Tool or Academic Risk?
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              Text: Jan2026
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