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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM31217

A Survey on the Efficacy and User Experience of AI-Driven Questioners to Improve Self-Assessment

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-

Subject:

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Paper ID:

IJSREM31217

Volume:

8 |

4 |

Apr 2024

Pages:

Publication Frequency:

Monthly (12 issues per year)

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Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

MW

Mohammad Waris

MKT

Mahesh Kumar Tiwari

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Abstract

In order to give students critical feedback on their understanding and development, assessments are an essential part of their educational journeys. Nevertheless, traditional assessment techniques frequently become less relevant as education moves more and more to digital platforms, creating a gap in thorough self-assessment procedures. This study investigates the effectiveness and user experience of augmenting self-assessment in digital learning settings by integrating an artificial intelligence (AI) Questioner system. When students study on their own, they interact with the material on their own, and the lack of formal evaluations may make it more difficult for them to determine what they have learned and where they still need to improve. Through the use of artificial intelligence (AI) and machine learning algorithms, the AI-based Questioner functions as a dynamic tool that actively involves students in self-assessment tasks while they interact with lectures or PDFs, among other digital resources. This study compares traditional classroom-based evaluations with AI-based self-assessment tools to see which is more effective. It looks at the differences in learning outcomes between students who use AI-Based Questioner for assessment and self-study and those who attend regular classrooms. Using a mixed-methods approach, this study assesses how AI-driven interventions affect learners’ self-assessment practices and overall learning experiences by combining quantitative analysis of user interaction data with qualitative participant input. This study’s conclusions highlight how AI-based Questioner systems might improve self-study and self-evaluation procedures in online learning settings. The AI-powered Questioner encourages metacognitive awareness and active involvement by giving students quick, individualized feedback. The study’s findings ultimately advance our knowledge of how technology may help students assess their own learning and develop autonomous learning skills. Teachers and instructional designers may build more flexible and learner-centered digital learning environments by utilizing AI-based Questioners, giving students the ability to take charge of their education.

KEYWORDS

Self-Assessment Augmenting Digital Learning Self-Study Assessment Practices and Qualitative.

Cite This Article

Mohammad Waris, Mahesh Kumar Tiwari, "A Survey on the Efficacy and User Experience of AI-Driven Questioners to Improve Self-Assessment," International Journal of Scientific Research in Engineering and Management, vol. 8, no. 4, Apr 2024.

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12/31/45402

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Review Rounds: 2

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