Student Learning Performance Evaluation: Mitigating the Challenges of Generative AI Chatbot Misuse in Student Assessments

Since the launch of ChatGPT, a growing number of generative artificial intelligence (AI) chatbots have entered the market. Although chatbots have the potential to help students learn, misusing them to complete assessments raises questions about the authenticity of the work and puts students at risk of academic misconduct. Given the crucial role of assessments in evaluating students’ learning performance, uncertainties about the authenticity of the work call into question the extent to which students have achieved the intended learning outcomes. This study conducted a thematic analysis to provide an overview of the challenges that chatbot misuse may pose to student learning performance evaluation, followed by the various mitigation strategies to overcome these challenges. This study searched the Education Resources Information Centre (ERIC) database for peer-reviewed articles published in scholarly journals after 30 November 2022 (the launch date of ChatGPT, as this study focuses on generative AI rather than other types of AI) and until 30 April 2024. The thematic analysis of 17 articles identified five major themes (and respective sub-themes) in the discussions of these articles, i.e., reasons students use chatbots for assessments, challenges that chatbots may pose to student learning performance evaluation, mitigation strategies, detection strategies, and counter-detection strategies. As chatbots become more prevalent and powerful, the study's findings provide education stakeholders with insightful information on the implications of students misusing chatbots for assessments and how this affects their learning performance evaluation.

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Publication Details

Journal
European Conference on e-Learning
Published
2024-10-23
DOI
https://doi.org/10.34190/ecel.23.1.2567
Citations
2
Primary Topic
Online Learning and Analytics
Type
article
Field-Weighted Citation Impact
0.82
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article

Student Learning Performance Evaluation: Mitigating the Challenges of Generative AI Chatbot Misuse in Student Assessments

Chun Meng Tang, Lee Yen Chaw
2 citations
European Conference on e-Learning
Online Learning and Analytics
0.82
article

Student Learning Performance Evaluation: Mitigating the Challenges of Generative AI Chatbot Misuse in Student Assessments

Chun Meng Tang, Lee Yen Chaw
article en
2 citations

Abstract

Since the launch of ChatGPT, a growing number of generative artificial intelligence (AI) chatbots have entered the market. Although chatbots have the potential to help students learn, misusing them to complete assessments raises questions about the authenticity of the work and puts students at risk of academic misconduct. Given the crucial role of assessments in evaluating students’ learning performance, uncertainties about the authenticity of the work call into question the extent to which students have achieved the intended learning outcomes. This study conducted a thematic analysis to provide an overview of the challenges that chatbot misuse may pose to student learning performance evaluation, followed by the various mitigation strategies to overcome these challenges. This study searched the Education Resources Information Centre (ERIC) database for peer-reviewed articles published in scholarly journals after 30 November 2022 (the launch date of ChatGPT, as this study focuses on generative AI rather than other types of AI) and until 30 April 2024. The thematic analysis of 17 articles identified five major themes (and respective sub-themes) in the discussions of these articles, i.e., reasons students use chatbots for assessments, challenges that chatbots may pose to student learning performance evaluation, mitigation strategies, detection strategies, and counter-detection strategies. As chatbots become more prevalent and powerful, the study's findings provide education stakeholders with insightful information on the implications of students misusing chatbots for assessments and how this affects their learning performance evaluation.

European Conference on e-LearningVol. 23(1)
James Cook University Singapore (SG), UCSI University (MY)
Openalex Percentile: Top 18%
Online Learning and Analytics
0.82
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Student Learning Performance Evaluation: Mitigating the Challenges of Generative AI Chatbot Misuse in Student Assessments — Chun Meng Tang, Lee Yen Chaw · European Conference on e-Learning (2024) | TGRS Research Map | TGRS