From Learners to Contributors: Redefining Bachelor's and Master's Theses in the Context of Generative AI Tools

The integration of Generative AI (GenAI) tools, such as ChatGPT, into academic environments has raised significant questions about the traditional format and integrity of final theses, particularly in engineering studies. This paper investigates the goal of final theses and the appropriate actions to achieve them in the context of modern technological advancements. Utilizing Dettmer's methodology, we identify and analyze the underlying causes of challenges introduced by GenAI tools. Our findings highlight the need for curriculum adaptations to address these challenges effectively. These include shifting the emphasis from thesis components easily generated by AI to students' contributions from a research project. These adaptations aim to enhance the quality and originality of final theses, foster deeper student engagement in research projects, and introduce innovative, multimedia formats for thesis presentation. The proposed solutions not only uphold academic integrity but also leverage the potential of GenAI tools to enrich the educational experience.

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Publication Details

Published
2024-09-18
DOI
https://doi.org/10.1109/icdle63439.2024.00010
Primary Topic
AI-based Problem Solving and Planning
Type
article
Field-Weighted Citation Impact
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article

From Learners to Contributors: Redefining Bachelor's and Master's Theses in the Context of Generative AI Tools

Igor Miladinović, Sigrid Schefer-Wenzl
AI-based Problem Solving and Planning
article

From Learners to Contributors: Redefining Bachelor's and Master's Theses in the Context of Generative AI Tools

Igor Miladinović, Sigrid Schefer-Wenzl
article en

Abstract

The integration of Generative AI (GenAI) tools, such as ChatGPT, into academic environments has raised significant questions about the traditional format and integrity of final theses, particularly in engineering studies. This paper investigates the goal of final theses and the appropriate actions to achieve them in the context of modern technological advancements. Utilizing Dettmer's methodology, we identify and analyze the underlying causes of challenges introduced by GenAI tools. Our findings highlight the need for curriculum adaptations to address these challenges effectively. These include shifting the emphasis from thesis components easily generated by AI to students' contributions from a research project. These adaptations aim to enhance the quality and originality of final theses, foster deeper student engagement in research projects, and introduce innovative, multimedia formats for thesis presentation. The proposed solutions not only uphold academic integrity but also leverage the potential of GenAI tools to enrich the educational experience.

Hochschule Campus Wien (AT)
Quality Education
Openalex Percentile: Top 72%
AI-based Problem Solving and Planning
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