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Title:
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DESIGNING CHATBOT-RESISTANT INTERACTIVE QUESTIONS FOR COMPUTER SCIENCE ASSESSMENT |
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Author(s):
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Johannes Knaut, Michael Wiehl and Mike Altieri |
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ISBN:
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978-989-8704-72-6 |
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Editors:
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Demetrios G. Sampson, Dirk Ifenthaler and Pedro Isaías |
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Year:
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2025 |
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Edition:
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Single |
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Keywords:
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AI-Resistant Assessment, Interactive Learning, Computational Thinking, Programming Education, Chatbot Misuse |
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Type:
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Short Paper |
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First Page:
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422 |
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Last Page:
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426 |
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Language:
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English |
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Cover:
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Full Contents:
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Paper Abstract:
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The rise of generative AI tools such as ChatGPT has introduced new challenges for digital assessment in higher
education, particularly in programming lectures. This paper presents a practical approach to designing automatically
assessed questions to reduce susceptibility to AI-generated answers while promoting deeper student engagement. Three
programming questions in a university-level online competency test were redesigned using three pedagogically grounded
principles: interactive exploration, dynamic process encoding and contextual specificity. Evaluation using performance
data from two student cohorts and direct testing against an AI model showed that the redesigned questions led to lower
average scores but more code runs per question, indicating increased student engagement and reduced reliance on
automated solutions. This work contributes actionable strategies for maintaining assessment integrity in an AI-rich
learning environment. |
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