Developing AI-Resistant Assessments in an Introductory Computer Science Class
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Popescu, Diana Maria
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Abstract
Since the public launch of ChatGPT on November 20, 2022, there has been significant interest in its role in education. While some analyses focus on its potential to support student learning, a parallel line of investigation examines its potential to facilitate the false
demonstration of competency on assessments[1, 2, 3]. A broader underlying question is whether certain skills remain valuable to teach in the age of artificial intelligence, particularly in developing a new generation of computer programmers who continue to employ
critical problem-solving skills in their work. The work presented in this thesis is split into two research stages that examine the impact of large language models (LLMs), such as ChatGPT-4, on a college-level introductory computing course offered simultaneously as
a massive open online course (MOOC) on the edX platform. The first stage targets the free version of ChatGPT while the second one builds on the outcomes discovered and adds in relation to the paid version of GPT. The study focuses on the strengths and limitations
of LLMs in solving coding assignments, while also aiming to identify problems that are resistant to LLM solutions, so these types of specific categories could be used by other instructors in their MOOC courses to provide a better experience for students. The thesis
explores GPT’s proficiency in various areas, including pseudo-code interpretation, handling multiple correct answers, and addressing complex problem statements. The goal is
to create a robust framework that discourages over-reliance on AI assistance from some
students while preserving the scalability of the course. This research provides insights into the dynamics of AI in education and emphasizes the need for a balanced approach between technological assistance and genuine student participation.
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Undergraduate Research Option Thesis