AI-Tutoring in Software Engineering Education

Eduard Frankford, Clemens Sauerwein, Patrick Bassner, Stephan Krusche, Ruth Breu
4/3/2024
cs.SEcs.AI

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Abstract

With the rapid advancement of artificial intelligence (AI) in various domains, the education sector is set for transformation. The potential of AI-driven tools in enhancing the learning experience, especially in programming, is immense. However, the scientific evaluation of Large Language Models (LLMs) used in Automated Programming Assessment Systems (APASs) as an AI-Tutor remains largely unexplored. Therefore, there is a need to understand how students interact with such AI-Tutors and to analyze their experiences. In this paper, we conducted an exploratory case study by integrating the GPT-3.5-Turbo model as an AI-Tutor within the APAS Artemis. Through a combination of empirical data collection and an exploratory survey, we identified different user types based on their interaction patterns with the AI-Tutor. Additionally, the findings highlight advantages, such as timely feedback and scalability. However, challenges like generic responses and students' concerns about a learning progress inhibition when using the AI-Tutor were also evident. This research adds to the discourse on AI's role in education.

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Cite this paper

@article{frankford2024aitutoring,
  title  = {AI-Tutoring in Software Engineering Education},
  author = {Eduard Frankford and Clemens Sauerwein and Patrick Bassner and Stephan Krusche and Ruth Breu},
  year   = {2024},
  eprint = {2404.02548},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2404.02548v2}
}

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