Abstract:

This paper explores the integration of Generative Artificial Intelligence (GenAI) methods into computer science courses, examining their implications from both lecturer and student perspectives. With the rapid advancement of AI technologies, there is a growing need to incorporate GenAI methods into educational curricula to prepare students for the evolving landscape of computer science.

From the student viewpoint, we discuss the benefits of integrating GenAI methods into coursework. GenAI can improve problem-solving skills by offering a diverse array of solutions for comparison and evaluation in modeling tasks such as data and process models, as well as in coding solutions and algorithm design. This fosters a deeper understanding of the underlying principles and encourages critical analysis of different approaches. By leveraging GenAI algorithms and platforms, students can engage in hands-on activities that foster creativity, innovation, and analytical reasoning.

A great advantage of GenAI is the ability to explain algorithms, models or even code or individual elements of these artifacts, e.g. individual steps of algorithms or programs. Students can always ask exactly the questions they have (and GenAI tools have much more patience than lecturers). We illustrate how GenAI-driven projects and assignments can cultivate a deeper understanding of complex computational concepts while encouraging collaboration and interdisciplinary thinking. By applying GenAI, students can refine their coding skills, optimize algorithms, and develop efficient solutions to real-world problems. This fosters a deeper understanding of the underlying principles and encourages critical analysis of different approaches.

From the lecturer's viewpoint, we discuss strategies for integrating GenAI tools into various student learning activities. This includes leveraging GenAI for coding assistance, such as providing guidance on syntax like the filter and pipes pattern in Linux. GenAI can offer step-by-step explanations for solving data science problems, aiding students in understanding complex concepts effectively. Additionally, we explore how GenAI can be utilized to enhance writing skills by offering suggestions on grammar, style, and structure, thus facilitating clearer communication of technical concepts in written assignments and reports. With an emphasis on the importance of adaptability and continuous learning, we propose strategies for educators to effectively incorporate GenAI concepts, tools, and applications into their teaching methodologies.

From these viewpoints, we propose a new approach to integrating GenAI methods into computer science education that considers the diverse needs and perspectives of both educators and learners. By embracing GenAI technologies responsibly, educators can empower students to become versatile problem solvers and critical thinkers equipped to tackle the challenges of the digital age. Overall, this paper offers a comprehensive framework for integrating GenAI methods into computer science education, facilitating the development of essential skills and competencies needed to thrive in an AI-driven world.

Keywords:

Generative AI in education, computer science courses, GenAI tools, lecturer and student perspectives.