Abstract:

In today's increasingly artificial intelligence (AI)-driven world, fostering AI literacy among students is crucial for enhancing the quality and relevance of their scientific writing. Recent exploratory studies such as Cieliebak et. al 2023 have shed light on the integration of AI tools like ChatGPT into scientific writing, particularly in the context of undergraduate thesis writing. These studies have shown promising results in terms of improving the writing process through AI assistance. However, uncertainties persist regarding the ideal integration of AI tools into pedagogical practices. The following study builds upon prior research by exploring the integration of AI tools into scientific writing education and addressing the uncertainties, e.g. prompting, selection of AI tools, post-editing, knowledge acquisition, or AI literacy. Using a design-based research (DBR) approach, we developed a new course design for scientific writing that incorporates AI tools as part of the Communication Competence Courses program for undergraduate engineering students at the Zurich University of Applied Sciences (ZHAW). The School of Engineering’s approach to AI is very open, allowing students to use AI generated content in any written assignment or test. Subsequently, the new course provides students with a structured framework for navigating the complexities of scientific writing using AI in a student-centered format (prioritizing workshops over lectures), as opposed to the traditional teacher-centered approach. At each stage of the writing process - topic selection, contextual clarification, structuring, and text composition - relevant AI tools are seamlessly incorporated to facilitate various writing tasks, thus enhancing students' understanding of the writing process. The first iteration of the new course is currently underway, with continuous testing and evaluation scheduled to conclude at the end of spring term 2024. Feedback from students and lecturers will be gathered and analyzed to identify the strengths as well as areas for improvement in the course design. This will be followed by a second trial period in the spring semester of 2025, during which the redesigned course will be tested again, incorporating the lessons learned from the initial feedback. This iterative approach aims to refine the course design systematically based on student and lecturer input, ultimately optimizing the integration of AI tools and addressing evolving writing challenges. This study thus underscores the importance of addressing uncertainties in AI integration in educational contexts. The iterative approach to course design allows for continuous refinement based on student and lecturer feedback, highlighting the dynamic nature of pedagogical practice. In addition to course design modifications, considerations for training and supporting faculty in the effective use of AI tools are explored. This study therefore contributes to the ongoing discourse on the integration of AI tools into the teaching of scientific writing, emphasizing the potential of dynamic course designs to enhance students' scientific writing skills and prepare them for the demands of a rapidly evolving digital landscape.

Keywords:

Scientific writing, AI literacy, design-based research, AI tools, course design, student-centered learning.