MARC

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022 |a 2048-8637 
022 |a 2048-8645 
035 |a 3279067020 
045 2 |b d20251001  |b d20251031 
084 |a 183529  |2 nlm 
100 1 |a Hatakka, Mathias 
245 1 |a Lessons Learned from Creating Course Content using Generative AI 
260 |b Academic Conferences International Limited  |c Oct 2025 
513 |a Conference Proceedings 
520 3 |a The release of generative AI tools such as OpenAI's ChatGPT has sparked interest in their implications for education. While early discourse emphasized concerns about plagiarism and academic integrity, recent studies have begun to explore the potential of these tools to support teaching and learning. This paper presents a case study on the use of ChatGPT in the redesign of a first-year systems development project course for informatics students. The course required the integration of various course materials, making it a suitable context for evaluating generative AI's role in course material development. The aim of the study is to present lessons learned from using ChatGPT in the development of course content. Drawing on our practical experience as course designers and instructors, we outline lessons learned from using ChatGPT in the creation of key course elements, including case descriptions, SQL scripts, and requirements specifications. We found that ChatGPT was effective for generating coherent initial drafts of content, but its outputs often required refinement to ensure pedagogical alignment. Challenges included the generation of misleading or irrelevant non-functional requirements and logically flawed code, despite syntactic correctness. Our findings highlight the importance of prompt engineering, critical review, and maintaining a human-in-the-loop approach. We conclude that while ChatGPT can significantly reduce development time for some tasks, it should be used as a complementary tool. This study contributes practical insights to the growing field of AI- assisted education. 
610 4 |a OpenAI 
653 |a Teaching 
653 |a Documentation 
653 |a Prompt engineering 
653 |a Teachers 
653 |a Chatbots 
653 |a Education 
653 |a Educational materials 
653 |a Engineering 
653 |a Learning 
653 |a Language 
653 |a Pedagogy 
653 |a Requirements specifications 
653 |a Redesign 
653 |a Generative artificial intelligence 
653 |a Plagiarism 
653 |a Case studies 
653 |a Content creation 
653 |a Systems development 
653 |a Design 
653 |a Informatics 
653 |a Customization 
653 |a Large language models 
653 |a Sustainable Development 
653 |a Course Descriptions 
653 |a Literature Reviews 
653 |a Syntax 
653 |a Course Objectives 
653 |a Experiments 
653 |a Educational Objectives 
653 |a Artificial Intelligence 
653 |a Student Motivation 
653 |a Material Development 
653 |a Use Studies 
653 |a Language Processing 
653 |a Database Management Systems 
653 |a Course Content 
653 |a Educational Resources 
653 |a Information Science 
653 |a Instructional Materials 
653 |a Cheating 
653 |a Time 
653 |a Databases 
653 |a Student Needs 
653 |a Outcomes of Education 
700 1 |a Ask, Andreas 
773 0 |t European Conference on e-Learning  |g (Oct 2025), p. 146-154 
786 0 |d ProQuest  |t Education Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3279067020/abstract/embedded/H09TXR3UUZB2ISDL?source=fedsrch 
856 4 0 |3 Full Text  |u https://www.proquest.com/docview/3279067020/fulltext/embedded/H09TXR3UUZB2ISDL?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3279067020/fulltextPDF/embedded/H09TXR3UUZB2ISDL?source=fedsrch