🤖 AI Summary
This study addresses the absence of systematic mechanisms in higher education institutions to effectively align institutional policies, curricula, and students’ generative artificial intelligence (GenAI) practices, which has led to regulatory ambiguity and academic integrity risks. Employing a mixed-methods approach—including policy document analysis, a survey of 151 computer science students, quantitative statistical analysis, and thematic synthesis of open-ended responses—the research identifies ChatGPT as the predominant GenAI tool, primarily used for research assistance, programming, and text processing. Findings reveal widespread gaps in students’ awareness of institutional policies and underlying structural deficiencies. In response, the study proposes the first lightweight, iterative GenAI adoption framework tailored for higher education, integrating governance, pedagogy, and assessment to bridge terminological vagueness, regulatory gaps, and misalignment between policy and practice, demonstrating its feasibility and practical utility in guiding systematic GenAI integration.
📝 Abstract
The rapid development of GenAI technologies is transforming learning, assessment, and academic production in higher education. Despite increasing student adoption, many institutions lack operational mechanisms to systematically align regulations and curricula with evolving generative artificial intelligence practices, creating regulatory ambiguity and academic integrity risks. This study investigates how students utilize generative artificial intelligence tools in computer science-oriented disciplines and develops a structured, lightweight framework supporting institutional adaptation to pervasive GenAI usage. We conducted a case study at the University of Applied Sciences and Arts Hannover (Germany), combining document analysis with an online survey (N = 151) targeting Business Information Systems and E-Government students. Quantitative responses were analyzed statistically, while open-ended responses underwent thematic synthesis. Generative artificial intelligence adoption was widespread, with ChatGPT as the dominant tool. Students primarily used generative artificial intelligence for research assistance, programming support, and text processing. However, substantial policy uncertainty was observed: many students were unaware of or unsure about institutional generative artificial intelligence regulations. Document analysis revealed regulatory gaps, ambiguous terminology, and inconsistencies between formal rules and teaching practices. To address these shortcomings, we propose the AI Adoption Framework for Higher Education, an iterative and operational model integrating document analysis, empirical observation, synthesis of findings, and targeted updates of regulations and curricula. The framework addresses governance, assessment validity, and academic integrity under generative artificial intelligence conditions and provides practical guidance for institutional adaptation.