Two new papers submitted to arXiv explore the integration of AI into engineering education. The first paper, based on a survey of 100 students, highlights how students use large language models (LLMs) for tasks like writing support and coding, while also expressing concerns about accuracy, bias, and academic integrity. It advocates for a purpose-driven approach to AI integration, emphasizing critical literacy. The second paper reflects on a project-based course at the University of Bremen focused on engineering AI-enabled systems, noting persistent challenges in architectural decisions, deployment, and data management due to uneven expertise, but concluding that the course improved system-level reasoning. AI
IMPACT These papers highlight the dual nature of AI in education, showing its utility for learning while underscoring the need for critical engagement and careful system design.
RANK_REASON Two academic papers published on arXiv discussing AI in education.
- AI Algorithms: Theory and Engineering
- AI
- alphaXiv
- arXiv
- CatalyzeX
- DagsHub
- engineering education
- Gotit.pub
- Hugging Face
- large-language models
- ScienceCast
- University of Bremen
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