The landscape of education is changing rapidly, shaped by emerging pedagogical approaches, technological innovations such as artificial intelligence (AI), and evolving societal expectations, all of which demand thorough evaluation of new educational tools. Although large language models (LLMs) present substantial opportunities especially in Higher Education, their propensity to generate hallucinations and their limited specialized knowledge may introduce significant risks. This study aims to address these risks by examining the practical implementation of an LLM-enhanced assistant in a university level course. We implemented a generative AI assistant grounded in a retrieval-augmented generation (RAG) model to replicate a previously teacher-led, time-intensive exercise. To assess the effectiveness of the LLM, we conducted three separate experiments through iterative mixed-methods approaches, including a crossover design. The resulting data address central research questions related to student motivation, perceived differences between engaging with the LLM versus a human teacher, the quality of AI-generated responses, and the impact of the LLM on students' academic performance. The results offer direct insights into students' views and the pedagogical feasibility of embedding LLMs into specialized courses. Finally, we discuss the main challenges, opportunities and future directions of LLMs in teaching and learning in Higher Education.

翻译：教育格局正在快速演变，受到新兴教学法、人工智能等技术创新以及不断变化的社会期望的驱动，这要求对新教育工具进行全面评估。尽管大型语言模型尤其为高等教育带来了重大机遇，但其产生幻觉的倾向以及有限的专业知识可能引入显著风险。本研究旨在通过考察大型语言模型增强型辅助工具在大学课程中的实际实施来应对这些风险。我们基于检索增强生成模型实现了一个生成式AI辅助工具，以复现之前由教师主导、耗时长的练习。为评估大型语言模型的效果，我们通过迭代混合方法（包括交叉设计）进行了三项独立实验。所得数据解答了核心研究问题，包括学生动机、与人类教师互动相比与大型语言模型互动的感知差异、AI生成回复的质量，以及大型语言模型对学生学业表现的影响。结果直接揭示了学生的观点以及将大型语言模型嵌入专业课程的教学可行性。最后，我们讨论了大型语言模型在高等教育教学中面临的主要挑战、机遇及未来方向。