Hui Wang

Hui Wang, Anh Dang, Zihao Wu et al.

The advancements in Generative Artificial Intelligence (GenAI) provide opportunities to enrich educational experiences, but also raise concerns about academic integrity. Many educators have expressed anxiety and hesitation in integrating GenAI in their teaching practices, and are in needs of recommendations and guidance from their institutions that can support them to incorporate GenAI in their classrooms effectively. In order to respond to higher educators' needs, this study aims to explore how universities and educators respond and adapt to the development of GenAI in their academic contexts by analyzing academic policies and guidelines established by top-ranked U.S. universities regarding the use of GenAI, especially ChatGPT. Data sources include academic policies, statements, guidelines, and relevant resources provided by the top 100 universities in the U.S. Results show that the majority of these universities adopt an open but cautious approach towards GenAI. Primary concerns lie in ethical usage, accuracy, and data privacy. Most universities actively respond and provide diverse types of resources, such as syllabus templates, workshops, shared articles, and one-on-one consultations focusing on a range of topics: general technical introduction, ethical concerns, pedagogical applications, preventive strategies, data privacy, limitations, and detective tools. The findings provide four practical pedagogical implications for educators in teaching practices: accept its presence, align its use with learning objectives, evolve curriculum to prevent misuse, and adopt multifaceted evaluation strategies rather than relying on AI detectors. Two recommendations are suggested for educators in policy making: establish discipline-specific policies and guidelines, and manage sensitive information carefully.

Yangning Li, Tingwei Lu, Yinghui Li et al.

Efficient instruction tuning aims to enhance the ultimate performance of large language models (LLMs) trained on a given instruction dataset. Curriculum learning as a typical data organization strategy has shown preliminary effectiveness in instruction tuning. However, current curriculum tuning methods suffer from the curriculum rigidity, since they rely solely on static heuristic difficulty metrics. These methods fail to adapt to the evolving capabilities of models during training, resulting in a fixed and potentially sub-optimal learning trajectory. To address the issue, Competence-Aware Multi-Perspective cUrriculum inStruction tuning framework termed CAMPUS is proposed. CAMPUS offers several advantages: (1) Dynamic selection for sub-curriculum. (2) Competency-aware adjustment to the curriculum schedule. (3) Multiple difficulty-based scheduling. Extensive experiments prove the superior performance of CAMPUS, compared to other state-of-the-art baselines for efficient instruction tuning.