Orofacial dysfunction in persons with congenital or childhood-onset neuromuscular disorders.

Generative artificial intelligence is profoundly changing the teaching and learning models of higher education. In recent years, large language models represented by ChatGPT have been rapidly popularized in universities around the world. Related studies have analyzed the AI ​​adoption policies and implementation guidelines of various universities from a global perspective and found that this technology has been widely used in diverse scenarios such as writing assistance, knowledge question answering, programming learning and academic research. However, the introduction of generative AI has also triggered discussions on academic integrity, teaching quality and student ability cultivation. Some studies have explored whether generative AI is a threat to academic integrity or an opportunity for educational reform from a multicultural perspective. Some scholars have further pointed out that this technology may constitute a paradigm shift in higher education and have a fundamental impact on traditional teaching concepts and assessment methods. Research has explored the impact of generative AI on students’ digital literacy from multiple perspectives. A systematic literature review has sorted out the research trends, core challenges and future directions in this field, laying the foundation for subsequent research. Policy research focuses on how universities formulate AI usage norms. Some scholars have constructed a comprehensive AI policy framework for university teaching. Other studies have revealed the differences in strategies for dealing with generative AI by analyzing university policy texts, resource allocation and usage guidelines. At the empirical level, existing work has mostly used questionnaire surveys or behavioral log analysis methods to systematically sort out the overall status of artificial intelligence applications in higher education and reveal the statistical correlation between AI tool use and learning effectiveness. Some studies have used ChatGPT as a typical case to deeply analyze the interactive behavior characteristics of students and chatbots and their potential educational value. While these studies provide valuable descriptive findings, correlation-based approaches cannot establish directional relationships or account for confounding factors. Understanding potential causal pathways remains essential for designing evidence-based educational interventions.