The rapid advancement of generative artificial intelligence (AIGC) is profoundly reshaping the entire academic production chain, offering unprecedented efficiency gains while introducing significant ethical challenges. This article synthesizes insights from seven leading experts across diverse fields—including computer science, law, information science, and publishing— to explore the transformative impact of artificial intelligence (AI) on academic creativity from three dimensions: technological implementation, ethical and normative reconstruction, and future ecosystem development. From a technological perspective, AI is evolving from a mere tool to an active "collaborator." As Li Xiu (Tsinghua University) argues, based on Bloom's Taxonomy, AI efficiently handles lower-order cognitive tasks (e.g., information retrieval, terminology translation, and case enumeration), freeing researchers to focus on higher-order tasks such as analysis, evaluation, and creativity. This "cognitive co-evolution" between humans and AI represents a paradigm shift in academic work. Shen Xibin (Chinese Medical Journals Publishing House Co., Ltd.) further emphasizes AI's role as a "thinking partner" that expedites writing and enhances logical coherence, while Liu Li (Zhipu AI) notes its expanding utility across research ideation, literature review, data visualization, and manuscript polishing. However, these advancements bring critical ethical dilemmas. Wang Qian (East China University of Political Science and Law) demonstrates through empirical testing that AI can generate academically plausible yet ethically problematic content—including fabricated citations and data—raising questions about authorship accountability. The distinction between "AI-generated" and "AI-assisted" content, as defined by the World Intellectual Property Organization (WIPO), becomes crucial in determining academic integrity. Internationally, organizations such as COPE and ICMJE discourage attributing authorship to AI but advocate for transparent disclosure of its use. Yet, as Xu Lifang (Wuhan University) warns, the proliferation of AI-generated fraudulent papers in sensitive fields like health and environment undermines the evidence base of public knowledge. The governance of AI in academia requires a balanced approach. Xu proposes a "moderate regulation" model that avoids the pitfalls of outright prohibition (which stifles innovation) and privileged access (which exacerbate inequities). Instead, she advocates for dynamic, risk-based governance—e.g., pre-approval ethical reviews for high-risk fields like gene editing, sandbox testing for clinical applications, and traceable AI-use reporting in publishing. Zhang Xin (Society of China University Journals) adds that while over 24% of major global publishers have issued AI-use guidelines, detection technologies remain immature, with an average accuracy of only 50-60% in identifying AI-generated text. Looking ahead, AI is poised to further integrate into academic workflows, enabling personalized research assistance, cross-lingual "secondary creation, " and intelligent academic search engines. However, as Chu Jingli (Chinese Academy of Sciences) emphasizes, AI cannot replace human creativity—the core of academic research. The future ecosystem must be one of "human-AI symbiosis, " where humans remain ultimately responsible for steering research direction, ensuring ethical standards, and exercising critical judgment. In conclusion, this article calls for a collaborative effort among researchers, publishers, AI developers, and policymakers to build a responsible academic ecosystem—one that embraces AI's efficiency while safeguarding integrity through robust governance, transparency, and continuous human oversight.