The rapid adoption of generative artificial intelligence (AI) tools in academic environments has raised critical questions around ethics, authorship, and research integrity. This study proposes a comprehensive framework to support the ethical and responsible integration of AI technologies into academic and scientific work. Drawing on international editorial guidelines, institutional policies, and relevant legislation such as the GDPR, the framework maps good practices across all stages of research, from idea generation and literature review to manuscript preparation, publication and dissemination. The methodology includes documentary analysis, comparison of AI platforms, and examination of best practices from universities and publishers. Findings highlight key risks such as algorithmic bias, data privacy issues, plagiarism, and over-reliance on automated outputs. The framework offers practical guidance on tool selection, transparency measures, training programs, and institutional policy development, also introducing original resources such as checklists and templates for responsible AI use declarations. Conclusions align with general consensus that while AI can enhance efficiency and support academic work, its use must be governed by rigorous ethical standards and human oversight. The proposed model serves as a flexible reference for students, educators, and researchers seeking to balance technological innovation with scientific integrity.