AI-Augmented Research Workflows: Ethical Integration & Scholarly Integrity

About this course

What this course covers

The rapid integration of large language models and AI-assisted tools into academic research presents scholars with both productive efficiencies and unprecedented ethical obligations. This course provides a critical and practical framework for the responsible use of AI across the research lifecycle — from literature discovery and methodology design through manuscript drafting and peer-review preparation — while maintaining the integrity standards demanded by Scopus and ISI-indexed journals. Learning outcomes • Evaluate AI tools for literature discovery and synthesis (Elicit, Consensus, Semantic Scholar, Research Rabbit) • Apply AI assistance to drafting with documented transparency and attribution protocols compliant with publisher policies • Identify and mitigate AI risks — hallucinated citations, disciplinary bias, and loss of authorial voice • Comply with the AI authorship policies of major publishers (Springer Nature, Elsevier, Wiley, Taylor & Francis, IEEE) • Construct a personal or institutional AI-use policy aligned with COPE and ICMJE guidance Assessment & deliverables AI tool audit of three platforms with validation methodology; AI-assisted manuscript section with a transparency disclosure statement; reflective essay on AI ethics in disciplinary context (1,500 words).

What you’ll learn

Outcomes you can put to work

Curriculum

• 01

  Module 1 — The AI Landscape in Academic Research

  0 lessons

  A taxonomy of AI tools and the critical distinction between augmentation and delegation of scholarly judgment; publisher and institutional AI policies, COPE/ICMJE guidance, and misconduct thresholds.

• 02

  Module 2 — Practical AI Integration Across the Lifecycle

  0 lessons