MS-AI-145 | AI Assisted Academic Research equips students and researchers to use AI tools effectively and responsibly across the academic research workflow. With a practical focus on large language models (LLMs) and AI‑augmented scholarly tools, the course shows how AI can support topic exploration, research question refinement, literature searching, reading and summarizing, synthesis across sources, note and reference organization, and academic writing—without compromising integrity, originality, or disciplinary standards.
Through six hands-on modules, learners practice selecting appropriate tools for each research stage, writing better prompts, and building verification habits that prevent common failures such as hallucinated facts, fabricated citations, and shallow or biased summaries. Activities emphasize checking AI output against primary sources and traditional scholarly databases, triangulating results, and maintaining clear documentation of what was AI-assisted and what was authored by the researcher.
By the end of the course, learners can explain key AI concepts relevant to research (e.g., embeddings and retrieval‑augmented generation), integrate AI into their existing workflow, critically evaluate AI outputs for accuracy, relevance, completeness, and bias, and apply ethical, legal, and institutional norms—including transparent disclosure and citation/acknowledgment practices. Graded module quizzes and a final assessment are complemented by applied, self‑graded assignments that build a portfolio of responsible AI‑supported research practices.
