Title: AI Assisted Academic Research

Location: US

Company: Microsoft

About this course:
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.

Define core AI concepts for researchers

Explain how large language models work

Describe embeddings and semantic similarity

Explain retrieval-augmented generation for scholarship

Map AI across research workflow stages

Distinguish appropriate versus inappropriate AI uses

Refine research questions with AI support

Generate keywords and Boolean search strings

Combine AI search with scholarly databases

Verify AI-suggested citations in databases

Summarize papers using structured AI prompts

Extract methods, findings, and limitations accurately

Synthesize themes across multiple sources

Organize literature using AI-augmented tools

Draft and revise text while preserving voice

Fact-check AI-assisted writing against sources

Detect hallucinations, bias, and incompleteness

Build a personal AI validation checklist

Apply citation and AI-use disclosure practices

Follow ethical, legal, and privacy norms

In this course , students will learn to use AI tools and LLMs to explore topics, search and synthesize literature, draft and revise academic writing, and evaluate AI output—while maintaining integrity, proper citation, and ethical disclosure.

Duration: 1 week

Skills:
- Academic Writing
- Artificial Intelligence
- Ethical Standards And Conduct
- Large Language Modeling
- Research
- Workflow Management
- Writing

Curriculum:
- Define core AI concepts for researchers
- Explain how large language models work
- Describe embeddings and semantic similarity
- Explain retrieval-augmented generation for scholarship
- Map AI across research workflow stages
- Distinguish appropriate versus inappropriate AI uses
- Refine research questions with AI support
- Generate keywords and Boolean search strings
- Combine AI search with scholarly databases
- Verify AI-suggested citations in databases
- Summarize papers using structured AI prompts
- Extract methods, findings, and limitations accurately
- Synthesize themes across multiple sources
- Organize literature using AI-augmented tools
- Draft and revise text while preserving voice
- Fact-check AI-assisted writing against sources
- Detect hallucinations, bias, and incompleteness
- Build a personal AI validation checklist
- Apply citation and AI-use disclosure practices

- Follow ethical, legal, and privacy norms