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AI Research Skills: Use AI Rigorously Across the Full Research Lifecycle

A complete framework for integrating AI tools into your research workflow — from literature discovery to writing and peer review — with integrity, attribution, and verifiability built in.

5 hoursTotal content
22 lessonsShort modules
287+Students enrolled
⭐ 4.8/5Average rating

$167

One-time payment · Lifetime access

  • 5 hours of structured video content — 22 lessons
  • AI Prompt Library: 60+ tested research prompts
  • Academic Integrity Checklist PDF
  • Tool Comparison Sheet: 12 AI research tools scored
  • Verification Protocol Worksheet
  • Lifetime access + updates as tools evolve
  • Access to private Discord community
  • 30-day money-back guarantee
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🔒 30-day money-back guarantee — no questions asked

What You'll Learn

Evaluate AI tool reliability for each research task
Run AI-assisted literature searches with Elicit and Semantic Scholar
Extract structured data from multiple papers using Elicit
Use Consensus for quick research question answering
Write AI-assisted sections that are attributable and verifiable
Detect and eliminate hallucinated citations before submission
Build prompts that produce structured academic summaries
Disclose AI use correctly across journal and institution policies
Set up a repeatable AI workflow across multiple projects
Maintain your scholarly voice when using AI writing assistance

Course Curriculum

1

The Current State of AI in Research

What AI can and cannot do reliably in 2026. Calibrating expectations honestly.

2

A Framework for Rigorous AI Use

The 4-step verification framework — generate, verify, attribute, disclose.

3

The Research AI Toolkit

Overview and hands-on test of Elicit, Semantic Scholar, Consensus, Perplexity, Connected Papers, and Research Rabbit.

4

Literature Discovery with AI

Using Research Rabbit and Connected Papers to map a citation network from a single seed paper.

5

Structured Literature Extraction with Elicit

Extracting methodology, findings, and limitations from 20+ papers simultaneously.

6

Research Question Answering with Consensus

Getting evidence-backed answers with source attribution — and knowing when to distrust them.

7

Detecting Hallucinations

The 5 patterns AI uses when hallucinating references and how to catch each one.

8

Prompt Engineering for Academic Work

The prompt structures that produce structured, citable, well-attributed output.

9

AI-Assisted Literature Review Writing

From summaries to a coherent literature review section — with every source verified.

10

AI for Editing and Restructuring

Using AI to improve clarity and structure without losing your argument or voice.

11

AI-Assisted Data Analysis Narration

Describing results and generating discussion sections using AI — with guardrails.

12

Academic Integrity and Disclosure

How to disclose AI use correctly for COPE-compliant journals, institutional policies, and funding bodies.

13

Building Your Personal AI Workflow

Combining tools into a repeatable process adapted to your discipline and project type.

Why This Course Exists

There are two camps in academic AI discourse: those who say AI will destroy research, and those who use it uncritically and create real integrity problems. This course charts the middle path.

The framework here is practical, discipline-agnostic, and designed to remain valid as tools continue to evolve. You’ll learn how to evaluate new tools, not just how to use the ones popular today.

Prerequisites

  • No prior AI tool experience required
  • No programming knowledge required
  • You should be actively doing research (the exercises use real papers)

Discipline Coverage

Examples and case studies cover social sciences, STEM, humanities, and health sciences. The framework applies to all.