Prompt: Verify and Compile 2026 University NLP Courses

You are an expert academic research assistant.

Objective

Compile a comprehensive directory of 50–100 university Natural Language Processing (NLP) courses for 2026.

The output must contain only verified, working URLs from official university websites.

Requirements

For every course, verify all of the following before including it:

1. The URL is accessible (not 404 or broken).
2. The page is hosted on an official university domain (e.g., .edu, .ac.uk, .edu.au, .edu.sg, etc.).
3. The page corresponds to an NLP-related course.
4. The course page is publicly accessible (no login required).
5. The course is either:

• offered in 2026,
• offered in 2025–2026,
• or an official archived course with publicly available materials.

Do not include unofficial mirrors, GitHub copies (unless they are the official course website), Canvas, Moodle, Blackboard, or commercial reposts.

Include

• Natural Language Processing
• Statistical NLP
• Computational Linguistics
• Deep Learning for NLP
• Neural NLP
• Language Technologies
• Large Language Models (LLMs)
• Information Extraction
• Text Mining
• Text Analytics
• Machine Translation
• Speech and Language Processing
• Dialogue Systems
• Question Answering
• Semantic Parsing
• Knowledge-based NLP

Exclude

• MOOCs (Coursera, edX, Udemy, etc.)
• Commercial training courses
• Tutorials
• Workshops
• Summer schools (unless university courses)
• Broken URLs
• Login-only pages

Search Scope

Search only official university websites.

Prioritize institutions such as:

• Stanford
• Carnegie Mellon
• MIT
• Harvard
• UC Berkeley
• Cornell
• Columbia
• Princeton
• Yale
• Brown
• University of Pennsylvania
• Johns Hopkins
• University of Washington
• UIUC
• UMass Amherst
• Georgia Tech
• University of Maryland
• UT Austin
• Ohio State
• University of Michigan
• University of Toronto
• McGill
• Waterloo
• ETH Zürich
• EPFL
• University of Cambridge
• University of Oxford
• University of Edinburgh
• Imperial College London
• UCL
• King's College London
• Saarland University
• University of Stuttgart
• University of Tübingen
• LMU Munich
• TU Munich
• University of Amsterdam
• KU Leuven
• University of Helsinki
• University of Copenhagen
• Sapienza University of Rome
• University of Bologna
• University of Barcelona
• IIT Delhi
• IIT Bombay
• IIT Madras
• IIT Kanpur
• IIT Kharagpur
• IIT Guwahati
• IISc Bangalore
• IIIT Hyderabad
• IIIT Delhi
• NUS
• NTU
• HKUST
• Tsinghua University
• Peking University
• KAIST
• Seoul National University
• ANU
• University of Melbourne
• UNSW
• Monash University

Continue searching beyond these universities until at least 50–100 verified courses have been collected.

Verification Procedure

For each university:

1. Locate the official course page.
2. Verify the page loads successfully.
3. Confirm the URL is official.
4. Verify that the course title matches the page.
5. Record the academic year.
6. Verify the instructor(s), if available.
7. Verify the department.

Only after all checks pass should the course be included.

Output Format

Produce a Markdown table with the following columns:

Where:

• Official URL = verified working URL
• URL Status = Working

Quality Requirements

• Minimum: 50 courses
• Preferred: 75–100 courses
• Every URL must be individually verified.
• No duplicate courses.
• No duplicate URLs.
• No broken links.
• No unofficial websites.

Final Validation

Before returning the results:

• Confirm every URL is working.
• Remove all broken links.
• Remove redirects to missing pages.
• Remove login-only pages.
• Remove unofficial mirrors.

Finally report:

• Total universities covered
• Total verified courses
• Total countries represented
• Number of URLs tested
• Number of URLs rejected
• Number of URLs accepted

Return only the final verified Markdown table followed by the validation summary.

This prompt is designed to maximize the likelihood of obtaining a high-quality, verifiable dataset from Claude or another research-capable LLM.