AI Toolkit for STEM Education

The following resource compiles a range of AI tools that are useful in the Department of Engineering. The aim of this toolkit is to have a collaborative space where colleagues can share best practices and state-of-the-art AI tools. Work in Progress.

Key Issues for AI

The presence of an icon represents “good” practice around the category. However, categories are better considered as a spectrum rather than binary classification.
General

LLMs

  • Copilot
  • Chat-GPT
  • Claude
  • Gemini
Research Assistants

Search Engines

  • Consensus
  • Perplexity

Analysis

  • Elicit
  • SciSpace

Collaboration

  • PaperBrain
Academic Writing

Writing

  • Paper Pal
  • Jenni
  • Sourcely
  • Heuristica
Multimedia

Image

  • Stable Diffusion
  • Adobe Firefly

Presentation

  • Mentimeter
  • Slidesgo

Voice

  • ElevenLabs

Video/Content Creation

  • Canva
  • Adobe Express
Admin Tasks

Timetabling

  • Timefold

Mail

  • Mailbutler

Meeting Assistants

  • Otter

Grading/Learning Platforms

  • Learnosity
Data Analytics

Analysis

  • Julius

Platform

  • databricks

Insights

  • Quivr
Engineering Software

Design

  • Fusion 360 Generative Design

FEA

  • Ansys AI
Since the rapid development of AI outpaces the implementation of effective regulations, these Guiding Principles have been proposed to inform the adoption of AI tools:
Know what AI is and its limitations
Use AI lawfully, ethically and responsibly
Use AI securely
Meaningful human controls at the right stage
Proper management of AI lifecycle
Use the right tool for the job
Open and collaborative
Commercial colleagues from start
Skills and expertise to implement AI
Right assurances in place
Developed by Raul Ochoa Cabrero