Research map / Large language models / AI and society
AI Toolkit: Libraries and Essays for Exploring the Technology and Ethics Behind AI
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Describes AITK, an open-source set of Python libraries and Jupyter computational essays for novices to explore AI mechanics and ethics, with pilots in humanities courses and a 17-participant usability study.
Problem
Existing AI resources either explain AI without interaction or teach building AI with substantial coding, leaving novices and humanities audiences without an accessible, interactive way to understand AI and its ethical implications.
Approach
Combine Python visualization libraries with pre-written Jupyter notebooks as computational essays so non-programmers can run, modify and visualize AI models (e.g., neural networks, word embeddings, image generation, robotics) alongside explanatory text on societal impacts.
Key results
- Usability study with 17 participants (6 novice, 6 intermediate, 5 experienced): at least 75% strongly agreed or agreed with every statement.
- 87% of participants agreed they understood a topic better after using AITK and benefited from it.
- 100% of participants agreed they would recommend AITK to others.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use AITK to review a topic | AITK users · AITK usability study | agreement that understanding improved: 87% | Helped |
| Use AITK | AITK users · AITK usability study | agreement that toolkit use was beneficial: 87% | Helped |
| Use AITK | AITK users · AITK usability study | agreement to recommend the toolkit: 100% | Helped |
Surprising findings
- Novices working alone struggled with navigating GitHub and with notebook errors caused by skipped cells, unlike students in guided pilot courses.
- Some CS students felt the labs were too easy and wanted more technical depth.
Limitations
- Usability evaluation relied on small participant groups and self-reported Likert surveys.
- Novices struggled to navigate the repository and recover from execution errors without guidance.
- Notebooks may be too light for CS students seeking more technical work.
Experimental setup
Datasets: 6x6 handwritten-style digit images created for the Basic Neural Networks notebook
Metrics: Likert agreement on usability statements proportion of participants agreeing
Builds on: Jupyter notebooks
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