# The pop song generator: designing an online course to teach collaborative, creative AI

arXiv: https://arxiv.org/abs/2306.10069 · HTML: https://mlsanity.com/paper/2306.10069/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 43 references that are in the map.
- First posted on arXiv 15 Jun 2023.

## Builds on (most cited first, 2 of 2)

- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [Jukebox: A Generative Model for Music](https://mlsanity.com/paper/2005.00341/) (2020)

## Abstract (arXiv)

This article describes and evaluates a new online AI-creativity course. The course is based around three near-state-of-the-art AI models combined into a pop song generating system. A fine-tuned GPT-2 model writes lyrics, Music-VAE composes musical scores and instrumentation and Diffsinger synthesises a singing voice. We explain the decisions made in designing the course which is based on Piagetian, constructivist 'learning-by-doing'. We present details of the five-week course design with learning objectives, technical concepts, and creative and technical activities. We explain how we overcame technical challenges to build a complete pop song generator system, consisting of Python scripts, pre-trained models, and Javascript code that runs in a dockerised Linux container via a web-based IDE. A quantitative analysis of student activity provides evidence on engagement and a benchmark for future improvements. A qualitative analysis of a workshop with experts validated the overall course design, it suggested the need for a stronger creative brief and ethical and legal content.