Analyzing AI-Generated Descriptions of Music History


Our understanding and teaching of music history can sometimes be narrowly defined by dominant narratives. This lesson not only highlights potential biases in AI-generated content but also sparks a broader conversation on making music education more inclusive, ensuring students appreciate the rich tapestry of global musical traditions.
Materials Needed Time needed 
Computers with internet access 30 mins
  • Students will be able to dissect and evaluate the AI-generated description for content accuracy, omissions, and biases.
  • Students will be able to recognize and discuss potential Eurocentric biases in AI outputs.
  • Students will be able to reflect on the implications of AI biases on music education and discuss ways to make music history teaching more diverse and inclusive.
Key Concepts & Vocabulary 

Artificial Intelligence: Computer systems able to perform tasks that normally require human intelligence.

Bias: Prejudice in favor of or against one thing, person, or group compared with another.

Eurocentrism: The inclination to interpret or evaluate other cultures in terms of European or, more broadly, Western values and perspectives.

  • Introduction (5 minutes)
    • Introduce the objective: understanding and critiquing an AI-generated description of a specific music history period.
    • Briefly discuss the prevalence of Eurocentric biases in music history.AI Description
  • Generation (3 minutes)
    • Select a period of music history (e.g., Baroque, Jazz Age, etc.).
    • Prompt ChatGPT to generate a description of the chosen period
    • Project the description for all students to read.
  • Class Dissection (15 minutes)
    • Engage in a class discussion, dissecting the content of the music history description. Guiding Questions might include:
      • How is this time period defined by ChatGPT in terms of inception, development, and style?
      • What people and locations does ChatGPT use to drive its narrative?
      • Are there clear or subtle Eurocentric biases present?
      • What information requires external verification?
  • Reflection and Way Forward (7 minutes)
    • Ask students to reflect on the potential biases in AI and the implications for music education.
    • Discuss strategies for a more diverse and inclusive approach to teaching music history, ensuring multiple perspectives are represented.
Supplemental activity ideas

AI vs. Textbook: Compare the AI-generated description to a standard music history textbook’s account. Discuss similarities, differences, and potential

Diverse Perspectives: Ask students to research non-European perspectives, influences, and contributions to the chosen period of music and present their findings.

AI in Modern Music: Discuss the role of AI in modern music creation and how it might influence or reflect cultural biases.