A Picture Is Worth a Thousand Words: Co-designing Text-to-Image Generation Learning Materials for K-12 with Educators

Authors

  • Safinah Ali Massachusetts Institute of Technology
  • Prerna Ravi Massachusetts Institute of Technology
  • Katherine Moore Massachusetts Institute of Technology
  • Hal Abelson Massachusetts Institute of Technology
  • Cynthia Breazeal Massachusetts Institute of Technology

DOI:

https://doi.org/10.1609/aaai.v38i21.30373

Keywords:

Generative AI, Text-to-image Generation, Creative ML, Teacher Education

Abstract

Text-to-image generation (TTIG) technologies are Artificial Intelligence (AI) algorithms that use natural language algorithms in combination with visual generative algorithms. TTIG tools have gained popularity in recent months, garnering interest from non-AI experts, including educators and K-12 students. While they have exciting creative potential when used by K-12 learners and educators for creative learning, they are also accompanied by serious ethical implications, such as data privacy, spreading misinformation, and algorithmic bias. Given the potential learning applications, social implications, and ethical concerns, we designed 6-hour learning materials to teach K-12 teachers from diverse subject expertise about the technical implementation, classroom applications, and ethical implications of TTIG algorithms. We piloted the learning materials titled “Demystify text-to-image generative tools for K-12 educators" with 30 teachers across two workshops with the goal of preparing them to teach about and use TTIG tools in their classrooms. We found that teachers demonstrated a technical, applied and ethical understanding of TTIG algorithms and successfully designed prototypes of teaching materials for their classrooms.

Published

2024-03-24

How to Cite

Ali, S., Ravi, P., Moore, K., Abelson, H., & Breazeal, C. (2024). A Picture Is Worth a Thousand Words: Co-designing Text-to-Image Generation Learning Materials for K-12 with Educators. Proceedings of the AAAI Conference on Artificial Intelligence, 38(21), 23260-23267. https://doi.org/10.1609/aaai.v38i21.30373