  Textbooks are one of the main mediums for delivering high-quality education
to students. In particular, explanatory and illustrative visuals play a key
role in retention, comprehension and general transfer of knowledge. However,
many textbooks lack these interesting visuals to support student learning. In
this paper, we investigate the effectiveness of vision-language models to
automatically enhance textbooks with images from the web. We collect a dataset
of e-textbooks in the math, science, social science and business domains. We
then set up a text-image matching task that involves retrieving and
appropriately assigning web images to textbooks, which we frame as a matching
optimization problem. Through a crowd-sourced evaluation, we verify that (1)
while the original textbook images are rated higher, automatically assigned
ones are not far behind, and (2) the precise formulation of the optimization
problem matters. We release the dataset of textbooks with an associated image
bank to inspire further research in this intersectional area of computer vision
and NLP for education.
