Publications

Priming at Scale: An Evaluation of Using AI to Generate Primes for Mobile Readers

Conference on Human Factors in Computing Systems (CHI 2025) (LBW)

Publication date: April 18, 2025

Namrata Srivastava, Jennifer Healey, Rajiv Jain, Guanli Liu, Ying Ma, Borano Llana, Dragan Gasevic, Tilman Dingler, Shaun Wallace

Text summaries, images, and mind maps are well-known methods for priming readers to better engage with content. Previously, these "primes" needed to be hand-crafted, limiting their use. The advent of generative technologies makes the automatic creation of custom primes for any passage a realistic possibility. Here, we evaluate the efficacy of primes generated using AI on reading comprehension, reading speed, and re-engagement during mobile reading, which is notorious for its frequent interruptions. We used a mobile platform to present a reading task with an interruption to 44 readers (21 with English as a first language). We found that AI primes increased reading speed by an average of 7% for all readers in the initial reading task with no loss of comprehension and that visual primes had a significant interruption recovery effect for people whose first language was not English. Across all readers, text primes had both the initial reading speed increase and were overall most preferred.

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