Priming at Scale
An Evaluation of Using AI to Generate Primes for Mobile Readers
Namrata Srivastava (Monash University, VanderBilt University)
Jennifer Healey (Adobe Research)
Rajiv Jain (Adobe Research)
Guanli Liu (University of Melbourne)
Ying Ma (University of Melbourne)
Borano Llana (University of Rhode Island)
Dragan Gasevic (Monash University)
Tilman Dingler (TU Delft - Industrial Design Engineering)
Shaun Wallace (University of Rhode Island)
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Abstract
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.