[3] The Semantic Scholar team is actively researching the use of artificial intelligence in natural language processing, machine learning, human–computer interaction, and information retrieval.

One of its aims was to address the challenge of reading numerous titles and lengthy abstracts on mobile devices.

[7] It also seeks to ensure that the three million scientific papers published yearly reach readers, since it is estimated that only half of this literature is ever read.

[12] Semantic Reader provides in-line citation cards that allow users to see citations with TLDR (short for Too Long, Didn't Read) automatically generated short summaries as they read and skimming highlights that capture key points of a paper so users can digest faster.

[14] Like the previously cited search engines, Semantic Scholar also exploits graph structures, which include the Microsoft Academic Knowledge Graph, Springer Nature's SciGraph, and the Semantic Scholar Corpus (originally a 45 million papers corpus in computer science, neuroscience and biomedicine).

[18] In March 2018, Doug Raymond, who developed machine learning initiatives for the Amazon Alexa platform, was hired to lead the Semantic Scholar project.

[19] As of August 2019[update], the number of included papers metadata (not the actual PDFs) had grown to more than 173 million[20] after the addition of the Microsoft Academic Graph records.