Assessing the feasibility and acceptability of Lettersmith for assisting global health researchers in academic writing

INTRODUCTION: Health researchers in low-resource settings often face barriers to publication due to limited access to training in academic writing. While generative artificial intelligence (genAI) tools are increasingly available, concerns about data privacy, accuracy, and user engagement may limit its use. Lettersmith is a free educational technology that helps writers learn to craft different types of writing, including academic writing. We tested the feasibility and acceptability of Lettersmith for improving manuscript abstracts among a small sample of African researchers who spoke English as their second language. METHODS: Researchers attended a virtual two-hour pilot workshop during which they set up the software, practiced using it to improve their draft abstracts, and completed an evaluation survey. We asked participants to evaluate the software with surveys. RESULTS: Nineteen researchers participated and 16 completed the evaluation. All found Lettersmith to be helpful for abstract revision, with 69% reporting it as extremely helpful and 31% as somewhat helpful. Getting oriented to the software was their biggest challenge. Nearly all participants (88%) said they would recommend Lettersmith to a colleague with great enthusiasm. CONCLUSION: Lettersmith shows promise as a feasible and acceptable tool for supporting academic writing skill development among early-stage researchers in resource-limited settings.