Research Interests:
Daniel is a Ph.D. student in Applied Linguistics at Penn State whose work explores how human–AI partnerships can make language assessment more equitable for multilingual learners. Drawing on fifteen years of teaching experience in Colombia and on technology-focused curriculum design, he investigates AI-mediated dynamic assessment, especially automatic-speech-recognition and large-language-model feedback to help learners negotiate meaning, refine pronunciation, and develop academic writing. Daniel has led augmented-reality and AI-literacy initiatives that empower students to engage critically with emerging technologies. As a LinDiv trainee, he aims to design assessment interfaces that honor linguistic diversity across the lifespan and to collaborate with engineers, psychologists, and other disciplines to ensure that the next generation of language-technology tools amplifies, rather than flattens, human voices.
