Skip to main content

Dissertation Awards

Awarding research at the intersection of language and technology

Mango Languages invites advanced Ph.D. candidates to submit applications for the Mango Dissertation Awards. This program supports exceptional dissertation research at the intersection of second language acquisition and educational technology.

A woman in a green sweater working at a laptop at a tidy home desk with a coffee cup.

Who can apply

Submission details

Applicants must be ABD by the application deadline, meaning all coursework and comprehensive exams are complete and the dissertation proposal has been accepted.

Applications should be submitted online. Submissions for 2026 are closed.

2026

Application dates

Call opens

April 15, 2026

Proposal deadline

June 15, 2026

Applicants notified

September 1, 2026

Applying

What to send us

  1. 1

    Basic contact information

    So we can reach you about the outcome.

  2. 2

    A research summary

    • Summary of the background literature that contextualizes the dissertation research

    • Clearly stated research questions

    • Description of the methodology employed

    • Expected results and potential implications for second language acquisition, educational technology, and related fields

    • Maximum of 2 pages, single-spaced, including references. Up to 2 additional pages of figures and tables may be included

  3. 3

    Your CV

  4. 4

    Your research supervisor's contact information

    We may contact them to complete a brief survey regarding your advanced standing and likelihood of completion.

Recipients

Meet the award winners

We've recognized 13 graduate students for their stand-out research at the intersection of second language learning and educational technology. Check out the winners from past years below.

2026 recipients

  • Matt Malone

    Linguistics

    CUNY Graduate Center

    “Beyond Indo-European: New Benchmarks and Synthetic Data Methods for Grammatical Error Correction in Turkish and Vietnamese”

    Matt's dissertation research focuses on developing better tools for correcting grammatical errors in low-resource languages. While English has many sophisticated writing and grammar tools, languages like Vietnamese and Turkish have far fewer resources available. This project explores how we can build useful grammar-correction technology for these languages without relying on huge collections of writing that have been manually corrected by experts. Specifically, we use computer-generated examples of grammatical mistakes to train language models to identify and correct errors. We also investigate ways of determining which examples are most useful for training these models. By developing more effective methods for working with limited data, this research aims to make grammar technology more accessible to speakers and learners of Vietnamese and Turkish.

    This dissertation work is being completed in collaboration with CUNY Graduate Center Computational Linguistics student, Zilola Samigjonova.

  • Soyeon Sim

    Applied Linguistics & ESL

    Georgia State University

    “Human and AI interlocutors in L2 spoken interaction: A corpus-based study of phraseological and interactional competence”

    Although voice-based AI chatbots are increasingly used for language practice, little is known about whether learners communicate with them in the same ways they communicate with people. Soyeon's research examines how the person, or technology, we speak with shapes the way we use a second language. Around 200 Korean university students learning English will complete short decision-making tasks with three partners: another English learner, a person whose first language is English, and a voice-based AI chatbot. Their recorded conversations will form a spoken-language dataset. Soyeon will compare the recurring word and sentence patterns learners use in each setting, as well as how they introduce and develop ideas and show that they are listening and understanding. By identifying similarities and differences across AI and human conversations, this dissertation aims to help teachers and researchers design speaking tasks that provide meaningful opportunities for learners to practice communicating in a second language.

  • Annika Wallander

    Spanish Linguistics & Second Language Acquisition

    University of Wisconsin-Madison

    “How L2s VOCAL-ize: Understanding progress and goals through a Spanish vowel pronunciation intervention”

    First-language (L1) English speakers often retain their speech patterns when learning Spanish as a second language (L2), which can pose challenges for learning accurate pronunciation. Target-like pronunciation of Spanish vowels is especially important for distinguishing words and communicating clearly, but educational technologies specifically designed to support the learning and practice of Spanish vowel sounds are limited. Annika's research focuses on VOCAL-ize, a pronunciation tool that was created to train Spanish vowels by giving learners visual feedback on their pronunciations and comparing them with those of a Spanish speaker. Through weekly pronunciation training over the course of a semester with beginner-level university Spanish learners, she is studying how different pronunciation feedback approaches affect Spanish vowel accuracy and communicative effectiveness. Learners also complete weekly reflections and surveys about their experiences, motivation, and pronunciation progress. Through these findings, Annika aims to understand the effectiveness and overall experience of visual feedback in Spanish vowel learning. She also hopes to contribute a novel, engaging tool to support vowel pronunciation and enable learners to strengthen their communication skills in Spanish.

Be first to know when the next call opens

Submissions for the 2026 cycle are closed. Email research@mangolanguages.com and we'll tell you when the next one opens, or work with us in the meantime.