UROP Project

AI-Mediated Learning: Exploring Language Learners’ Interaction with Artificial Intelligence and Its Impact on Language Development

AI, language learning, learner perception, self-directed learning, digital education

Research Mentor: Ms. Qian Yixin Qian , Yixin Qian
Department, College, Affiliation: Florida State University, Education, Health, and Human Sciences
Contact Email: yq23@fsu.edu
Research Assistant Supervisor (if different from mentor):
Research Assistant Supervisor Email:
Faculty Collaborators:
Faculty Collaborators Email:

Looking for Research Assistants: Yes
Number of Research Assistants: 1
Relevant Majors: Open to all majors
Project Location: On FSU Main Campus
Research Assistant Transportation Required: No, the project is remote
Remote or In-person: Partially Remote
Approximate Weekly Hours: 6-8 hours, Flexible schedule (Combination of business and outside of business. TBD between student and research mentor.)
Roundtable Times and Zoom Link:
Not participating in the roundtable

Project Description

With the rapid integration of generative AI tools into everyday learning, language learners are increasingly engaging with AI for various educational purposes. This project investigates how different types of interactions with general-purpose AI influence learners’ perceptions of their language mastery, their actual language learning practices, and their attitudes toward AI-supported versus traditional in-person instruction.

Drawing on an interaction typology—from information seeking and conversational practice to emotional support and editing—this study will categorize learners’ use of AI and explore the frequency and purposes behind these engagements. The research aims to uncover how learners assess their own language proficiency in relation to their AI use, how they perceive differences between self-directed AI-supported learning and classroom-based instruction, and what role AI plays in shaping their learning motivation, autonomy, and satisfaction.

This mixed-methods study will combine survey data, user interviews, and analysis of AI-based learning artifacts (e.g., prompts, outputs, revisions) to provide a comprehensive view of AI-assisted language learning. By capturing both quantitative patterns and qualitative insights, the project seeks to inform the design of more responsive AI-based learning environments and pedagogies that align with learners' real-world behaviors and needs.

Research Tasks: 1. Conduct a literature review
Investigate existing studies on the use of AI in language learning and self-directed learning, synthesizing themes, gaps, and methodological trends.

2. Co‑develop a learner survey
Design a survey instrument to capture AI interaction types, usage frequency, and learner perceptions—with mentor guidance.

3. Conduct semi-structured interviews
Facilitate interviews with language learners, alongside the mentor, to explore their experiences using AI tools in learning contexts.

4. Analyze AI-generated artifacts
Examine learner–AI interactions (e.g. prompts submitted, AI revisions, dialogue transcripts) to identify usage patterns and learning impact.

5. Code and thematically analyze interview data
Collaborate with the mentor to perform qualitative coding of interview transcripts, identifying emergent themes and insights.

6. Participate in quantitative survey analysis
Work under the mentor’s direction to analyze survey data, employing descriptive statistics and basic inferential tests as appropriate.

7. Compile findings into research outputs
Contribute to writing the research paper and preparing presentation materials—distilling insights, structuring results, and polishing language.

Skills that research assistant(s) may need: Proficiency in quantitative and qualitative data analysis (Required)

Familiarity with SPSS, R, and other statistical or data visualization tools (Required)

Experience with survey platforms (e.g., Qualtrics, Google Forms) (Recommended)

Strong academic writing, editing, and literature synthesis skills (Recommended)

Ability to transcribe and analyze interview data with attention to detail (Recommended)