UROP Research Mentor Project Submission Portal: Submission #1254
Submission information
              Submission Number: 1254
  Submission ID: 20866
  Submission UUID: 0b5ee086-c563-446c-892e-22cb0ec99638
      Submission URI: /urop-research-mentor-project-submission-portal
          Submission Update: /urop-research-mentor-project-submission-portal?token=6TGC9ZOktETp6TTfNzWrWchjwVizKH8Wwn_wyl5kNfE
      Created: Mon, 08/18/2025 - 03:11 PM
  Completed: Mon, 08/18/2025 - 03:32 PM
  Changed: Thu, 10/30/2025 - 10:49 AM
  Remote IP address: 172.59.65.219
  Submitted by: Anonymous
  Language: English
  Is draft: No
    Webform: UROP Project Proposal Portal
      Submitted to: UROP Research Mentor Project Submission Portal
    
          Research Mentor Information
      
  
  
  Zhiying Li
  
  
  
  
      
  
  
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  Graduate Student
  
  
  
  
      
  
  
  Phil Hiver
  
  
  
  
  
      
  
  
  Education, Health, and Human Sciences
  
  
  
  
      
  
  
  School of Teacher Education, Second Language Education Program
  
  
  
  
      
  
  
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          Additional Research Mentor(s)
      
  
  
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          Overall Project Details
      
  
  
  Tracking L2 Learners’ Engagement with AI-Generated Corrective Feedback and Learning Gains: A Multiple-Case Study
  
  
  
  
      
  
  
  AI-Generated Corrective Feedback; Engagement with Feedback; L2 Writing Instruction
  
  
  
  
      
  
  
  No
  
  
  
  
      
  
  
  1
  
  
  
  
      
  
  
  Open to all majors
  
  
  
  
      
  
  
  Virtual
  
  
  
  
      
  
  
  No, the project is remote
  
  
  
  
      
  
  
  Fully Remote
  
  
  
  
      
  
  
  5
  
  
  
  
      
  
  
  Flexible schedule (Combination of business and outside of business. TBD between student and research mentor.)
  
  
  
  
      
  
  
  The integration of generative AI (Gen-AI) tools into second language (L2) writing has transformed how L2 learners receive and process corrective feedback, but little is known so far about how learners’ engagement with AI-generated feedback shapes long-term knowledge development. This mixed-methods, multiple-case study will contribute to this gap. The data collection procedure will last for approximately five weeks: 5-10 university-level participants will complete weekly writing tasks online, submit drafts to a Gen-AI tool, learn from the generated feedback, and complete immediate and delayed repeated writing tasks. With the longitudinal data, this study intends to track (1) students’ cognitive and affective engagement with the Gen-AI feedback through surveys and interviews, (2) students’ learning gains gauged by their successful error correction in immediate post-writing (which is, immediate gains) and delayed post-writing (which is, sustained gains). Research findings will empower educators to leverage AI’s potential to facilitate L2 learners’ immediate and long-term learning growth.
  
  
  
  
      
  
  
  1.	Contribute to draft IRB documents (protocol, consent forms, recruitment materials) under the mentor’s guidance.
2. Schedule writing sessions for participants on synchronous video-communication platforms.
3. Collaborate with the mentor to collect data remotely, including monitoring and recording participants’ processes of pre-writing, feedback learning, and post-writing, distributing surveys, and conducting structured interviews using pre-approved protocols.
4. Collaborate with the mentor to analyze data.
5. Maintain organized datasets and keep them anonymized.
  2. Schedule writing sessions for participants on synchronous video-communication platforms.
3. Collaborate with the mentor to collect data remotely, including monitoring and recording participants’ processes of pre-writing, feedback learning, and post-writing, distributing surveys, and conducting structured interviews using pre-approved protocols.
4. Collaborate with the mentor to analyze data.
5. Maintain organized datasets and keep them anonymized.
      
  
  
  Interest in the research of AI in education (Required). 
Organizational skills for managing multiple participants/timelines (Required).
Communicative skills for giving clear instructions to participants (Required).
Background/coursework in Applied Linguistics, Education, Psychology, or TESOL (Recommended).
  Organizational skills for managing multiple participants/timelines (Required).
Communicative skills for giving clear instructions to participants (Required).
Background/coursework in Applied Linguistics, Education, Psychology, or TESOL (Recommended).
      
  
  
  I believe mentoring thrives through intentional structure and shared curiosity. In my mentoring, I will provide clear expectations and resources while empowering mentees to take ownership of tasks like data coding or interview design. Before each new responsibility, I will offer targeted training to cultivate mentees’ confidence and skills. I prioritize whole-person development, encouraging mentees to reflect on how their work connects to broader questions in the field (AI-mediated learning) and our communities. Specifically for this project, mentees will gain hands-on experience with mixed-methods L2 research and skills transferable to future work in language learning/teaching or educational technology. Above all, I foster a collaborative space where mistakes are part of the process and where curiosity drives growth, both for the project and researchers.
  
  
  
  
      
  
  
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UROP Program Elements
      
  
  
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  2025
  
  
  
  
      
  
  
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