TEACHER/SUPERVISOR: Dr. Mohammadreza Farrokhnia
TOPIC
Receiving feedback does not necessarily mean that students understand it or know how to act on it. During revision, students need to interpret feedback, evaluate its relevance, explore possible responses, and decide how to revise their work: A process referred to as feedback sensemaking.
This internship is embedded in a recently funded SoTL project at UT that uses students’ interactions with GenAI as a window into these otherwise largely invisible sensemaking processes. The aim is not primarily to study GenAI, but to understand how students make sense of teacher feedback while revising academic writing.
ASSIGNMENT
You will work alongside another student researcher to analyse students’ GenAI chat logs and retrospective think-aloud interview transcripts collected while students revise academic writing in response to teacher feedback.
Your main task is to identify recurring feedback sensemaking practices, for example, interpreting feedback intent, clarifying meaning, evaluating feedback, seeking revision solutions, and deciding how to act. Coding will begin from a theory-informed framework based on established feedback-sensemaking procedures, while remaining open to additional practices that emerge from the data. Your tasks in general include:
- Pilot-code a subset of chat logs and interview transcripts;
- Refine the coding scheme and codebook together with the research team;
- Independently code a shared subset of data and examine coding consistency;
- Code the larger dataset once the codebook is stabilised;
- Compare chat-log behaviour with students’ retrospective explanations to triangulate enacted sensemaking and underlying reasoning.
The coded dataset will subsequently be used to examine which sensemaking practices and combinations of practices are associated with more productive feedback uptake and revision, and to inform pedagogical guidelines for supporting students’ feedback sensemaking.
WHAT YOU WILL LEARN
You will gain hands-on experience with qualitative coding, codebook development, intercoder agreement, analysis of human–AI interaction data, retrospective interview analysis, and research on feedback and academic writing. In addition, there may be an opportunity to contribute to a scientific article based on the project findings, which would further help you develop your academic writing skills.
KEY REFERENCES
Wichmann, A., Funk, A., & Rummel, N. (2018). Leveraging the potential of peer feedback in an academic writing activity through sense-making support. European Journal of Psychology of Education, 33(1), 165-184.
Wood, J. (2021). A dialogic technology-mediated model of feedback uptake and literacy. Assessment & Evaluation in Higher Education, 46(8), 1173-1190.
Kim, J., Lee, S. S., Detrick, R., Wang, J., & Li, N. (2026). Students-Generative AI interaction patterns and its impact on academic writing. Journal of Computing in Higher Education, 38(1), 504-525.