Context-aware GenAI feedback for fostering student feedback literacy and informing revision decisions in social science writing
Open accessAbstract
Generative AI (GenAI) provides immediate feedback but often lacks the contextual grounding for discipline-specific writing such as social science writing. This decontextualization leads to low cognitive engagement and surface-level revisions. This study investigated how a context-aware GenAI, integrated with Bitzer’s rhetorical situation theory, influences student feedback literacy (SFL) and revision decisions. 84 undergraduate students were assigned to either a standard GenAI group (n = 42) using rubric-based feedback, or a context-aware GenAI group (n = 42) using feedback grounded in rhetorical situation theory. GenAI-student interaction logs were analysed using Epistemic Network Analysis to examine structural patterns of SFL. Revision decisions were analysed using thematic analysis and ANOVA. The two groups revealed different structural patterns of SFL. The context-aware GenAI group demonstrated stronger cognitive-behavioural connections, enabling students to evaluate feedback for rhetorical relevance. In contrast, the standard GenAI group showed stronger affective-behavioural connections, where emotional burdens led to behavioural compliance and surface-level revisions. The ANOVA results showed that the context-aware group made significantly more rhetorical-level revisions, while the control group focused on surface-level corrections. This study contributes to the reconceptualization of SFL by revealing the interfering role of affective burdens in the feedback process.
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LLMs may produce feedback that lacks specificity, contains hallucinated information, or fails to account for the broader context and goals of a student's writing.
“LLMs may produce feedback that lacks specificity, contains hallucinated information, or fails to account for the broader context and goals of a student's writing (Davis & Mahmoud, 2026;Tseng, 2026;Jia et al., 2024).”
From Think Before You Accept: Can Written Justification Reduce Uncritical Uptake of AI Writing Suggestions? (2026) · cites this paper as Tseng, 2026
Defaulting to AI suggested revisions may constrain students' rhetorical agency by giving up control over what to say and how to say it.
“Defaulting to AI suggested revisions may therefore constrain students' rhetorical agency by giving up control over what to say and how to say it (Tseng, 2026).”
From Think Before You Accept: Can Written Justification Reduce Uncritical Uptake of AI Writing Suggestions? (2026) · cites this paper as Tseng, 2026
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Sorted by citations
Tao, Y., Meyer, J., & Kizilcec, R. F. (2026) · arXiv · doi:10.48550/arxiv.2609.23936
- Authors
- Tseng, S.-S.
- Affiliations
- National Chengchi University (TW)
- Corresponding author
- Tseng, Sheng-Shiang
- ORCID
- Tseng, Sheng-Shiang
- Venue
- Assessment & Evaluation in Higher Education
- Publisher
- Informa UK Limited
- Published
- 2026-04-17
- Pages
- 1-20
- ISSN
- 0260-2938 (ISSN-L)
- 1469-297X
- OpenAlex
- W7154836483
- Cited by
- 2
- References
- 8
- Citations by year
- 2026: 2
- FWCI
- 15.01
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Assessment & Evaluation in Higher Education (publishedVersion, cc-by)
- Funders
- National Science and Technology Council (NSTC 113-2423-H-032-001-MY4)
- Topics
- Social Sciences › Education › Student Assessment and Feedback
- Social Sciences › Arts and Humanities › Literature and Literary Theory › Discourse Analysis in Language Studies
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Keywords
- AI-generated feedback, context-aware feedback, social science writing, epistemic network analysis, cognitive engagement, AI-assisted writing
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