Evaluating Artificial Intelligence, Peer, and Instructor Feedback Across Scientific Communication Assignments in Undergraduate Biology
Open accessAbstract
Generative artificial intelligence (AI) has emerged as a potential source of formative feedback for student writing. However, relatively little is known about how AI‐generated feedback compares with instructor and peer feedback across different Writing‐to‐Learn (WTL) assignments implemented in authentic undergraduate biology classrooms. This exploratory study compared AI (specifically ChatGPT‐5), peer, and instructor feedback across infographic and academic explanatory article assignments implemented in three undergraduate biology courses representing diverse student populations. Agreement among feedback sources was evaluated using intraclass correlation coefficients (ICC), while student perceptions of feedback usefulness, trust, engagement, and effort were assessed using Likert‐scale survey data. Agreement between AI and instructor feedback varied according to assignment modality, disciplinary context, and revision stage. AI demonstrated moderate agreement with instructor feedback during the draft stage, but agreement frequently diverged following student revision, particularly for higher‐order rubric criteria such as content accuracy and disciplinary reasoning. Agreement was consistently stronger for academic explanatory articles than for infographics. Students perceived instructor feedback as more useful and trustworthy than either peer or AI feedback, whereas the effort required to apply feedback did not differ across feedback sources. AI feedback was generally perceived similarly to peer feedback. Collectively, these findings suggest that AI is best used as a formative, early‐stage scaffold within WTL assignments rather than as a summative evaluation tool. When integrated into structured instructional workflows alongside instructor and peer feedback, large language models can support student revision while preserving the central role of instructor expertise in undergraduate biology education.
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Zhai and Nehm proposed that AI is already extensively utilized in formative assessment across diverse educational settings, so the focus should shift to exploring how to effectively integrate AI to enhance formative assessment practices.
“Zhai and Nehm [50] proposed that AI is already extensively utilized in formative assessment across diverse educational settings, so that the focus should shift from questioning whether AI can or should be used to exploring how to effectively integrate AI to enhance formative assessment practices.”
From Comparing Student Preferences for AI-Generated and Peer-Generated Feedback in AI-driven Formative Peer Assessment (2025) · cites this paper as [50]
The Discussion’s “Pedagogical Implications for AI-Assisted Feedback in STEM Courses” section argues for asking when and how AI feedback can support learning, which supports the proposed shift toward effective integration. But the paper does not state that AI is already extensively used for formative assessment across diverse educational settings, nor does it attribute that proposal to Zhai and Nehm. (LLM, conf=0.85)
Experimental — accuracy not yet measuredAI can evaluate students' responses and provide feedback based on the evaluation in formative assessment.
“In formative assessment, AI can evaluate students’ responses and provide feedback based on the evaluation [15, 18, 43, 50].”
From Comparing Student Preferences for AI-Generated and Peer-Generated Feedback in AI-driven Formative Peer Assessment (2025) · cites this paper as [50]
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Comparing Generative AI and teacher feedback: student perceptions of usefulness and trustworthiness
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Assessing student perceptions and use of instructor versus <scp>AI</scp> ‐generated feedback
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Sorted by citations
Shin, I., Hwang, S. B., Yoo, Y. J., Bae, S., & Kim, R. Y. (2025) · Proceedings of the 15th International Learning Analytics and Knowledge Conference · doi:10.1145/3706468.3706488 · Cited by 16 · see citing works
- Authors
- Boies, L.
- Affiliations
- St. Mary's University, Texas (US)
- Corresponding author
- Boies, Lori
- ORCID
- Boies, Lori
- Venue
- Ecology and Evolution
- Publisher
- Wiley
- Published
- 2026-09
- Volume
- 16
- Issue
- 9
- Pages
- e74365-e74365
- ISSN
- 2045-7758 (ISSN-L)
- PMC
- PMC13590280
- OpenAlex
- W7213773395
- Cited by
- 0
- References
- 5
- FWCI
- 0.00
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Ecology and Evolution (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- St Mary's University
- Topics
- Social Sciences › Education › Student Assessment and Feedback
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Education › Science Education and Pedagogy
- Keywords
- generative AI, ChatGPT-5, peer feedback, instructor feedback, formative feedback, writing to learn, student perceptions of feedback, student revision, large language models, undergraduate biology education
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