Actionable companion · Assessment and evidence of learning

From evidence to district action: Evaluating Artificial Intelligence, Peer, and Instructor Feedback Across Scientific Communication Assignments in Undergraduate Biology

AI-assisted editorial implementation guide. The district actions are proposed applications, not findings or recommendations attributed to the original authors. No additional peer review is asserted.

Evidence and context

Confirm the study population, setting and task before applying its findings to your district.

Original authors’ abstract, reproduced under the linked source license:

ABSTRACT 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. Keywords: artificial intelligence feedback, formative feedback, science communication, undergraduate biology education, writing‐to‐learn

A 90-day district pilot

1. Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
2. Days 16–30 · Design. Redesign one low-stakes assessment: require a first attempt, an AI-assisted revision, a source-check log and a short oral explanation. Use the same rubric for the comparison task. Name an accountable owner and set the baseline, success threshold and budget before implementation.
3. Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
4. Days 61–90 · Decide. Blind-score reasoning, factual accuracy and unaided transfer on a fresh task. Record suspected misconduct separately from demonstrated evidence. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Lori Boies. Evaluating Artificial Intelligence, Peer, and Instructor Feedback Across Scientific Communication Assignments in Undergraduate Biology. Ecology and Evolution (2026-09-20). https://doi.org/10.1002/ece3.74365

Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.