The DOI matches its Crossref record.
Process not product in the written assessment
Abstract
This chapter describes established assessment designs, where students are led through the creation process starting with the identification of relevant content drawing from ideas used in systematic reviews ( Crowther, 2010 ). Search strategies and keywords are logged, and the validity of the content is critically appraised. AI is then built into the assessment design by incorporating prompts that were used and a justification of how the generated text has been used to inform the final written article. This AI-driven process is then assessed focusing on the justification of the resources used, and the critiquing of the content material whether books, articles, or AI-generated text.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Citations
Cited by
Sorted by citations
Generative AI in Higher Education: Balancing Innovation and Integrity
Francis, N. J., Jones, S., & Smith, D. P. (2025) · British Journal of Biomedical Science · doi:10.3389/bjbs.2024.14048 · Cited by 295
Self-citation
Metadata
- Authors
- Smith, D., & Francis, N.
- Venue
- Using Generative AI Effectively in Higher Education
- Publisher
- Routledge
- Published
- 2024-06-03
- Pages
- 115-126
- OpenAlex
- W4399293548
- Cited by
- 9
- References
- 0
- Citations by year
- 2026: 4 · 2025: 3 · 2024: 2
- FWCI
- 26.33
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Routledge eBooks (publishedVersion)
- Topics
- Social Sciences › Education › Student Assessment and Feedback
- Social Sciences › Education › Artificial Intelligence in Education
- Social Sciences › Library and Information Sciences › Academic Writing and Publishing
- Keywords
- AI-generated text, written assessment, assessment design, process-oriented assessment, systematic reviews, search strategies, critical appraisal, AI prompts, content validity, student writing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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