Knowledge Resource
Research Summary: An NLP-assisted and explainable rule-based platform for formative feedback from academic similarity reports
- Original authors
- Attribution requires verification
- Original source
- Frontiers in Education
- Summary & Analysis prepared by
- Aziz Shuaib Ausi
- Resource type
- Research Summary / Knowledge Resource
- Resource published on AZIZ OS
- 3 October 2026
- Reading time
- 1 min
- Publication type
- Knowledge Resource
- Availability
- Open access
About this Summary & Analysis
AZIZ OS provides independently prepared summaries and analytical interpretations of externally published research and knowledge sources. The underlying works remain attributable to their original authors and rights holders. This resource is intended to improve accessibility and understanding and does not replace the original publication.
A study details the development and preliminary assessment of an NLP-assisted, explainable, rule-based platform designed to provide formative feedback from academic similarity reports. This platform aims to move beyond simple textual overlap identification to offer actionable guidance for student writing revision. Fifteen instructors evaluated the system's draft feedback for 200 undergraduate cases, assessing its perceived usefulness.
Why it matters
This development addresses a critical gap in educational technology by transforming static similarity reports into dynamic, actionable feedback mechanisms. It has the potential to enhance learning outcomes by providing students with clearer guidance on improving writing, while also improving the efficiency and consistency of instructor feedback processes.
Key insights
- Traditional academic similarity reports highlight textual overlap but lack functionality to determine plagiarism or guide students on writing revision.
- The developed platform utilizes Natural Language Processing (NLP) to generate explainable, rule-based formative feedback.
- The system operates through a teacher-mediated workflow, allowing instructors to review and edit feedback drafts.
- A preliminary, feasibility-oriented assessment involved 15 instructors evaluating 200 de-identified undergraduate similarity-report cases.
- Instructor ratings (Accept, Review, Redo) were mapped to analytical categories (Correct, Partial, Adjust) to assess the platform's perceived usefulness.
- Exploratory analyses examined associations between teacher ratings and factors like severity, operational issue category, source type, and similarity percentage.
Source
Frontiers in Education — https://www.frontiersin.org/articles/10.3389/feduc.2026.1928575
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Verification
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- Verification ID
- ASA-EXE-2026-01166
- Version
- v1.0 · r0
- Issued
- 3 October 2026
- Resource prepared by
- Aziz Shuaib Ausi
- Resource status
- Research Summary / Knowledge Resource
- Underlying work
- An NLP-assisted and explainable rule-based platform for formative feedback from academic similarity reports
- Original authors
- Attribution requires verification
- Original source
- Frontiers in Education
- Provenance status
- Attribution requires verification
- Rights
- Underlying publication rights remain with the respective copyright holder(s). Refer to the original source for authoritative publication and licensing information.
This verification confirms the AZIZ OS resource record and its documented provenance. It does not establish authorship of the underlying external work.