Generative AI and Education: Digital Pedagogies, Teaching Innovation and Learning Design
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
PREPARE demonstrates an automation-first authoring workflow that converts a single textbook into a deployable Moodle course through quota-aware orchestration and HTML-based packaging, enabling repeatable course-scale production without exposing internal code.
“Beyond the educational benefits, PREPARE demonstrates an automation-first authoring workflow that converts a single textbook into a deployable Moodle course through quota-aware orchestration and HTML-based packaging, enabling repeatable course-scale production without exposing internal code [23].”
From AI for All: Adaptive, Accessible, and Inclusive Learning Experiences in the Age of Intelligent LMSs (2026) · cites this paper as [23]
The citation lists haven’t been fetched yet
Cited by 92
- Authors
- Pratschke, B. M.
- Affiliations
- University of Manchester (GB)
- Corresponding author
- Pratschke, B. Mairéad
- Venue
- SpringerBriefs in Education
- Publisher
- Springer Nature Switzerland
- Published
- 2024
- ISSN
- 2211-1921 (ISSN-L)
- 2211-193X
- OpenAlex
- W4401929609
- Cited by
- 92
- References
- 0
- Citations by year
- 2026: 54 · 2025: 34 · 2024: 4
- FWCI
- 50.83
- Type
- book
- Language
- en
- Open access
- closed
- Licence
- Locations
- Springer briefs in education (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Keywords
- generative AI, education, digital pedagogy, artificial intelligence in education
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 ›
Are you an author, or do you see a problem with this page? Request a review
