AccessiLearnAI: An Accessibility-First, AI-Powered E-Learning Platform for Inclusive Education
Open accessAbstract
Online education has become an important channel for extensive, inclusive and flexible learning experiences. However, significant gaps persist in providing truly accessible, personalized and adaptable e-learning environments, especially for students with disabilities, varied language backgrounds, or limited bandwidth. This paper presents AccessiLearnAI, an AI-driven platform, which converges accessibility-first design, multi-format content delivery, advanced personalization, and Progressive Web App (PWA) offline capabilities. Our solution is compliant with semantic HTML5 and ARIA standards, and incorporates features such as automatic alt-text generation for images using Large Language Models (LLMs), real-time functionality for summarization, translation, and text-to-speech capabilities. The platform, built on top of a modular MVC and microservices-based architecture, also integrates robust security, GDPR-aligned data protection, and a human-in-the-loop to ensure the accuracy and reliability of AI-generated outputs. Early evaluations indicate that AccessiLearnAI improves engagement and learning outcomes across multiple ranges of users, suggesting that responsible AI and universal design can successfully coexist to bring equity through digital education.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
Adherence to WCAG 2.1 AA and Section 508, along with the provision of a Voluntary Product Accessibility Template (VPAT), is mandatory.
“Adherence to WCAG 2.1 AA and Section 508, along with the provision of a Voluntary Product Accessibility Template (VPAT), is mandatory (Stelea, Robu and Sandu, 2025).”
From A Multi-Dimensional Decision Framework for Evaluating and Selecting AI-Enabled Instructional Technology in U.S. Higher Education (2025) · cites this paper as Stelea, Robu and Sandu, 2025
Section 2.4, “Identified Gaps” (page unavailable), calls Section 508 a legal mandate and refers to W3C guidelines. The paper also discusses WCAG 2.1 in its front-end design section, but the passages do not establish that WCAG 2.1 AA compliance or provision of a VPAT is mandatory. (LLM, conf=0.85)
Experimental — accuracy not yet measuredWCAG 2.1 AA accessibility standards are a non-negotiable criterion in institutional procurement analysis.
“Institutional procurement analysis identifies FERPA compliance, WCAG 2.1 AA accessibility standards, and data residency requirements as non-negotiable criteria.”
From A Multi-Dimensional Decision Framework for Evaluating and Selecting AI-Enabled Instructional Technology in U.S. Higher Education (2025) · cites this paper as Stelea, Robu and Sandu, 2025
Section 2.1 discusses WCAG 2.1 compliance in e-learning platforms, and Section 3.2.1 says the platform follows WCAG 2.1 guidelines. Neither passage addresses WCAG 2.1 AA as a non-negotiable institutional procurement criterion; searches for procurement, FERPA, data residency, and WCAG 2.1 AA did not locate such an analysis in our copy. (LLM, conf=0.74)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Artificial Intelligence in Education: A Review
Chen, L., Chen, P., & Lin, Z. (2020) · IEEE Access · doi:10.1109/access.2020.2988510 · Cited by 4,133 · see citing works
Artificial intelligence in higher education: the state of the field
Crompton, H., & Burke, D. (2023) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-023-00392-8 · Cited by 2,010 · see citing works
Explainable AI (XAI): Core Ideas, Techniques, and Solutions
Dwivedi, R., Dave, D., Naik, H., Singhal, S., Omer, R., Patel, P., Qian, B., Wen, Z., Shah, T., Morgan, G., et al. (2023) · ACM Computing Surveys · doi:10.1145/3561048 · Cited by 1,386 · see citing works
Human-in-the-loop machine learning: a state of the art
Mosqueira-Rey, E., Hernández-Pereira, E., Alonso-Ríos, D., Bobes-Bascarán, J., & Fernández-Leal, Á. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10246-w · Cited by 1,137 · see citing works
A survey on large language model (LLM) security and privacy: The Good, The Bad, and The Ugly
Yao, Y., Duan, J., Xu, K., Cai, Y., Sun, Z., & Zhang, Y. (2024) · High-Confidence Computing · doi:10.1016/j.hcc.2024.100211 · Cited by 1,083 · see citing works
Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review
Gligorea, I., Cioca, M., Oancea, R., Gorski, A.-T., Gorski, H., & Tudorache, P. (2023) · Education Sciences · doi:10.3390/educsci13121216 · Cited by 980 · see citing works
Yenduri, G., Ramalingam, M., Selvi, G. C., Supriya, Y., Srivastava, G., Maddikunta, P. K. R., Raj, G. D., Jhaveri, R. H., Prabadevi, B., Wang, W., et al. (2024) · IEEE Access · doi:10.1109/access.2024.3389497 · Cited by 759 · see citing works
An Overview of Artificial Intelligence Ethics
Huang, C., Zhang, Z., Mao, B., & Yao, X. (2022) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2022.3194503 · Cited by 583 · see citing works
AI-based personalized E-learning systems: Issues, challenges, and solutions
Murtaza, M., Ahmed, Y., Shamsi, J. A., Sherwani, F., & Usman, M. (2022) · IEEE Access · doi:10.1109/access.2022.3193938 · Cited by 386 · see citing works
Worldwide AI ethics: A review of 200 guidelines and recommendations for AI governance
Corrêa, N., Galvão, C., Santos, J., del Pino, C., Pinto, E. P., Barbosa, C., Massmann, D., Mambrini, R., Galvão, L., Terem, E., et al. (2022) · Patterns · doi:10.1016/j.patter.2023.100857 · Cited by 330 · see citing works
Factors influencing teacher self-efficacy for inclusive education: A systematic literature review
Wray, E., Sharma, U., & Subban, P. (2022) · Teaching and Teacher Education · doi:10.1016/j.tate.2022.103800 · Cited by 250 · see citing works
Neural machine translation in foreign language teaching and learning: a systematic review
Klímová, B., Pikhart, M., Benites, A. D., Lehr, C., & Sanchez‐Stockhammer, C. (2022) · Education and Information Technologies · doi:10.1007/s10639-022-11194-2 · Cited by 236 · see citing works
Metacognition: ideas and insights from neuro- and educational sciences
Fleur, D. S., Bert Bredeweg, A., & van den Bos, W. (2021) · npj Science of Learning · doi:10.1038/s41539-021-00089-5 · Cited by 221 · see citing works
Bray, A., Devitt, A., Banks, J., Sánchez, S., Mena, M. S., Riviou, K., Byrne, D., Flood, M., Reale, J., & Terrenzio, S. (2023) · British Journal of Educational Technology · doi:10.1111/bjet.13328 · Cited by 208 · see citing works
Algorithmic Impact Assessments and Accountability: The Co-construction of Impacts
Metcalf, J., Moss, E., Watkins, E. A., Singh, R., & Elish, M. C. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445935 · Cited by 195 · see citing works
Tate, T. P., & Warschauer, M. (2022) · Educational Psychologist · doi:10.1080/00461520.2022.2062597 · Cited by 167 · see citing works
Ethical artificial intelligence for teaching-learning in higher education
Airaj, M. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-12545-x · Cited by 115 · see citing works
RESTful API Testing Methodologies: Rationale, Challenges, and Solution Directions
Ehsan, A., Abuhaliqa, M. A. M. E., Catal, C., & Mishra, D. (2022) · Applied Sciences · doi:10.3390/app12094369 · Cited by 100 · see citing works
A Systematic Review of the Use of Bloom's Taxonomy in Computer Science Education
Masapanta-Carrión, S., & Velázquez‐Iturbide, J. Á. (2018) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/3159450.3159491 · Cited by 83 · see citing works
Natural Language Processing (NLP): An Introduction
Egger, R., & Gokce, E. (2022) · Tourism on the verge · doi:10.1007/978-3-030-88389-8_15 · Cited by 57 · see citing works
Quality Assurance in E-Learning: A Proposal from Accessibility to Sustainability
Timbi-Sisalima, C., Sánchez‐Gordón, M., Hilera, J. R., & Otón, S. (2022) · Sustainability · doi:10.3390/su14053052 · Cited by 52 · see citing works
Artificial Intelligence and Inclusive Education: Speculative Futures and Emerging Practices
(2019) · Perspectives on Rethinking and Reforming Education · doi:10.1007/978-981-13-8161-4 · Cited by 51 · see citing works
Role of Artificial Intelligence in Online Education: A Systematic Mapping Study
Shafique, R., Aljedaani, W., Rustam, F., Lee, E., Mehmood, A., & Choi, G. S. (2023) · IEEE Access · doi:10.1109/access.2023.3278590 · Cited by 46 · see citing works
School Effectiveness in Multilingual Education: A Review of Success Factors
Kirss, L., Säälik, Ü., Leijen, Ä., & Pedaste, M. (2021) · Education Sciences · doi:10.3390/educsci11050193 · Cited by 44 · see citing works
Liew, T. W., Tan, S.-M., Pang, W. M., Khan, M. T. I., & Kew, S. N. (2022) · Education and Information Technologies · doi:10.1007/s10639-022-11255-6 · Cited by 42 · see citing works
Kazimzade, G., Patzer, Y., & Niels Pinkwart, C. (2019) · Perspectives on rethinking and reforming education · doi:10.1007/978-981-13-8161-4_4 · Cited by 40 · see citing works
AI-assisted audio-learning improves academic achievement through motivation and reading engagement
Jafarian, N. R., & Kramer, A. (2024) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2024.100357 · Cited by 39 · see citing works
Acosta-Vargas, P., Salvador-Acosta, B., Novillo-Villegas, S., Sarantis, D., & Salvador-Ullauri, L. (2024) · Emerging Science Journal · doi:10.28991/esj-2024-08-04-021 · Cited by 34 · see citing works
Encyclopedia of Cryptography, Security and Privacy
Jajodia, S., Samarati, P., & Yung, M. (2025) · Springer eBooks · doi:10.1007/978-3-030-71522-9 · Cited by 29 · see citing works
The use of accessibility metadata in e-learning environments: a systematic literature review
Ingavélez-Guerra, P., Otón, S., Hilera-González, J. R., & Sánchez‐Gordón, M. (2021) · Universal Access in the Information Society · doi:10.1007/s10209-021-00851-x · Cited by 26 · see citing works
Khamzina, K., Stanczak, A., Brasselet, C., Desombre, C., Legrain, C., Rossi, S., Guirimand, N., & Cilia, F. (2024) · Educational Psychology Review · doi:10.1007/s10648-024-09851-8 · Cited by 24 · see citing works
Exploring students’ perceptions of the design and use of the Moodle learning management system
Al-Fraihat, D., Alshahrani, A. M., Alzaidi, M. S., Shaikh, A. A., Al-Obeidallah, M. G., & Al‐Okaily, M. (2025) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2025.100685 · Cited by 23 · see citing works
Jónsdóttir, A. A., Kang, Z., Sun, T., Mandal, S., & Kim, J.-E. (2021) · Human Factors The Journal of the Human Factors and Ergonomics Society · doi:10.1177/00187208211010949 · Cited by 23 · see citing works
Cross-Site Scripting Attacks and Defensive Techniques: A Comprehensive Survey*
Weamie, S. J. Y. (2022) · International Journal of Communications Network and System Sciences · doi:10.4236/ijcns.2022.158010 · Cited by 20 · see citing works
Model for Profiling Users With Disabilities on e-Learning Platforms
Sánchez-Gordón, S., Aguilar-Mayanquer, C., & Calle-Jiménez, T. (2021) · IEEE Access · doi:10.1109/access.2021.3081061 · Cited by 19 · see citing works
Fairness and Data Protection Impact Assessments
Kasirzadeh, A., & Clifford, D. (2021) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3461702.3462528 · Cited by 17 · see citing works
Humanising Text-to-Speech Through Emotional Expression in Online Courses
Hillaire, G., Iniesto, F., & Rienties, B. (2019) · Journal of Interactive Media in Education · doi:10.5334/jime.519 · Cited by 17 · see citing works
Contemporary Issues in Communication, Cloud and Big Data Analytics
(2021) · Lecture notes in networks and systems · doi:10.1007/978-981-16-4244-9 · Cited by 16 · see citing works
A Survey on Trends of Two-Factor Authentication
Tirfe, D., & Anand, V. K. (2021) · Lecture notes in networks and systems · doi:10.1007/978-981-16-4244-9_23 · Cited by 16 · see citing works
Niarman, A., Iswandi, & Candri, A. K. (2023) · International Journal Software Engineering and Computer Science (IJSECS) · doi:10.35870/ijsecs.v3i3.1850 · Cited by 14 · see citing works
Acosta-Vargas, P., Acosta-Vargas, G., Salvador-Acosta, B., & Jadán-Guerrero, J. L. (2024) · International Conference on eDemocracy & eGovernment · doi:10.1109/icedeg61611.2024.10702085 · Cited by 13 · see citing works
Tiwary, T., & Mahapatra, R. P. (2022) · 2022 3rd International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT) · doi:10.1109/icict55121.2022.10064545 · Cited by 12 · see citing works
PWA vs the Others: A Comparative Study on the UI Energy-Efficiency of Progressive Web Apps
Huber, S., Demetz, L., & Felderer, M. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-74296-6_35 · Cited by 10 · see citing works
Competitiveness, quality education and universities: the shift to the post-pandemic world
Trapero, F. G. A., Guerra-Leal, E. M., Kim, J., & Parra, J. C. V. (2024) · Journal of Applied Research in Higher Education · doi:10.1108/jarhe-08-2023-0376 · Cited by 8 · see citing works
Stelea, G. A., Sângeorzan, L., & David, N. (2025) · Future Internet · doi:10.3390/fi17020067 · Cited by 6 · see citing works
Self-citationAccessible IoT Dashboard Design with AI-Enhanced Descriptions for Visually Impaired Users
Stelea, G. A., Sangeorzan, L., & Enache-David, N. (2025) · Future Internet · doi:10.3390/fi17070274 · Cited by 5 · see citing works
Self-citationAI Enhancements for Linguistic E-Learning System
Liu, J., Li, S., Ren, C., Lyu, Y., Xu, T., Wang, Z., & Chen, W. (2023) · Applied Sciences · doi:10.3390/app131910758 · Cited by 5 · see citing works
Analyzing Database Optimization Strategies in Laravel for an Enhanced Learning Management
Sasmoko, Indrianti, Y., Manalu, S. R., & Danaristo, J. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.10.306 · Cited by 5 · see citing works
WebSight: An AI-based Approach to Enhance Web Accessibility for the Visually Impaired
S, S. R. M., Joy, E., & Leka Shree, J. (2024) · doi:10.1109/icstem61137.2024.10561228 · Cited by 5 · see citing works
Proceedings of the 2nd International Conference on Emerging Technologies and Intelligent Systems
Al-Sharafi, M. A., Al-Emran, M., Al-Kabi, M. N., & Shaalan, K. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-25274-7 · Cited by 4 · see citing works
Mukherjee, D., & Hasan, K. K. (2022) · Springer proceedings in business and economics · doi:10.1007/978-981-19-0357-1_15 · Cited by 3 · see citing works
Comparison of Web Based and PWA in Online Learning
Nugraha, D. T., Anjara, F., & Faizah, S. (2022) · Atlantis highlights in engineering/Atlantis Highlights in Engineering · doi:10.2991/ahe.k.220205.035 · Cited by 3 · see citing works
Web Application Security Threats and Mitigation Strategies when Using Cloud Computing as Backend
Yamani, A. Z., Bajbaa, K., & Aljunaid, R. (2022) · International Conference on Computational Intelligence and Communication Networks (CICN) · doi:10.1109/cicn56167.2022.10008368 · Cited by 2 · see citing works
Developing an online/offline educational application to the students in rural areas
Lakshmi, G. J., Surekha, T. L., Harathi, R., & Bhargavi, B. (2025) · A.A. Balkema eBooks · doi:10.1201/9781003587392-18 · Cited by 1 · see citing works
Flagged first, then by citations
Research works printed but not linked: 3
- Nasereddin, M., ALKhamaiseh, A., Qasaimeh, M., & Al-Qassas, R. (2021). A systematic review of detection and prevention tech- niques of SQL injection attacks. Information Security Journal: A Global Perspective, 32(4), 252-265. https://doi.org/10.1080/19393555.2021.1995537. · DOI
- Reddy, V. M., Vaishnavi, T., & Kumar, K. P. (2023, July 19-21). Speech-to-text and text-to-speech recognition using deep learning. 2nd International Conference on Edge Computing and Applications (ICECAA) (pp. 657-666), Namakkal, India. https://doi.org/10.1109/ICECAA58104.2023.10212222. · DOI
- Sagi, S. (2025). Optimizing LLM inference: Metrics that matter for real time applications. Journal of Artificial Intelligence & Cloud Com- puting, 2025(4), 2-4. https://doi.org/10.47363/JAICC/2025(4)446. · DOI
Cited by
Sorted by citations
Advancing Inclusive Education Through Artificial Intelligence and Assistive Innovation
Kumar, R., & Kaur, M. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-2716-5.ch001 · Cited by 6 · see citing works
Chaotic Polar Light Optimization with CatBoost for Performance Analysis of Blind Adaptive E-Learners
Yuvapriya, P., Subramanian, P., & Surendran, R. (2025) · International Conference on Applied Artificial Intelligence and Computing (ICAAIC) · doi:10.1109/icaaic64647.2025.11330235 · Cited by 1 · see citing works
Gao, M., Liu, Y., & Lin, X. Y. (2026) · Adult Learning · doi:10.1177/10451595261436346 · Cited by 2 · see citing works
Karabatak, S., Alanoğlu, M., Karabatak, M., & Basatoğrul, B. (2026) · International Symposium on Digital Forensics and Security (ISDFS) · doi:10.1109/isdfs69419.2026.11458986 · Cited by 1 · see citing works
Xiao, T., Tian, X., & Yang, M. (2026) · International Conference on Digital Education and Information Technology (DEIT) · doi:10.1145/3802607.3802624 · Cited by 1 · see citing works
Irikefe, P. O., Ullah, I., Falahat, M., Rahman, S. U., Kee, D. M. H., & Akpowene, T. O. (2026) · Asian Education and Development Studies · doi:10.1108/aeds-02-2026-0086 · Cited by 1 · see citing works
Customizing Playful Education Through Gamification
Khun, J. L. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7225-9.ch010
Akter, F., & Parvatha Reddy, K. K. (2025) · International Journal For Multidisciplinary Research · doi:10.36948/ijfmr.2025.v07i06.84921
“Adherence to WCAG 2.1 AA and Section 508, along with the provision of a Voluntary Product Accessibility Template (VPAT), is mandatory (Stelea, Robu and Sandu, 2025).”
InfluentialNatsiopoulos, G. D. (2025) · International Journal of Research and Review · doi:10.52403/ijrr.20251212
Pliasun, B. (2025) · Bulletin of Luhansk Taras Shevchenko National University Pedagogical Sciences · doi:10.12958/3083-6514-2025-4-31-37
Corry, M. D. (2026) · Quarterly review of distance education · doi:10.1108/qrde-12-2025-0027
A Multi-Dimensional Framework for Adaptive Scaffolding in Large Language Model
Yabouri, H. E., Hibbi, F.-Z., & Abdoun, O. (2026) · International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) · doi:10.1109/iraset68627.2026.11538832
Ayoubi, A., Laaouina, L., Jeghal, A., & Tairi, H. (2026) · International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) · doi:10.1109/iraset68627.2026.11538523
Pachay, A., & Vera, A. A. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-30524-4_11
An Accessible AI-Powered System for Real-Time Student Answer Evaluation and Feedback
Nallapareddy, S. R., Akula, V., Rajoli, N. R., Bigonah, M., Gautam, S. S., & Marghitu, D. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-29653-5_39
Mahesh, B., & Kanagalakshmi, K. (2026) · International Conference on Emerging Trends in Mobile Computing and Sustainable Informatics (ICEMCSI) · doi:10.1109/icemcsi67638.2026.11603019
Decolonizing AI-Assisted Language Development in Children With Autism and Disabilities
Ali, J. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7724-7.ch009
Mahmudah, A., Prastowo, A., Hidayat, N., & Muqowin (2026) · Decision Sciences in Human Learning and Development · doi:10.64268/decision.v1i1.5
Okuonghae, O. (2026) · College & Undergraduate Libraries · doi:10.1080/10691316.2026.2705481
Virtual Assistants for Deaf and Hard-of-Hearing Students
Kamiński, K. (2026) · Springer eBooks · doi:10.1007/978-3-031-51447-0_38-1
Evaluation of Inductive Coding with LLMs
Dörfel, L., & Ammoneit, R. (2026) · Education Sciences · doi:10.3390/educsci16081314
Damasco, B. J. K. (2026) · Multidisciplinary Reviews · doi:10.31893/multirev.2027093
González, J. J., Arteaga, J. M., Rojano-Cáceres, J. R., & Amador, C. E. V. (2026) · International Multidisciplinary Journal of Emerging Technologies and Applications · doi:10.67294/w7x3p272
Eğitimde Yapay Zekâ: İlkokul Öğretmenlerinin Perspektifinden Bir İnceleme
Kaya, E., & Yıldırım, N. (2026) · Temel Eğitim · doi:10.52105/temelegitim.1937771
Open Education for Equity in AI-Supported Learning: A Randomized SPOC Study
Hadyaoui, A., & Cheniti‐Belcadhi, L. (2026) · IEEE International Conference on Advanced Learning Technologies (ICALT) · doi:10.1109/icalt68022.2026.00011
- Authors
- Stelea, G. A., Robu, D., & Sandu, F.
- Affiliations
- Transilvania University of Brașov (RO)
- Corresponding author
- Stelea, George Alex
- Venue
- Education Sciences
- Publisher
- MDPI AG
- Published
- 2025-08-29
- Volume
- 15
- Issue
- 9
- Pages
- 1125
- ISSN
- 2227-7102 (ISSN-L)
- OpenAlex
- W4413817022
- Cited by
- 24
- References
- 46
- Citations by year
- 2026: 21 · 2025: 3
- FWCI
- 146.11
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Education Sciences (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Transilvania University of Brașov
- Topics
- Social Sciences › Human Factors and Ergonomics › Digital Accessibility for Disabilities
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Information Systems › Educational Technology in Learning
- Keywords
- e-learning accessibility, inclusive education, students with disabilities, large language models, text-to-speech, machine translation, content summarization, progressive web app, offline learning, Universal Design for Learning, human-in-the-loop, microservices, GDPR compliance, responsible AI, personalized learning
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
