DIGITALIZATION IN THE INCLUSIVE EDUCATIONAL PROCESS: OVERCOMING LANGUAGE BARRIERS
Bibliographic record
Abstract
The article explores the role of digitalisation in overcoming language barriers and creating an inclusive learning environment for students with diverse needs. The main problem addressed in the paper is the limited access to knowledge due to language, physical and cognitive barriers, which remains an important challenge for traditional educational systems. The purpose of the article is to analyse innovative digital solutions that can contribute to the formation of adapted learning environments for all participants in the educational process, taking into account their individual characteristics and needs. The article considers various technologies that can simplify the learning process for students with disabilities. These include adaptive learning platforms, text-to-audio programs, interactive applications that support sign language, and specialised mobile applications with built-in functions for users with special needs. An important aspect of the work is the analysis of international experience, including cases from Canada, Germany and the United States, where such technologies help to integrate the principles of inclusion into education. The author also focuses on international accessibility standards, which are the basis for developing digital learning products that meet modern requirements. The use of international standards allows for the implementation of universal solutions in the educational process that are accessible to all students and take into account their individual needs. The article shows that the introduction of digital tools and platforms is an important step towards more accessible, flexible and equitable education. The proposed approaches can serve as a basis for developing programmes and strategies aimed at integrating digitalisation into the educational process, which will reduce barriers to learning and promote an open educational environment for all students, according to their characteristics and capabilities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".