INCLUSION, DEVELOPMENT AND WELL-BEING OF NEUROATYPICAL INDIVIDUALS THROUGH ALTERNATIVE EDUCATIONAL APPROACHES: A BOON OR A BANE? A NARRATIVE LITERATURE REVIEW
Bibliographic record
Abstract
This article investigates the potential of alternative educational approaches, such as Montessori, Reggio Emilia, unschooling, forest schools, and Universal Design for Learning (UDL), to support the inclusion, development, and well-being of neuroatypical learners, particularly autistic and ADHD children, within Francophone education systems. Based on a narrative literature review methodology, the study explores theoretical foundations in developmental psychology, identifies key educational challenges faced by neuroatypical students, and examines how these pedagogies address their cognitive and developmental diversity. Findings reveal that while these alternative pedagogies offer promising pathways for fostering autonomy, social participation, and emotional regulation, they remain under-researched, particularly in French-speaking contexts. Most available studies are situated in Anglophone environments, limiting their transferability. Moreover, educational systems in France, Switzerland, and Québec often frame inclusion as a secondary measure rather than a foundational principle, contributing to systemic barriers such as discrimination, stigmatization, exclusion, and inadequate support. The article calls for a paradigm shift toward inclusive education as a structural commitment and emphasizes the need for interdisciplinary empirical research to evaluate the real impact of these pedagogical models. It concludes by advocating for a deeper societal understanding of cognitive diversity and the implementation of education policies grounded in human rights, child development, and xenosophy, a deep knowledge of the Other. Article visualizations:
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".