Evidence map›Paper›PMID 42447173›Full record

ArticlePloS one2026

Using natural language processing to support digital communication in adolescents and young adults with autism spectrum disorder.

Faris Algahtani

Abstract read
In one paragraph

Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Faris AlgahtaniDepartment of Special Education, Faculty of Education, University of Jeddah, Jeddah, Saudi Arabia.ORCID https://orcid.org/0000-0002-4941-3442

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAutism Spectrum Disorder (ASD) is characterized by social communication challenges, including difficulties interpreting figurative language, understanding conversational context, and expressing thoughts clearly. This feasibility study examined an NLP-based communication assistance tool for adolescents and young adults with ASD. The tool combined sentiment analysis, intent identification, and situational simplification to provide real-time feedback in digital communication.

methodsParticipants were 16 individuals with ASD (aged 15-28 years) and 16 neurotypical conversation partners in a within-subjects, mixed-methods design. The intervention lasted eight weeks. The tool was used in both structured tasks and naturalistic conversations.

resultsPreliminary data suggest associations between tool use and improved outcomes. Communication clarity increased from 3.12 to 3.89 (d = 0.74, 95% CI [0.28, 1.20], p < 0.001 after Bonferroni correction). Anxiety decreased by 2.3 points on a 7-point scale (d = 1.21, 95% CI [0.62, 1.80], p < 0.001). Confidence scores improved by 42% from baseline (d = 1.15, 95% CI [0.56, 1.74], p < 0.001). Qualitative thematic analysis (Braun & Clarke, 2006; inter-coder reliability, κ = 0.81) identified five themes: reduced communication burden, learning through use, empowerment and autonomy, context-specific value, and desire for customization. These preliminary findings suggest that NLP-driven assistive technologies warrant further investigation in controlled trials.

conclusionThis study demonstrates feasibility and provides preliminary evidence for NLP-based communication support, though causal conclusions cannot be drawn due to the absence of a control group, small sample size (N = 16), and short duration.

Indexed as

Autism Spectrum DisorderCommunicationNatural Language ProcessingAdolescentAdultDigital MediaFeasibility StudiesFemaleHumansMaleYoung Adult

Identifiers

PMID42447173
PMCPMC13367686

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.