Inclusive design

Inclusive design is the practice of making information and interfaces work for people who differ in language, reading level, cognition, sensory ability, culture and expertise, instead of building one default for an imagined average user. Two ideas run through it. The first is adaptation: finding out what an individual needs or prefers and changing the presentation to fit, whether the user chooses the change or a system infers it. The second is the curb-cut effect, the claim that an alternative built for some users (captions, plain language, a text description) also helps everyone else. The evidence for adaptation is thin and shows that wrong guesses cost users. The evidence for the curb-cut effect is strong for video captions, but other features show no benefit, a benefit for novices only, or have not been tested.

History

The research did not cover the origin of the term "curb-cut effect" or the history of inclusive and universal design as movements. Dates for the standards that appear below (WAI-Adapt, CAST guidelines) are given where they are used.

Core concepts

Adaptation to the individual

Standards bodies and design guidance ask for content to adapt to the person. The W3C’s WAI-Adapt work aims to let content be tailored to individual users’ differing and conflicting needs, and defines personalization as tailoring the user experience to individual preferences or needs (Seeman et al., 2023a). Its Symbols module lets user agents (browsers and similar software) transform content using references to augmentative and alternative communication symbols, to support people with cognitive and learning disabilities (Seeman et al., 2023b). Microsoft’s guidelines for human-AI interaction advise letting users give feedback on preferences during normal use and customize globally what the system monitors and how it behaves (Amershi et al., 2019).

Findings on what people need are often specific to a group. In a study of descriptions of charts, blind and sighted readers differed significantly in which kinds of description content they ranked most useful, so one description style does not fit all readers (Lundgard & Satyanarayan, 2021). The most developed case is expertise. The expertise reversal effect is the finding that instructional support helping novices can lose its benefit or reverse for learners with more prior knowledge. A review spanning medical expertise, cognitive load in technical and academic domains, and sports movement found that more experienced learners may learn less than expected from instruction that is very effective for novices, with common underlying mechanisms (Kalyuga et al., 2012). A 2025 meta-analysis of 60 experiments reports that giving novices assistance has a stronger effect than withholding it from experts (Tips for Teachers, 2026).

Adaptation to expertise has been tested directly in tutoring. In two experiments with tutored problem solving, fading worked examples adaptively to each student beat fixed fading, which beat problem solving alone (Salden et al., 2010). In a controlled experiment with 155 undergraduates, buggy examples helped high-knowledge learners and guided examples helped low-knowledge learners (Tithi et al., 2026). The same paper notes that worked examples in tutoring systems often show an aptitude-treatment interaction in which learners with low prior knowledge benefit more (Tithi et al., 2026). For text, one usability study with domain experts in science, technology and medicine found that even highly educated readers want succinct, scannable text, though the evidence is weak because no sample size was given (Loranger, 2017). A study of plain language summaries of science found that simpler versions gave the best reading experience to readers with little or no topic familiarity, while readers highly familiar with the topic skipped details such as study limitations when summaries were overly plain (August et al., 2024).

For AI assistants, adapting to expertise has one small test. In a 40-participant study, adapting responses to the expertise inferred for each query raised satisfaction from 3.71 to 4.36 on a 5-point scale, a satisfaction measure and not a measure of comprehension or task success (Park et al., 2026).

Finding out what a user needs

An adaptive system must learn the user’s needs, and the sources describe two routes: ask, or infer. Style guides in journalism and technology take the first route for how to refer to disabled people. The National Center on Disability and Journalism guide no longer offers a default between person-first and identity-first wording and tells writers to find out, by asking the person, how they want to be described (National Center on Disability & Journalism, 2021). Apple’s style guide likewise says preferences vary and writers should ask the people concerned (Apple Inc., 2026).

Inference has a record of going wrong. In a CHI 2008 study of adaptive toolbars, higher accuracy of the system’s adaptation improved performance and use of the adaptive interface, more than predictability did, and both raised satisfaction, so a system that infers wrongly or erratically loses satisfaction (Gajos et al., 2008). Critics of adaptive interfaces argue that unpredictable adaptation can disorient users and do more harm than good (Gajos et al., 2008). In a benchmark of 21 models, 29.0% of naive personalization attempts gave worse preference alignment than generic responses, so personalizing without first eliciting preferences can backfire, although generic responses also fail individuals (Li et al., 2026c).

Matching to a person’s own stated preference is also not guaranteed to help. A 2024 meta-analysis of 21 studies found a small overall benefit of matching instruction to learning styles, but only about a quarter of outcome measures showed the crossover pattern the matching hypothesis requires, and the authors judged the benefit too small and infrequent to justify adopting it, suggesting teaching with multiple modalities instead (Clinton-Lisell & Litzinger, 2024). Willingham and colleagues state that scientific support for learning styles theories is lacking although many people believe them (Willingham et al., 2015). People’s judgments of what helps them can be wrong: they judge blocked study as more effective than interleaved study although interleaving gives better delayed memory (Krasnoff & Overkott, 2022).

Questions, controversies, and new developments

Do features built for some users help everyone?

Gernsbacher’s review frames captions as a curb-cut feature, developed for people with disabilities and beneficial to people with and without them. It reports over 100 empirical studies in which captioning a video improves comprehension, including for highly literate hearing adults (Gernsbacher, 2015). The review names a limit: automatic captions with errors can interfere with the benefit (Gernsbacher, 2015). A 2021 Stagetext survey found most young UK adults used subtitles, and only 10% of those users said they were deaf or hard of hearing, so use of the feature is mostly by people without a hearing need. This shows use, not improved outcomes (Stagetext, 2023).

The effect fails elsewhere. In two experiments with college students, on-screen text identical to the narration impaired recall and transfer, yet learners preferred it; slightly reworded text gave better recall and transfer, so learners’ preferences were a poor guide to what helped (Yue et al., 2013). The OpenDyslexic font, aimed at dyslexic readers, gave no gain in reading rate or accuracy in a small alternating-treatment study, even for the dyslexic students it targets (Wery & Diliberto, 2016). For simplified text, an eye-tracking study of adult native English readers found that simplified news texts made reading easier for 42% of readers but improved comprehension accuracy for only 2%; fluency gains were larger for slower, less experienced readers and comprehension gains for lower-comprehension readers (Gruteke Klein et al., 2025). A randomized trial of 143 members of the public found a structured plain language summary (a narrative plus a results table) was understood by more people than the standard Cochrane format (53% vs 18%), independent of education level, but it tested the whole format and not wording alone (Santesso et al., 2015). A 2026 systematic review of 29 studies found the evidence that Easy Read health information improves understanding limited and mostly low quality (Wilson et al., 2026).

Visuals added to text follow the same pattern of average benefit with a reversal for experts. Across 39 experiments, graphics added to text improved reading comprehension at a moderate level (Guo et al., 2020), and a 2025 meta-analysis of 63 studies found a small positive effect of adding illustrations to science texts, weaker for explanative, representational and incongruent illustrations (Lei et al., 2025). Novices gained from text integrated into diagrams, but more experienced learners did better with a diagram only (Cobb, 2021).

Inclusive design methods assert the generalization more than they test it. Microsoft’s inclusive design method holds that a solution made for one excluded person can be extended to many, using a persona spectrum across permanent, temporary and situational limits, but it offers this as a method and cites no outcome data (Microsoft Design, 2025). A practitioner critique argues that situational or temporary disabilities are weak proxies for permanent disabilities, so the generalization from one group to all is limited (Spillers, 2025). A study of a mixed-ability nonprofit reports that the access needs of different people sometimes conflict, so a feature can benefit some people while constraining others (Tang & Piper, 2026).

Universal Design for Learning

The CAST Universal Design for Learning Guidelines 3.0 (July 2024) organise recommendations under three principles: multiple means of engagement, representation, and action and expression (CAST, 2024). A systematic review of 13 studies in pre-K to grade 12 found mixed efficacy, with effect sizes from small to large, and the studies labelled UDL-based differed widely in how they linked the guidelines to the intervention components, so it is unclear which element drives any effect (Ok et al., 2016). A critique by Boysen (2021) argues that UDL, like learning styles, lacks evidence that implementing it increases student learning, though it could generate such evidence through controlled trials (Ashman, 2021).

Risks of inferring needs with AI

Models infer demographics from conversational cues (implicit personalization), and earlier work links this to lower-quality responses for users assumed to belong to minority groups; inference from stereotypical cues can persist for some groups even when the user states a different identity, which the authors say calls for transparency and control (Neplenbroek et al., 2025). Response quality is worse in some conditions already: undesirable behaviours occur disproportionately for users with lower English proficiency, lower education and origin outside the US (Poole-Dayan et al., 2024). ChatGPT responses to non-standard English varieties were rated by native speakers as worse on stereotyping and condescension (Fleisig et al., 2024), and models give less accurate answers and simpler reasoning explanations to African American English input than to equivalent Standard American English input (Zhou et al., 2025). Across 10 LLMs, 8 languages and 4 accessibility noise classes, models that recover intent from degraded input often add burden or hallucinated content (Pattnayak & Banerjee, 2026), and neurodivergent Reddit users report overly neurotypical responses and work around them by modifying prompts (Carik et al., 2024).

Personalization itself has costs. Distilled user memory profiles gave the largest rise in agreement sycophancy across tested models, with variation by model, and perspective sycophancy (mirroring the user’s viewpoint) rose only when the model could infer that viewpoint from context (Jain et al., 2026). Across 13 LLMs, adding user profiles and retrieved memory worsened irrelevant personalization, preference narrowing and sycophantic bias (Wang et al., 2026a); see Sycophancy in language models. Memory also creates privacy risk: in a sample of 80 ChatGPT users, 96 percent of memories were created by the system itself, 28 percent contained personal data as defined by the GDPR and 52 percent held psychological insights (Dash et al., 2026). A contributor to the W3C accessibility working group argued that preference settings are essential for disabled people but reveal disability, and that disabled people object to automated disclosure of their status (Wolberger, 2022).

Maturity of standards

WAI-Adapt is an explainer and draft modules; the Explainer is a draft note not endorsed by W3C or its members and may be replaced, and only the Symbols module is advancing to Candidate Recommendation (Seeman et al., 2023a). To exit that stage the Symbols module needs at least two user agents or extensions that react to the attribute (Seeman et al., 2023b).

Broader connections

Adaptation to readers’ language, reading level and expertise is treated as a matter of wording in Writing for comprehension, and the presentation of text and visuals in Evidence-based interface design. How people who think and communicate differently are served by explanation is the subject of Neurodivergence and communication. The expertise reversal findings above also connect to adapting explanations to what a learner already knows.

Further reading

• Gernsbacher (2015): the clearest case of the curb-cut claim, with the limit of error-prone automatic captions.
• Kalyuga et al. (2012): review of the expertise reversal effect across fields.
• Gajos et al. (2008): why the accuracy of an adaptive interface matters more than whether it is predictable.
• Clinton-Lisell and Litzinger (2024): meta-analysis of matching instruction to learning styles.
• Yue et al. (2013): a case where giving learners more access, in the form of redundant on-screen text, impaired learning.