Emergent literacy refers to the foundational knowledge and skills that precede formal reading and writing instruction, including phonological awareness, morphological awareness, orthographic awareness, and oral vocabulary. Researchers often divide these precursors into code-related skills, such as alphabet knowledge and print concepts, and meaning-related skills, such as vocabulary and listening comprehension. Because these early abilities reliably predict later reading fluency and comprehension, identifying where autistic children diverge from their peers has become a pressing question for educators and clinicians alike.

Most previous studies of emergent literacy in autism have been conducted in English-speaking or other alphabetic-language contexts. Chinese presents a fundamentally different challenge for young readers. Unlike alphabetic letters, which map onto phonemes, each Chinese character represents a tonal syllable and a unit of meaning. Characters are visually complex, built from stroke patterns, and many are semantic-phonetic compounds containing a radical that hints at meaning and another that suggests pronunciation. Learning to read Chinese therefore places heavy demands on visual memory and on the ability to perceive and process morphological structure.

The research team, led by Zhuo Chen of Shaanxi Normal University together with colleagues at Texas A&M University, recruited children from four inclusive kindergartens serving working-class families. The autistic and typically developing groups were closely matched in age and nonverbal reasoning as measured by Raven’s Colored Progressive Matrices. Autism diagnoses were confirmed with the Autism Diagnostic Observation Schedule, and all participating autistic children could produce short sentences, understand oral instructions, and use spoken language for basic communication. None of the kindergartens provided formal literacy instruction, so the skills measured reflected spontaneous early development rather than classroom teaching.

Each child completed a battery of assessments covering working memory, inhibitory control, rapid automatized naming, phonological awareness through a syllable deletion task, morphological awareness through a compounding construction task, orthographic awareness through tasks distinguishing real characters from pseudo-characters and legal stroke patterns from illegal ones, character recognition, and receptive and expressive vocabulary. The researchers then applied independent-sample t-tests with Bonferroni correction and hierarchical regression models to compare groups and identify predictors of character recognition and vocabulary knowledge.

The results revealed a strikingly uneven profile. Children with autism performed significantly worse than their peers on phonological awareness, morphological awareness, orthographic awareness, and both receptive and expressive vocabulary. They also showed weaker working memory and inhibitory control. Yet on character recognition and rapid automatized naming, the two groups were statistically indistinguishable. The authors suggest that a detail-focused cognitive processing style, long documented in autism, may help these children extract the visual patterns of Chinese characters with unusual precision, laying a foundation for successful decoding despite broader language difficulties.

Regression analyses showed that autism severity, nonverbal reasoning, inhibitory control, rapid naming, phonological awareness, and orthographic awareness together predicted character recognition among the autistic children, with the full model explaining roughly 87 percent of the variance. For typically developing children, working memory and morphological awareness emerged as additional unique predictors. Notably, orthographic awareness, the understanding of legal stroke patterns and positional rules governing how characters are assembled, contributed significantly to character reading in both groups, underscoring the importance of visual-orthographic discrimination in Chinese literacy acquisition.

The predictors of oral vocabulary told a different story. For autistic children, autism severity, nonverbal reasoning, rapid naming, and morphological awareness significantly predicted receptive vocabulary, while expressive vocabulary depended heavily on receptive vocabulary itself. For typically developing children, morphological awareness predicted both receptive and expressive vocabulary. The authors interpret this asymmetry cautiously: morphological awareness appears strongly tied to word meaning across both groups, but expressive language development in autism likely involves additional factors beyond emergent literacy skills, and the field’s findings on receptive-expressive gaps in autism have been mixed.

Why do autistic Chinese children lag specifically in phonological awareness? The study’s syllable deletion task required children to manipulate sound units mentally, a process demanding complex cognitive and linguistic control. Earlier work suggests autistic children fare better on simpler phoneme isolation tasks than on blending and deletion, and difficulties with social interaction and language comprehension may compound the challenge in Chinese, where syllable awareness develops largely through language exposure. Similarly, delays in orthographic awareness may reflect differences in visual processing and limited exposure to written Chinese during the preschool years.

The findings carry practical weight for educators. Rather than treating autistic children solely through the lens of deficit, the authors recommend building on their apparent strength in character recognition to expand vocabulary knowledge, using naturalistic teaching, peer activities, and digital tools that make language visually concrete. Because the study was cross-sectional and involved a relatively small sample of verbally fluent children in one region, the authors caution that causal conclusions cannot yet be drawn, and they call for longitudinal research including minimally verbal autistic children. Still, the work marks an important step toward literacy interventions tailored to autistic learners in non-alphabetic languages, where the road to reading is charted by strokes and radicals rather than letters and sounds.

Subject of Research: Emergent literacy development and its contribution to character recognition and oral vocabulary in Chinese-speaking kindergarteners with autism spectrum disorder.

Article Title: The Contribution of Emergent Literacy Skills to the Development of Character Recognition and Oral Vocabulary Knowledge Among Children With Autism Spectrum Disorder: The Case of Kindergarteners in China

Article References: Chen, Z., Kuo, L.-J., Dixon, L. Q., Han, C.-L., & Ma, Y. (2026). The Contribution of Emergent Literacy Skills to the Development of Character Recognition and Oral Vocabulary Knowledge Among Children With Autism Spectrum Disorder: The Case of Kindergarteners in China. Journal of Autism and Developmental Disorders. https://doi.org/10.1007/s10803-026-07514-x

Image Credits: AI Generated

DOI: 10.1007/s10803-026-07514-x

Keywords: autism spectrum disorder, emergent literacy, character recognition, oral vocabulary, phonological awareness, morphological awareness, orthographic awareness, Chinese language, kindergarten, rapid automatized naming, inhibitory control, working memory

The study’s premise rests on a well-established foundation in second language research. Vocabulary knowledge is multidimensional, encompassing word form, meaning, and use. In Mandarin Chinese, this means learners must simultaneously master pinyin romanization with accurate tone marks, semantic relationships including collocations and cultural connotations, and the syntactic patterns governing how words behave in sentences. Providing individualized, adaptive support across all these dimensions at once is a persistent challenge for classroom teachers, who rarely have the capacity to give every student immediate, personalized feedback. The researchers argued that ChatGPT’s conversational architecture—its ability to answer individualized questions, generate contextualized examples, and respond instantly—might fill precisely this gap.

The theoretical scaffolding for the intervention drew on several complementary frameworks. Scaffolding theory, rooted in Vygotsky’s work and Wood, Bruner, and Ross’s tutoring studies, describes contingent support that fades as learners gain autonomy. Long’s Interaction Hypothesis emphasizes that negotiating meaning through dialogue drives acquisition, while Swain’s Output Hypothesis holds that producing language forces learners to notice gaps and refine their knowledge. Craik and Lockhart’s Depth of Processing framework and Laufer and Hulstijn’s Involvement Load Hypothesis add that elaborately and cognitively processed material is retained more durably. ChatGPT-scaffolded instruction, the team reasoned, could activate all of these mechanisms at once: learners ask questions, negotiate meanings, generate sentences, and evaluate usage in an iterative loop.

Sixteen international undergraduates—seven Thai, eight Indonesian, and one Vietnamese student, aged 20 to 24 and proficient at HSK Levels 4 through 6—took part. They were drawn from two pre-existing Chinese language classes at a university in southeastern China, with eight students in each. One class received traditional teacher-directed vocabulary instruction, including explanation, guided reading, pronunciation correction, repetition, and dictation. The other used ChatGPT 3.5 under teacher guidance as a scaffold throughout each lesson, asking questions, requesting explanations, generating examples, composing phrases and sentences, and exploring contextual usage. Both groups were taught by the same experienced instructor, received identical instructional time of 45 minutes per lesson across 13 sessions, and studied the same 110 previously untaught target words from Lessons 2 through 7 of Boya Chinese Intermediate 1. Students typed Chinese characters using voice-to-text input on their mobile devices, since ChatGPT 3.5 itself offered no speech recognition or spoken output.

Vocabulary knowledge was assessed with researcher-developed pretests and posttests covering all 110 instructed items. Phonetic knowledge was measured through pinyin transcription with tonal accuracy, while a composite semantic-syntactic score averaged true/false meaning judgments against plausible distractor glosses with sentence-completion items requiring grammatical, meaningful word use. Two experienced instructors independently scored all assessments, achieving strong inter-rater agreement with intraclass correlation coefficients of 0.94 for phonetic and 0.91 for composite scores.

The quantitative pattern was suggestive. The ChatGPT-scaffolded class showed an observed mean gain of 80.38 points on the 110-point composite semantic-syntactic measure, compared with 58.13 points in the traditional class—a difference of 22.25 points, with an exact permutation test yielding p = .024. On phonetic knowledge, however, the classes were statistically indistinguishable, gaining 46.38 and 44.88 points respectively. An exact permutation test for phonetic gain returned p = .882. In interviews, six volunteers from the ChatGPT class described increased engagement, comprehensive explanations, rapid responses, and contextualized examples; one student noted feeling more comfortable asking ChatGPT questions without fear of embarrassment. Several volunteers, consistent with the quantitative pattern, said the tool was more helpful for meanings and usage than for pronunciation, and they flagged occasional inaccuracies and the inconvenience of VPN access.

Yet the researchers are unflinching about what these numbers cannot show, and that honesty is arguably the study’s most valuable contribution. Because instructional condition was completely confounded with class membership—one class per condition—no statistical model can separate a treatment effect from a class effect. The class-level indicator and the treatment indicator are perfectly collinear, leaving zero residual degrees of freedom for any significance test of the intervention itself. The students were also not randomly assigned, and the ChatGPT class began ahead on both measures at pretest, including a practically meaningful 13.25-point phonetic advantage. Student-level p values answer only how unusual the observed difference would be under random reallocation of these particular 16 students; they carry no information about whether the instructional approach produced it. Pre-existing differences in composition, prior instruction, peer dynamics, and motivation all remain competing explanations.

Measurement constraints add further caution. The instrument’s internal consistency and dimensional structure were never empirically established, since item-level responses were not retained after consensus scoring, and the semantic-syntactic composite cannot support separate conclusions about semantic versus syntactic development. The ChatGPT class’s posttest mean of 102.75 out of 110—with six of eight students scoring at least 105—signals a ceiling effect that destabilizes standardized effect sizes. Identical items at pretest and posttest may have produced practice effects, particularly for the guessable true/false semantic items, and the qualitative sample comprised only six self-selected volunteers who may have been positively predisposed toward the approach. The results therefore describe immediate performance on 110 instructed items, not retention, transfer, or broader lexical competence.

What the study does offer is a bounded but genuinely useful signal and a set of testable hypotheses. The convergence between the quantitative pattern—larger class-level change on meaning and usage, flat differences in pronunciation—and the interviewees’ independent perception that ChatGPT helped them understand word meanings better than pronunciation is theoretically coherent: text-based ChatGPT 3.5 provided no auditory modeling, so phonetic development plausibly depended on the teacher-led practice both classes received. The authors’ pedagogical implications are deliberately provisional: educators who adopt generative AI should match AI activities to the intended learning task, verify AI output, retain teacher oversight, and teach AI literacy so students can critically evaluate generated explanations. Future research, they argue, needs randomized controlled trials with larger samples, validated instruments with adequate posttest headroom, voice-enabled AI systems capable of real-time pronunciation feedback, systematic qualitative sampling across both conditions, and follow-up measures of long-term retention. In a field saturated with enthusiasm and thin on evidence, this small study models something rarer than a positive result: a template for how to test the AI-education hype honestly.

Subject of Research: An exploratory mixed-methods evaluation of ChatGPT-scaffolded instruction on second language Chinese vocabulary learning in two undergraduate classes.

Article Title: Evaluating an AI-scaffolded intervention for L2 vocabulary learning: affordances, constraints, and pedagogical implications

Article References: Lei, Q., Chen, Y., Chen, Y., Zhang, X., & Park, J. (2026). Evaluating an AI-scaffolded intervention for L2 vocabulary learning: affordances, constraints, and pedagogical implications. SN Social Sciences, 6(10), Article 461. https://doi.org/10.1007/s43545-026-01725-w

Image Credits: AI Generated

DOI: 10.1007/s43545-026-01725-w

Keywords: ChatGPT, second language learning, vocabulary acquisition, Chinese language, instructional scaffolding, generative AI, language education, pinyin tones, quasi-experimental design, semantic-syntactic knowledge, L2 pronunciation, educational technology

The study, conducted by Fei Song and Michael Dunn of the Centre for Biomedical Ethics at the National University of Singapore together with Zhenni Hu of Nanjing Normal University, examined 43 articles indexed in the Chinese Social Sciences Citation Index along with four policy reports. The analysis reveals that Chinese scholars have made substantial contributions to policy and governance discussions around trustworthy AI, but that these contributions remain conceptually scattered, with different disciplines approaching the question of trust from radically different angles. The researchers set out to synthesize this dispersed knowledge into a coherent conceptual account, and in doing so they arrived at a framework that treats trustworthiness not as a property of algorithms alone but as an emergent condition spanning technology, institutions, and human relationships.

The first layer of the framework concerns technical foundations. This is the layer most familiar to engineers and computer scientists, encompassing properties such as reliability, robustness, transparency, explainability, fairness, and safety. Chinese-language scholarship in this domain is extensive and spans a striking range of applications. Researchers have examined explainable learner models as the technical key to trustworthy personalized learning, explored blockchain-driven approaches to financial security intelligence, and investigated how algorithmic transparency in personalized recommendation systems shapes users’ perceptions of trustworthiness. Work on data annotation governance has highlighted backstage risks in the labor-intensive processes that feed machine learning systems, while archival science perspectives have been brought to bear on the question of algorithm provenance, asking how the origins and histories of algorithms can be documented and verified. These contributions demonstrate that the technical layer of trustworthiness is not merely a matter of model architecture but extends deep into the data pipelines, documentation practices, and supply chains that underpin AI systems.

The second layer addresses governance and regulatory arrangements. Here the Chinese literature is particularly distinctive, reflecting the country’s active policy discourse on AI regulation. Scholars have assessed pathways for trustworthy AI legislation, explored innovation-friendly regulatory designs that balance safety with technological development, and analyzed governance practices in terms of models, actors, objects, and tools. Policy documents play an important role in this layer, including the Ministry of Science and Technology’s 2019 governance principles for developing responsible AI, a white paper on trustworthy artificial intelligence produced by the China Academy of Information and Communications Technology, and more recent industry ecosystem reports and governance surveys. The review shows that Chinese scholars have been grappling with the same fundamental questions that animate regulators in Europe and the United States: how to translate abstract ethical principles into enforceable rules, how to assign liability when AI systems fail, and how to design oversight mechanisms that keep pace with rapidly evolving technology. One striking example is legal scholarship on liability when human–machine co-piloting fails, examining the negligence of safety operators through the lens of the principle of reliance.

The third layer, and the one the authors argue is most often neglected in purely technical accounts, concerns the conditions of human–AI interaction under which trust is formed and calibrated. The Chinese-language literature here is remarkably rich, drawing on empirical psychology, communication studies, and philosophy. Researchers have studied trust in automated vehicles, safety trust in intelligent domestic robots, and human–AI mutual trust in the era of artificial general intelligence. A dual-pathway model of trust calibration distinguishes between trust dampening and trust promoting mechanisms, offering a nuanced picture of how users adjust their reliance on machines. Other studies examine how anthropomorphism affects trust in AI agents, with a meta-analysis synthesizing evidence on how humanlike features in AI systems influence trust and how contextual factors moderate that effect. Work on large language models has explored how machine hallucinations impair human–machine trust and what recalibration mechanisms might restore it, a question of obvious urgency as chatbots and AI assistants become ubiquitous.

What emerges from this synthesis is the insight that these three layers are not independent. An AI system can be technically excellent, with high accuracy and robust performance, and still fail to be trustworthy if the governance arrangements around it are inadequate or if the conditions of human interaction systematically miscalibrate trust. Conversely, strong institutions and well-designed interaction conditions cannot compensate for fundamentally unreliable technology. The authors’ central claim is that an AI system is trustworthy only if it meets the requirements at all three layers simultaneously. This conjunctive structure has practical implications: it means that trustworthiness cannot be certified by any single test, audit, or label, but requires integrated assessment across the full stack from model internals to regulatory environments to the design of the encounters between humans and machines.

The framework also speaks to a long-running philosophical debate about whether trust in AI is even coherent. Some Western philosophers argue that trust, properly understood, requires mutual expectations and moral agency that machines cannot possess, and that what we call trust in AI is really a form of reliance or confidence. Others contend that it is entirely possible to trust medical AI systems and that denying this misunderstands the functional role of trust in clinical decision-making. The Chinese literature adds distinctive voices to this debate, with scholars asking directly whether artificial intelligence can serve as a trustee and defending the notion of trustworthy AI from a logical perspective. The review also engages with a network account of trustworthy AI, which holds that trustworthiness is distributed across the networks of actors, artifacts, and institutions surrounding an AI system rather than residing in the system alone. The three-layer framework can be read as a structured elaboration of this network idea, specifying the technical, institutional, and interactional nodes that such a network must contain.

The methodological significance of the study lies partly in its source base. Scholarship published in Chinese remains largely absent from mainstream academic debates, a gap reinforced by publication incentives that push Chinese humanities and social science researchers toward international English-language venues. By drawing on the Chinese Social Sciences Citation Index, the authors accessed a body of work that includes empirical studies using artificial neural networks and Q methodology to map the dimensions of human–AI trust, philosophical analyses grounded in Confucian traditions, and domain-specific investigations in education, journalism, healthcare, and finance. The result is a more globally representative picture of what the research community actually knows about trustworthy AI. The authors also note distinctive features of the Chinese context, including a practice-oriented view of science and technology that shapes how trustworthiness is framed, and a governance culture in which state-led principles, industry white papers, and academic analysis interact closely.

The study concludes by identifying three directions for future research. First, the conceptual fragmentation of the field calls for continued theoretical work to integrate technical, governance, and interactional perspectives into unified accounts of trustworthiness. Second, the rapid emergence of generative AI and large language models creates new empirical challenges, from hallucination-induced trust erosion to the trust dynamics of human–machine dialogue, that demand updated frameworks. Third, cross-linguistic and cross-cultural comparative research is needed to test whether the three-layer structure holds across different scholarly traditions and regulatory cultures, or whether trustworthy AI means genuinely different things in different contexts. As AI systems increasingly mediate news consumption, medical diagnosis, education, and financial services, the stakes of getting trustworthiness right could hardly be higher. This review makes the case that answering the question well requires listening to conversations that have been happening in Chinese all along, and that the resulting framework, grounded in arguments advanced in the Chinese context, offers a more robust conceptual account of how disparate claims about trust fit together than either technical or governance approaches alone can provide.

Subject of Research: Trustworthy AI beyond the technical: a three-layer framework informed by Chinese-language scholarship

Article Title: Trustworthy AI beyond the technical: a three-layer framework informed by Chinese-language scholarship

Article References: Song, F., Hu, Z., & Dunn, M. (2026). Trustworthy AI beyond the technical: a three-layer framework informed by Chinese-language scholarship. AI & SOCIETY. https://doi.org/10.1007/s00146-026-03366-2

Image Credits: AI Generated

DOI: 10.1007/s00146-026-03366-2

Keywords: Trustworthy, beyond, technical, three-layer, framework, informed, Chinese-language, scholarship, scientific research