This Systematic Literature Review (SLR) investigates how Artificial Intelligence (AI) enhances early literacy

learning and examines its pedagogical, ethical, and cultural implications within early childhood education.

Guided by the PRISMA 2020 framework, 18 peer-reviewed studies published between 2020 and 2025 in Scopus

and Web of Science databases were systematically reviewed. The synthesis highlights key AI applications, such

as adaptive storytelling platforms, robot-assisted literacy tools, and generative text systems that are able to

support vocabulary growth, reading comprehension, and active engagement among young learners. Findings

indicate that AI fosters personalized, inclusive, and culturally responsive literacy instruction while transforming

Artificial Intelligence (AI) is increasingly shaping the global landscape of education by enabling personalized,

data-driven, and adaptive learning experiences. While AI adoption is more established in higher and secondary

education, its integration into early childhood education (ECE) represents a promising yet under-explored

frontier (Kewalramani et al., 2021). The preschool years are foundational for literacy development, during which

language, communication, and cognitive skills form the basis for lifelong learning. Integrating AI into early

literacy instruction offers opportunities to enhance these skills through adaptive storytelling, intelligent feedback,

and multimodal learning environments (Azhar et al., 2025).

phonemic awareness through speech recognition, conversational agents, and robot-assisted learning (Al-Bogami

instruction. By examining studies indexed in Scopus and Web of Science, this review contributes to

understanding how AI can be harnessed to promote equitable, developmentally appropriate, and culturally

responsive early learning. The findings are expected to guide educators, researchers, and policymakers in

designing future AI-infused literacy interventions that strengthen both teaching practice and young children’s

holistic development. The objectives of this study are:

1. To identify and synthesize how Artificial Intelligence (AI) applications enhance early literacy learning

2. To analyze the pedagogical, ethical, and cultural implications of AI integration in early literacy.

capabilities, including the use of Boolean operators, and transparent, replicable processes that maintain the

integrity of the study (Gusenbauer & Haddaway, 2020). The use of these high-quality databases ensures the

accuracy and reliability of conducting the literature review (Zhao, 2014). The review followed the PRISMA

guidelines to evaluate and select articles. The process included identification, screening, eligibility assessment,

and inclusion. The PRISMA flow chart used in this study (Figure 1) is an adaptation of the original chart

developed by Moher et al. (2009) and subsequently modified by Page et al. (2021).

This SLR was conducted by using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-

Analysis). The advantages of PRISMA are the elements of transparency, consistency, and high standards in the

preparation of qualitative study reports through certain processes (Flemming et al. 2019). This systematic

Table 1 Search strings used in the databases

adapted to the context of this study. Each of the 18 articles was evaluated against 15 quality assessment (QA)

questions covering research objectives, methodology, data sources, analysis, limitations, and practical

recommendations related to AI, early literacy, and early childhood education. The questions are:

Table 3:Article’s quality assessment (Kitchenham, 2004)

Collectively, these findings suggest that AI functions not merely as a technological tool but as a pedagogical

partner that supports teachers in designing creative, equitable, and developmentally appropriate literacy

mediator who safeguards privacy, ensures responsible AI use, and mitigates algorithmic bias. Ethical reflection

Artificial intelligence (AI) is reshaping the research and policy landscape of early childhood education by

providing data-driven insights and analytical tools that advance equitable literacy development across diverse

contexts. Through machine learning and natural language processing (NLP), policymakers and researchers can

systematically evaluate early childhood legislation and identify the structural factors that predict the success of

literacy-related policies. Such computational approaches reveal how investments in teacher training, curriculum

reform, and family engagement influence literacy outcomes, thereby promoting evidence-based governance and

informed decision-making (Park & Hassairi, 2021). At the curriculum level, AI supports evidence-based

innovation by enabling educators and researchers to design developmentally appropriate and inclusive literacy

instrument of educational reform and a catalyst for social equity, bridging the gap between policy, practice, and

pedagogy. By leveraging AI in research, curriculum design, and policymaking, early childhood education

systems can move toward more just, inclusive, and data-informed literacy environments that ensure all children

have equitable access to foundational learning opportunities.

The integration of Artificial Intelligence (AI) in early childhood education marks a transformative shift in

teachers’ professional identity from content deliverers to designers, facilitators and evaluators of learning. AI-

driven platforms empower educators to personalize literacy instruction, analyze learner data and create adaptive

teachers’ professional autonomy by enabling them to interpret real-time analytics, design responsive literacy

However, despite these opportunities, AI integration introduces new professional challenges that may

educators must balance technological efficiency with pedagogical discernment using AI as a reflective partner

rather than a prescriptive authority. Developing AI-competent teachers, therefore, requires not only technical

training but also ethical awareness and critical pedagogy that uphold human-centered teaching values (Lakhe

Shrestha et al., 2025).

Artificial Intelligence (AI) holds significant promise for inclusive literacy development when designed with

cultural responsiveness in mind. AI-powered storytelling systems can embed local languages, narratives and

values, thereby strengthening children’s cultural identity and sense of belonging. For example, AI systems that

backgrounds and support literacy engagement. As Yang et al. (2022) argue, early AI curriculum models should

Nevertheless, serious risks accompany AI adoption in literacy environments, especially where access and local

adaptation are weak. A critical concern is the digital divide: children in low-income, rural, or marginalized

communities may lack access to high-quality devices, stable connectivity, or AI-enabled tools, which

exacerbates existing educational disparities. In fact, the growing gap between those who can harness AI and

those who cannot may create a “new digital divide” as AI-enhanced learning becomes the norm for advantaged

groups. In addition, many AI systems are designed with Western pedagogical assumptions and standardized

language corpora, risking cultural homogenization in early literacy content. Without intentional localization,

these systems may dilute or override indigenous pedagogies, linguistic diversity, and community values. Thus,

balancing global AI design with local educational philosophies requires participatory co-design processes

involving local educators, families and communities (Oyetade & Zuva, 2025).

foster an AI ecosystem that upholds equity, cultural inclusivity, and human-centered pedagogy in early childhood

education.

The findings of this study hold significant implications for advancing SDG 4 (Quality Education) through

inclusive and equitable AI integration in early childhood settings. Artificial Intelligence (AI) can empower

teachers to adopt data-informed, reflective, and culturally responsive literacy practices that honor the linguistic

should develop digital ethics, critical thinking, and adaptive pedagogical skills to ensure that AI use remains

responsible, transparent, and developmentally appropriate. Continuous professional development programs must

digital infrastructure across preschools. When guided by child-centered and culturally grounded principles, AI

studies. Many rely on small, context-specific samples or Western-centric datasets, creating potential publication

and developmentally appropriate literacy education in early childhood contexts.

children’s language, cognitive, and socio-emotional development over time. Researchers should also examine

the evolving roles of teachers in AI-mediated classrooms, focusing on ways to strengthen AI literacy, critical

thinking, and reflective practice. In addition, cross-cultural comparative studies can provide valuable insights

into how local contexts affect the equity and inclusivity of AI-based literacy interventions. Collaboration among

educators, technologists and policymakers will be crucial to ensure that AI applications align with early

childhood development principles and global goals for educational equity. Ultimately, future studies should

This systematic literature review concludes that Artificial Intelligence (AI) has transformative potential in

enhancing early literacy within early childhood education. The findings demonstrate that AI can serve as a

pedagogical partner, empowering educators to design adaptive, data-driven, and culturally responsive learning

environments. However, realizing this potential depends on educators’ AI literacy, equitable access to

technology, and ethical implementation. While AI promotes inclusivity and creativity in literacy instruction,

challenges such as digital inequality, cultural homogenization, and teacher dependency must be addressed

through targeted policies and professional development. It can be concluded that AI should not replace the human

element of teaching but rather augment it, fostering reflective, empathetic, and child-centered pedagogy. Future

research and practice must continue to explore frameworks that ensure AI integration aligns with developmental