¹ Primary Education, State University of Yogyakarta, Yogyakarta, Special Region of Yogyakarta, Indonesia
² Primary School Teacher Education, PGRI Ronggolawe Tuban University, East Java, Indonesia
³ Primary Education, Universitas Negeri Yogyakarta, Yogyakarta, Special Region of Yogyakarta, Indonesia
⁴ Department of Indonesian Language Education, State University of Yogyakarta, Yogyakarta, Special Region of Yogyakarta, Indonesia
⁵ Primary Madrasah Teacher Education, Hasyim Asy'ari Tebuireng University, Jombang, Indonesia
⁶ Technical and Vocational Education, State University of Yogyakarta Graduate School, Yogyakarta, Special Region of Yogyakarta, Indonesia
⁷ Educational Research and Evaluation, State University of Yogyakarta Graduate School, Yogyakarta, Special Region of Yogyakarta, Indonesia
⁸ English Education, Darussalam Gontor University, East Java, Indonesia
⁹ Master of Mathematics Education, State University of Yogyakarta, Yogyakarta, Special Region of Yogyakarta, Indonesia
¹⁰ Teacher Education Program, Musamus University, South Papua, Indonesia

Arik Umi Pujiastuti
Roles: Conceptualization, Data Curation, Funding Acquisition, Methodology, Project Administration, Writing – Original Draft Preparation

Ali Mustadi
Roles: Formal Analysis, Investigation, Supervision, Writing – Review & Editing

Kastam Syamsi
Roles: Supervision, Validation, Writing – Review & Editing

Evita Widiyati
Roles: Formal Analysis, Investigation, Methodology, Resources, Software

Alfiyandri Alfiyandri
Roles: Formal Analysis, Investigation, Resources, Software, Supervision

Rafiuddin Rafiuddin
Roles: Formal Analysis, Methodology, Resources, Supervision, Visualization

Ahmad Niayatulloh
Roles: Investigation, Software, Supervision, Validation, Visualization

Firstian Angger Aprilio
Roles: Resources, Software, Visualization, Writing – Original Draft Preparation

Dewi Puji Rahayu
Roles: Formal Analysis, Investigation, Methodology, Supervision, Validation, Writing – Review & Editing

Educational ecosystem theory conceptualizes learning as a dynamic interaction among learners, teachers, families, technologies, institutions, and wider social environments. Rather than treating learning as an isolated classroom activity, ecosystem perspectives emphasize the interconnected relationships that collectively influence educational development and learner outcomes. In contemporary educational contexts, artificial intelligence (AI) has emerged as a critical component of these ecosystems by facilitating adaptive learning, personalized instruction, intelligent feedback, and data-informed decision-making processes (Chen et al., 2024; Tariq, 2026). The integration of AI into educational ecosystems has significantly expanded opportunities for creating learner-centered and future-oriented learning environments. Recent studies indicate that AI-supported educational systems can enhance instructional effectiveness, improve learner engagement, and support the development of complex competencies required in rapidly changing societies (Zafeer et al., 2025; Fatmasari, 2026). Within elementary education, AI technologies increasingly function as cognitive, instructional, and collaborative supports that extend learning beyond traditional classroom boundaries and enable more personalized educational experiences (Erita et al., 2025; Chatzichristofis et al., 2025). From an ecosystem perspective, literacy development is no longer limited to reading and writing proficiency. Instead, literacy is understood as a multidimensional process supported by interactions among digital resources, AI technologies, learning communities, educators, and families. Quan et al. (2026) demonstrated that AI-integrated home literacy environments significantly influence children’s literacy development, suggesting that literacy ecosystems should be viewed as interconnected learning environments extending across school and home contexts. Consequently, AI-supported literacy ecosystems may provide an important foundation for cultivating broader competencies necessary for future learning and workforce participation.

The increasing influence of automation, digital transformation, and intelligent technologies has intensified global interest in future skills development. Future skills refer to transferable competencies that enable individuals to adapt successfully to technological, social, and economic change. These competencies commonly include critical thinking, creativity, communication, collaboration, adaptability, problem-solving, and digital competence (Burns et al., 2026; Arora et al., 2025). Recent scholarship suggests that AI-enhanced educational environments can play a substantial role in developing future skills by providing personalized learning pathways, intelligent scaffolding, and authentic problem-solving opportunities (Sagheer et al., 2025; Alé et al., 2025). AI-supported literacy activities encourage learners to engage in inquiry, knowledge construction, collaborative learning, and information evaluation, all of which are recognized as essential competencies for future workforce participation (Simos et al., 2026). Furthermore, AI literacy itself has emerged as a critical dimension of future skills. Studies have emphasized that learners must not only use AI tools effectively but also understand their ethical, social, and cognitive implications (Chakraburty et al., 2025; Zhou et al., 2025). AI-supported literacy ecosystems provide opportunities for learners to develop these competencies through meaningful interactions with intelligent technologies embedded within everyday learning experiences. Therefore, it is reasonable to assume that stronger AI-supported literacy ecosystems contribute positively to future skills development.

Lifelong learning has become a central objective of contemporary education systems due to the accelerating pace of technological and societal change. Lifelong learning competence refers to an individual’s capacity and disposition to continuously acquire, adapt, and apply knowledge throughout life. This competence encompasses self-regulated learning, learning autonomy, adaptability, curiosity, and continuous professional growth (Syarifuddin et al., 2025; Akpınar et al., 2025; Uğur, 2026). AI-supported learning environments offer unique opportunities to strengthen lifelong learning dispositions by promoting personalized learning experiences and encouraging learners to assume greater responsibility for their own learning processes. Through adaptive feedback, learning analytics, and intelligent support systems, learners are increasingly empowered to monitor their progress, identify learning needs, and pursue individualized learning goals (Taheri Hosseinkhani, 2025; Ergunova et al., 2026). Within literacy ecosystems, these processes may begin during elementary education when learners develop foundational habits associated with inquiry, reflection, and independent learning. AI-supported literacy ecosystems therefore represent more than instructional environments; they function as developmental infrastructures that foster the dispositions necessary for lifelong learning.

Future skills and lifelong learning competence are conceptually interconnected. While future skills provide the capabilities required to navigate complex and uncertain environments, lifelong learning competence enables individuals to continuously update and expand those capabilities throughout their lives. Research consistently suggests that critical thinking, creativity, adaptability, and digital competence contribute significantly to lifelong learning readiness (Sinclair, 2026; Burns et al., 2026). AI-enhanced learning environments have been shown to facilitate the development of these competencies through collaborative learning, problem-solving activities, and reflective learning processes (Okada et al., 2025). As learners strengthen their future-oriented competencies, they become more capable of engaging in self-directed learning and adapting to emerging educational and professional challenges. Accordingly, future skills development is expected to function as an important mechanism through which AI-supported literacy ecosystems contribute to lifelong learning competence.

Vocational readiness refers to the preparedness of learners to participate effectively in future educational, professional, and workforce environments. Contemporary perspectives increasingly recognize that vocational readiness extends beyond technical competencies to include adaptability, learning agility, communication skills, problem-solving abilities, and continuous learning orientation (Wahrini et al., 2026; Burns et al., 2026). As technological transformation continues to reshape labor markets, lifelong learning competence has become a crucial determinant of long-term employability and workforce resilience. Individuals who demonstrate strong lifelong learning dispositions are generally better equipped to acquire new competencies, respond to changing occupational demands, and sustain professional development across diverse career pathways (Akpınar et al., 2025). Although vocational readiness is commonly associated with secondary or vocational education, recent scholarship suggests that its foundations begin to emerge during elementary education through the development of transferable competencies and positive learning dispositions. Consequently, lifelong learning competence may serve as an important pathway connecting elementary educational experiences with future workforce preparedness.

Future workforce demands increasingly require individuals who can collaborate effectively, solve complex problems, utilize emerging technologies, and adapt to continuous change. These characteristics align closely with the competencies typically categorized as future skills (Arora et al., 2025). Research indicates that future skills significantly contribute to workforce preparedness because they enable learners to navigate dynamic employment environments and engage in continuous professional development (Sagheer et al., 2025). Within AI-supported educational environments, future skills may therefore represent a direct mechanism through which learners develop vocational readiness from an early age.

Educational ecosystem theory suggests that learning outcomes emerge through interconnected developmental pathways rather than direct linear relationships. Accordingly, the influence of AI-supported literacy ecosystems on vocational readiness may operate indirectly through future skills development and lifelong learning competence. AI-supported literacy ecosystems create opportunities for learners to engage in collaborative inquiry, adaptive learning, critical reflection, and digital knowledge construction. These experiences contribute to the development of future skills, which subsequently strengthen lifelong learning competence and prepare learners for future vocational participation (Okada et al., 2025; Chen et al., 2026). Therefore, future skills development and lifelong learning competence are proposed as sequential mediators linking AI-supported literacy ecosystems with vocational readiness.

The study was conducted in six elementary schools that had implemented AI-supported literacy initiatives as part of their digital learning programs. These initiatives included AI-assisted reading applications, adaptive literacy platforms, intelligent storytelling systems, AI-supported feedback tools, and collaborative digital literacy activities. The quantitative phase involved 368 students from Grades 4–6. These grade levels were selected because students had already developed foundational literacy skills and sufficient digital experience to engage meaningfully with AI-supported learning environments. Participants were selected using stratified random sampling to ensure proportional representation across schools, grade levels, and gender. For the qualitative phase, 24 participants were purposively selected using maximum variation sampling based on quantitative response patterns. The participants consisted of 12 students, 6 classroom teachers, 3 school principals, and 3 parents. This approach enabled the collection of diverse perspectives regarding the implementation and perceived impacts of AI-supported literacy ecosystems.

The conceptual framework positions AI-Supported Literacy Ecosystems (AILE) as the primary antecedent influencing Future Skills Development (FSD), Lifelong Learning Competence (LLC), and Vocational Readiness (VR). The framework is grounded in educational ecosystem theory, which conceptualizes learning as an interaction among learners, educators, technologies, families, and learning environments.

Four latent constructs were examined in this study:

A structured questionnaire was developed based on an extensive review of literature related to AI-supported learning, literacy ecosystems, future skills, lifelong learning, and workforce preparedness. The instrument consisted of 42 items measured using a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). Prior to the main study, the instrument was reviewed by five experts in educational technology, literacy education, curriculum studies, and artificial intelligence in education. A pilot study involving 52 students was subsequently conducted to evaluate item clarity, reliability, and construct validity. Table 1 presents the classification and operationalization of the study variables.

Data collection and analysis were conducted sequentially following the explanatory mixed-methods design. During the quantitative phase, data were collected from 368 students using the structured questionnaire administered during scheduled school sessions under researcher supervision. Participation was voluntary, and informed consent was obtained from school administrators, teachers, parents, and students prior to data collection. The quantitative phase aimed to test the proposed relationships among AI-Supported Literacy Ecosystems, Future Skills Development, Lifelong Learning Competence, and Vocational Readiness.

Quantitative data were analyzed using SmartPLS 4.0 following a two-stage SEM-PLS procedure. First, the measurement model was evaluated through indicator loadings, Cronbach’s alpha, Composite Reliability (CR), Average Variance Extracted (AVE), and Heterotrait–Monotrait Ratio (HTMT) to establish reliability and validity. Second, the structural model was assessed using path coefficients (β), coefficient of determination (R²), effect size (f²), predictive relevance (Q²), and bootstrapping with 5,000 resamples to examine direct, indirect, and serial mediation effects among the study constructs.

Following quantitative analysis, the qualitative phase was conducted to provide explanatory insights into the statistical findings. Twenty-four participants were purposively selected using maximum variation sampling based on quantitative response patterns. Semi-structured interviews and focus group discussions were conducted to explore participants’ experiences with AI-supported literacy practices, future skills development, lifelong learning behaviors, and vocational awareness. All interviews were audio-recorded, transcribed verbatim, and anonymized prior to analysis.

Qualitative data were analyzed using thematic analysis involving familiarization, coding, categorization, theme generation, and interpretive synthesis. Finally, qualitative findings were integrated with quantitative results using the connecting approach proposed by Draucker et al. (2020), enabling a comprehensive understanding of how AI-supported literacy ecosystems contribute to future skills development, lifelong learning competence, and vocational readiness.

Ethical approval was obtained from the Research Ethics Committee of Universitas Negeri Yogyakarta (Approval No. 612/UN34.12/PP/Pen/2026) prior to the commencement of the study. Participation was voluntary, and verbal informed consent was obtained from school administrators, teachers, and parents or legal guardians of participating students before data collection. In addition, verbal assent was obtained from all participating students. The use of verbal informed consent was approved by the Research Ethics Committee because the study involved minimal risk and was conducted within routine educational activities. Participants were informed about the objectives of the study, confidentiality procedures, the voluntary nature of participation, and their right to withdraw at any time without penalty. All collected data were anonymized and securely stored in accordance with international ethical standards for educational research involving minors.

Before testing the proposed hypotheses, the measurement model was evaluated to establish the reliability and validity of the latent constructs. As reported in Table 2, all indicator loadings exceeded the recommended threshold of 0.70, ranging from 0.812 to 0.892. These results indicate that all indicators contributed substantially to their respective constructs and adequately captured the conceptual domains under investigation.

A closer examination of Table 2 reveals an important pattern. Within the AI-Supported Literacy Ecosystem (AILE) construct, Adaptive Learning Pathways (λ = 0.892) and Digital Learning Tools (λ = 0.878) emerged as the strongest indicators. This suggests that the educational value of AI-supported literacy ecosystems is primarily derived from their ability to personalize learning experiences and provide adaptive instructional support rather than merely increasing access to digital technologies. Similarly, the strongest indicators of Future Skills Development (FSD) were Problem Solving (λ = 0.892), Communication (λ = 0.888), and Critical Thinking (λ = 0.876). This pattern indicates that AI-supported literacy practices are associated most strongly with higher-order competencies required in future educational and occupational contexts. Within Lifelong Learning Competence (LLC), Self-Directed Learning emerged as the dominant dimension (λ = 0.891), whereas Workforce Adaptability (λ = 0.892) represented the strongest manifestation of Vocational Readiness (VR).

The reliability and convergent validity results shown in Table 3 further support the robustness of the measurement model. Composite Reliability values ranged from 0.923 to 0.945, substantially exceeding the recommended threshold of 0.70. Likewise, AVE values ranged from 0.712 to 0.773, confirming that each construct explained more than 50% of the variance in its indicators.

Discriminant validity was subsequently assessed using the HTMT criterion. As shown in Table 4, all HTMT values remained below the conservative threshold of 0.85, demonstrating that the four constructs captured empirically distinct dimensions of AI-supported educational development.

Collectively, the findings presented in Tables 2–4 demonstrate satisfactory reliability, convergent validity, and discriminant validity, supporting the adequacy of the measurement model for structural analysis.

The structural model was evaluated to examine the proposed relationships among AI-Supported Literacy Ecosystems, Future Skills Development, Lifelong Learning Competence, and Vocational Readiness. The results are presented in Table 5. The strongest relationship identified in the model was the effect of AI-Supported Literacy Ecosystems on Future Skills Development (β = 0.687, t = 12.456, p < 0.001). This finding suggests that AI-supported literacy environments function as significant developmental platforms for nurturing critical thinking, communication, collaboration, creativity, and problem-solving skills. The magnitude of this relationship indicates that future skills development constitutes the most immediate educational outcome of AI-supported literacy ecosystem implementation.

AILE also demonstrated a substantial influence on Lifelong Learning Competence (β = 0.534, t = 9.234, p < 0.001), suggesting that adaptive and personalized literacy experiences contribute to the emergence of self-directed learning behaviours and learning autonomy. This finding indicates that AI-supported literacy ecosystems not only improve current learning experiences but also shape long-term learning dispositions. Future Skills Development emerged as a critical intermediary mechanism within the model. FSD significantly predicted Lifelong Learning Competence (β = 0.423, t = 7.891, p < 0.001) and Vocational Readiness (β = 0.456, t = 8.567, p < 0.001). Notably, the effect of FSD on Vocational Readiness was stronger than the direct effect of AILE on Vocational Readiness (β = 0.234), suggesting that much of the influence of AI-supported literacy ecosystems is transmitted through the development of future-oriented competencies. Similarly, Lifelong Learning Competence significantly predicted Vocational Readiness (β = 0.389, t = 6.723, p < 0.001). This result reinforces the argument that vocational readiness in elementary education is not solely a function of occupational awareness but is fundamentally shaped by learners’ capacity for continuous learning and adaptation.

The explanatory power of the model was substantial. As reported in Table 6, AILE explained 47.2% of the variance in Future Skills Development. Together, AILE and FSD explained 61.4% of the variance in Lifelong Learning Competence. Most importantly, AILE, FSD, and LLC collectively explained 69.8% of the variance in Vocational Readiness.

These results indicate that vocational readiness emerges through a developmental chain in which AI-supported literacy ecosystems foster future skills and lifelong learning competence, which subsequently shape learners’ readiness for future educational and occupational participation.

The qualitative phase was conducted to explain the mechanisms underlying the structural relationships identified in the SEM-PLS analysis. Five overarching themes emerged from the interviews.

Participants consistently emphasized the importance of adaptive learning pathways and personalized instructional support. These findings provide contextual explanation for the strong relationship between AILE and FSD identified in Table 5.

One teacher explained:

“Since adopting the AI literacy platform, I have observed substantial improvement among struggling readers. Students who previously found reading difficult are now able to progress because the system provides exercises tailored to their individual needs.” (Teacher A).

Similarly, another participant highlighted the value of immediate feedback mechanisms:

“The AI literacy assistant provides immediate feedback. When students make pronunciation errors, the system detects and corrects them instantly, allowing them to improve without waiting for teacher intervention.” (Teacher B).

Participants consistently described improvements in communication, creativity, collaboration, and problem-solving abilities. These observations support the strong path coefficient linking AILE and FSD.

As one teacher noted:

“Students have become more willing to experiment with new ideas in their writing. The AI writing assistant provides prompts that stimulate imagination and encourage creative exploration.” (Teacher D).

Another participant emphasized collaborative learning benefits:

“Students learn to share ideas, provide peer feedback, and work together on digital writing projects. These experiences have significantly improved their collaboration skills.” (Teacher E).

The interviews further revealed that AI-supported literacy environments encouraged self-regulation, learning autonomy, and independent inquiry.

One participant explained:

“The personalised learning pathways allow each student to progress at their own pace. Over time, students become more responsible for monitoring and managing their own learning.” (Teacher C).

These findings provide explanatory support for the significant relationship between AILE and LLC and for the influence of FSD on LLC observed in the quantitative analysis.

Despite the positive outcomes, participants reported several barriers including digital inequality, infrastructure limitations, teacher preparedness, and concerns regarding excessive dependence on AI.

For example:

“There remains a noticeable digital divide. Students from more advantaged families have access to technology at home, while others face significant limitations.” (Teacher A).

Similarly:

“Some students immediately consult the AI before attempting to think independently. This raises questions about the balance between technological support and autonomous reasoning.” (Teacher B).

These findings suggest that the educational value of AI-supported literacy ecosystems is contingent upon equitable access, adequate infrastructure, and responsible pedagogical implementation.

To generate comprehensive interpretations, quantitative and qualitative findings were integrated using a meta-inference approach. The integration results are presented in Table 7.

Taken together, the quantitative and qualitative findings converge on a central conclusion: AI-supported literacy ecosystems operate not merely as technological interventions but as developmental infrastructures that shape learners’ future-oriented trajectories. The strongest developmental pathway identified in the study indicates that AI-supported literacy ecosystems enhance future skills development, which subsequently strengthens lifelong learning competence and ultimately contributes to vocational readiness. At the same time, qualitative evidence suggests that the sustainability of these outcomes depends on addressing issues related to digital equity, infrastructure readiness, teacher capacity, and responsible AI integration.

The strongest relationship identified in the model was the effect of AI-Supported Literacy Ecosystems on Future Skills Development (β = 0.687). This finding suggests that AI-enhanced literacy environments provide meaningful opportunities for learners to develop critical thinking, communication, collaboration, creativity, problem-solving, and digital literacy competencies. The qualitative findings further indicate that adaptive learning pathways, personalized feedback, and collaborative digital activities serve as important mechanisms through which these competencies are cultivated. This result extends existing AI-in-education research by demonstrating that literacy ecosystems should not be viewed solely as tools for improving literacy achievement but as broader developmental platforms that nurture future-oriented competencies.

The significant effects of AI-Supported Literacy Ecosystems on Lifelong Learning Competence (β = 0.534) and Future Skills Development on Lifelong Learning Competence (β = 0.423) indicate that AI-supported literacy experiences contribute to the development of self-directed learning, learning autonomy, motivation, and reflective learning practices. Interview findings revealed that students became more independent in seeking information, managing their learning progress, and exploring additional learning resources. These findings suggest that the foundations of lifelong learning begin to emerge during elementary education when learners are provided with adaptive and personalized learning experiences.

Both Future Skills Development (β = 0.456) and Lifelong Learning Competence (β = 0.389) significantly influenced Vocational Readiness, while the direct effect of AI-Supported Literacy Ecosystems on Vocational Readiness was comparatively smaller (β = 0.234). This pattern indicates that vocational readiness develops primarily through the acquisition of future skills and lifelong learning dispositions rather than through technology exposure alone. The findings suggest that vocational readiness should be viewed as a long-term developmental process that begins in elementary education through the cultivation of transferable competencies, adaptability, and future-oriented thinking.

The study contributes to educational ecosystem theory by demonstrating that AI-supported literacy environments operate as developmental ecosystems that simultaneously foster future skills, lifelong learning competence, and vocational readiness. It also integrates four domains that are often examined separately: literacy ecosystems, future skills, lifelong learning, and vocational development.

From a practical perspective, the findings highlight the importance of designing AI-supported literacy ecosystems that emphasize personalization, collaborative learning, and learner autonomy. However, the qualitative findings also reveal persistent challenges related to digital inequality, infrastructure limitations, teacher preparedness, and concerns regarding excessive dependence on AI. Therefore, maximizing the benefits of AI-supported literacy ecosystems requires not only technological innovation but also equitable access, teacher professional development, and responsible AI implementation.

Despite these contributions, several limitations should be acknowledged. The study was conducted within a specific educational context, which may limit the generalizability of the findings. In addition, the cross-sectional design does not allow examination of long-term developmental changes. Future research should employ longitudinal and cross-cultural designs to investigate how AI-supported literacy ecosystems influence learner development over time and across diverse educational settings.

Overall, the study highlights the potential of AI-supported literacy ecosystems to transform elementary education from a literacy-focused intervention into a future-oriented developmental ecosystem that supports lifelong learning and vocational readiness in the age of artificial intelligence.

The authors gratefully acknowledge the financial and institutional support provided by the Indonesian Education Scholarship (BPI), Doctoral Scholarship Program for Indonesian Lecturers (PDDI), Center for Higher Education Funding and Assessment (PPAPT), Ministry of Higher Education, Science and Technology of Republic Indonesia, and Indonesian Endowment Fund for Education (LPDP).

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