AI literacy in a cultural context: essential components for generation Z

Gen Z (born between 1995 and 2012) exhibits distinct attributes shaped by information technologies and diverse cultural contexts. Understanding these traits is essential for educators seeking to engage this cohort effectively (Shorey, Chan, Rajendran, & Ang, 2021; Park & Kim, 2024); The influx of Gen Z students, alongside rapid advancements in AI, has prompted a critical reevaluation of traditional pedagogical approaches (Chen & Lin, 2024; Kumar & Mamgain, 2023); Bridging the gap between their expectations and institutional offerings requires exploring their perceptions and concerns to foster collaboration and improve learning experiences.

Comparative analyses reveal pronounced differences between Gen Z and earlier generations in educational preferences, learning styles, technology adoption and communication; These disparities arise from distinct historical, economic, social and technological influences (Hernandez de Menendez et al., 2020); Gen Z's pervasive digital access underpins their learning behaviors and highlights the need to address their cultural context (Turner, 2015). They expect AI-enhanced, hands-on, real-world learning that integrates technology and prepares them for contemporary careers.

Generative AI, influenced by socio-political and cultural forces, is reshaping self-directed learning and serving as a catalyst for personalized education, such as in language acquisition (Li, Bonk, Wang, & Kou, 2024). Over the past five years, AI literacy initiatives particularly in K–12 education – have expanded significantly (Goenka, Patnaik, & Pradhan, 2024); Some curricula treat AI literacy as an extension of digital or data literacy (Schüller, 2022); reflecting the converging competencies needed in a data-driven world.

European scholarship advocates for universal AI education, asserting that every citizen requires foundational AI knowledge; UNESCO defines AI as machines capable of human-like perception, learning, reasoning and creativity (Stolpe & Hallström, 2024). With many current workers lacking skills to transition into AI-related roles, integrating AI training into formal education systems has become increasingly urgent (Kong, Cheung, & Tsang, 2024).

Cultivating AI literacy for Generation Z is a societal imperative; As primary users of AI, Gen Z faces a dual reality: while offering transformative potential, AI also introduces risks such as algorithmic bias, privacy erosion and misinformation; Many lack the critical understanding needed to navigate these challenges, leaving them vulnerable to manipulation and reinforcing broader issues like digital inequity and social polarization; A comprehensive AI literacy framework must integrate technical, ethical and cultural dimensions; This includes addressing region-specific needs as seen in contexts like Iran, where technological adoption outpaces curricular adaptation and equipping students to critically engage with AI's societal impacts. Beyond technical skills, emphasis must be placed on fairness, accountability and cross-cultural competence to ensure AI serves diverse global communities; By fostering such multidimensional literacy, we can empower Gen Z to harness AI responsibly, mitigate its risks and lead in an era defined by human–AI collaboration.

The integration of AI into interactive learning ecosystems enables instruction and personalized engagement, empowering educators with intelligent tools and increasing student acceptance of technology (Wu, Zhang, Ma, Yue, & Dong, 2024); Cultivating AI literacy is essential to mitigate potential drawbacks and maximize benefits. While literacy was historically confined to reading and writing in sanctioned contexts (Fransman, 2008), the digital revolution has expanded it into a dynamic construct encompassing visual, media, computer, information and AI literacies (Järnerot & Veelo, 2020; Al-Azri, Alharrasi, & Al-Aufi, 2023; Potter, 2010; Kong, 2014).

A theoretical framework for AI and Gen Z literacy emphasizes digital fluency and socio-ethical awareness. AI literacy involves understanding and using AI technologies responsibly a necessity for digitally native learners (Lee et al., 2024); Long and Magerko (2020) propose an AI literacy model covering tool selection, interaction comprehension and ethical considerations. Chardonnens (2025) highlights how integrating AI with active learning strategies like project-based learning enhances motivation and self-regulation; Stolpe and Hallström (2024) identify three AI literacy paradigms technical, scientific and socio-ethical aligning with Gen Z's demand for personalized, immersive experiences; Together, these frameworks advocate a balanced approach fostering critical thinking and digital agency.

AI literacy thus joins a family of “new” literacies that promote critical awareness and domain-specific competencies, including digital literacy (Nguyen & Habók, 2024), computational literacy (Hachmann, 2024), critical digital literacy (Ilomäki et al., 2023), media literacy (Potter, 2010), information literacy (Tuominen, Savolainen, & Talja, 2005), data literacy (Gould, 2017), visual/auditory literacy (Bodén, 2023) and algorithmic literacy (Ridley & Pavlik-Pots, 2021).

Long and Magerko (2020) define AI literacy as the competency to apply AI tools to solve problems across personal and professional domains. However, integrating AI into education introduces pedagogical and ethical complexities, necessitating educator proficiency in both AI theory and practice (Beege, Hug, & Nerb, 2024). For Generation Z, tech-savviness expands learning beyond traditional settings, where AI literacy simultaneously boosts engagement and critical thinking (Singh, Vasishta, & Singla, 2024; Baskoro, Mariza, & Sutapa, 2023) yet risks encouraging procrastination (Lim & Lee, 2024) and provoking privacy concerns (Jabar, Chiong-Javier, & Pradubmook Sherer, 2024). These insights underscore the imperative for balanced, ethically informed AI adoption in education. Related findings are synthesized in Table 1.

The data were analyzed using reflexive thematic analysis within the qualitative framework established by Braun and Clarke (2006). This involved a six-phase iterative process: familiarizing ourselves with the data, generating initial codes, searching for themes, reviewing themes, defining and naming themes and producing the report.

Participants were selected through criterion-based purposeful sampling to include information-rich cases aligned with the study's objectives. This approach prioritized individuals with direct expertise and experience relevant to the research focus. Sampling continued until theoretical saturation was achieved ensuring a comprehensive exploration of the phenomenon. A referral network of recognized experts was also employed to diversify perspectives, in line with established qualitative practices (Parker, Scott, & Geddes, 2019). The sample consisted of educational specialists, university faculty and curriculum experts who met predefined inclusion criteria, including substantial professional experience, advanced subject-matter knowledge and familiarity with both AI literacy and relevant cultural contexts. All participants held positions at the associate professor level or higher and demonstrated willingness to engage deeply with the research questions. A detailed overview of participant demographics is provided in Table 2.

This study utilized semi-structured interviews, guided by a protocol aligned with research objectives and refined by six educational management experts. Interviews were adapted dynamically to capture detailed responses (Magaldi & Berler, 2018).

Reliability was strengthened through colleague debriefing and member checking, with participants verifying their responses and interpretations. Purposive sampling ensured diverse professional backgrounds, continuing until thematic saturation was reached. Participants also reviewed emergent themes to reconcile discrepancies.

Dependability was ensured via an independent audit of data collection and analysis procedures. Conformability was achieved by maintaining comprehensive documentation, ensuring all findings were traceable to raw data. Transferability was supported through thick descriptions of the research context and participants.

All interviews were transcribed verbatim and analyzed through repeated close readings. Significant statements were inductively coded using MAXQDA software, enabling systematic thematic organization while adhering to qualitative research standards.

The research implementation process was carried out during the years 2024–2025 and the ethical and professional considerations of the research were observed; the objectives of the research were explained to the participants and they participated with satisfaction and awareness of the research process; their information was kept confidential. The ethical guidelines outlined by the American Psychological Association's publication manual and to this study. These guidelines include principles such as confidentiality, data privacy and obtaining written informed consent from the individuals in the sample.

1. What are the essential AI literacy competencies (and their sub-dimensions) that Generation Z requires navigating culturally diverse digital environments?

2. What culturally-embedded learning facilitators and inhibitors most significantly impact Generation Z's acquisition of AI literacy skills?

3. Which culturally-adaptive teaching methodologies most effectively develop AI literacy competencies in Generation Z students from diverse backgrounds?

As presented in Tables 3 to 6, the findings derived from expert interviews highlight key themes including AI literacy competencies, learning facilitators and inhibitors of AI literacy and culturally adaptive teaching practices. These tables display the frequency and percentage of statements associated with each concept, offering a quantitative overview of how often specific ideas were referenced in relation to each open code. As Table 3 shows, AI literacy competencies include Ethical AI Awareness, Critical Digital Discernment, Intercultural AI Communication, Global AI Governance Fluency, Emotional & Social AI Intelligence. Table 4 shows Learning facilitators of AI literacy in the form of nine main components. Table 5 shows the main elements of Learning inhibitors of AI literacy in the form of five main components including Economic Constraints, Outdated Educational System, Brain Drain & Talent Flight, Sociocultural Barriers and Surveillance Concerns. In this regard, Table 6 shows Culturally-adaptive teaching with eight main components.

Based on Table 3, AI literacy competencies are composed of five main components, with Ethical AI Awareness being the most prominent at 37%, followed by Intercultural AI Communication (24%), Critical Digital Discernment (23%), Global AI Governance Fluency (9%) and Emotional & Social AI Intelligence (7%). Each component encompasses specific subcomponents focused on the cultural, ethical and critical application of AI, such as analyzing AI bias, using AI for cross-cultural dialogue, understanding global AI regulations and navigating AI's social and emotional impacts across different cultural contexts.

Table 4 details the nine principal facilitators of AI literacy led by Internet Access & Infrastructure (15%), Student Motivation & Identity (13%) and a three-way tie between School AI Initiatives, Job Market & Career Pathways and Cognitive & Cultural Strengths (each at 12%). These are followed by Higher Education & Universities (11%), Private Sector & Startups (9%) and STEM Education System and Self-Learning Culture (each at 8%); The corresponding subcomponents reveal a holistic ecosystem spanning formal education, digital infrastructure, economic incentives, cultural attitudes and self-directed learning channels that collectively enable and encourage the development of AI skills.

Table 6 outlines five principal inhibitors of AI literacy, with Sociocultural Barriers being the most significant at 29%, followed by an Outdated Educational System (25%), Brain Drain & Talent Flight (18%), Economic Constraints (16%) and Surveillance Concerns (12%). Their subcomponents reveal interconnected challenges spanning pedagogy, culture and economics, such as a rote-learning culture, emigration of skilled talent, English proficiency gaps, job market uncertainty and fears of government monitoring, which collectively hinder the effective development of AI competencies.

Table 6 details eight components of culturally-adaptive teaching, led by Metacognitive Strategy Integration and Multilingual & Multimodal Instruction (each at 18%), followed by Culturally Relevant Project-Based Learning (15%), Peer-to-Peer & Community-Based Learning (12%), Critical AI Literacy & Ethics (11%) and both Gamified & Adaptive Learning Platforms and Cross-Disciplinary Integration (each at 8%). Their subcomponents outline a pedagogical approach that leverages cultural relevance, multiple languages and media, ethics, peer collaboration and adaptive technology to create an inclusive and effective learning environment.

As digital natives, Generation Z requires an AI literacy framework that moves beyond technical proficiency to include ethical, cultural and social dimensions. Which the research findings in the competencies section paid attention to, this entails understanding AI's technical workings while critically examining how these systems reflect and reinforce societal biases (Biagini, 2025). Immersed in AI, Gen Z must be equipped not only to use these tools but to evaluate their implications for equity, privacy and identity (Gupta et al., 2024).

Culturally responsive education is essential. Through participatory design and storytelling; like collecting family narratives or using multimedia to express cultural identity students can explore AI as a technical and cultural mediator, understanding how it can perpetuate or challenge social inequities (Dangol et al., 2024); Although cultural differences can have an impact on it, its infrastructure, especially in developing countries, should be considered in schools and universities; Hands-on activities, such as designing simple AI models, transform students from passive consumers into active creators who can identify bias and envision more equitable technologies (Wang et al., 2025).

This vision aligns with a broader educational transformation toward interdisciplinary, student-centered learning; Connecting academic content to culturally meaningful, real-world problems like environmental justice or cultural preservation fosters deeper engagement and civic responsibility; Schools also play a leading and facilitating role in this; Involving families and community leaders further enriches the curriculum with intergenerational wisdom (Eguchi, Okada, & Muto, 2021).

This holistic approach integrates several key pillars:

1. Metacognitive Development: Teaching students to plan, monitor and evaluate their own learning cultivates independence and resilience (Chardonnens, 2025).

2. Multilingual & Multimodal Access: Using diverse tools podcasts, infographics, simulations ensure inclusive instruction that affirms student identities (Walter, 2024).

3. Creative Expression: Digital storytelling allows for powerful identity exploration and cultural transmission, building empathy.

4. Ethical Technology Use: Students must learn to navigate AI's ethical dimensions, questioning systems and advocating for responsible innovation (Biagini, 2025; Chan & Lee, 2023).

5. Engaging Tools: Gamified learning platforms can offer personalized, narrative-driven pathways that maintain engagement (Gupta et al., 2024).

6. Collaborative Teaching: Cross-disciplinary integration prepares students to tackle complex challenges by synthesizing knowledge from multiple fields.

Together, these strategies form a cohesive vision for education that is inclusive and future-ready. By grounding learning in lived experience and fostering ethical, reflective thinking, we can equip all learners to lead with empathy, innovation and purpose.

Integrating AI literacy into a culturally responsive, interdisciplinary framework has profound implications for education and society. Academically, it requires redesigned curricula that blend technical AI knowledge algorithms, data and training with ethical reasoning and cultural relevance; Educators need professional development to lead critical discussions and create inclusive, multimodal learning experiences; Embedding AI across subjects and affirming students' cultural identities can boost engagement, strengthen metacognitive skills and ensure equitable access to future-ready learning; Assessments, in turn, must measure interdisciplinary thinking, ethical reflection and creative problem-solving.

Socially and ethically, this approach cultivates informed digital citizens and responsible innovators. By examining how AI can either perpetuate or disrupt bias, students learn to critically assess technology's impact on equity, privacy and identity; Involving families, elders and community mentors enriches intergenerational dialogue, strengthens civic ties and preserves cultural heritage through storytelling and multilingual expression; Ultimately, this model democratizes AI literacy, empowering Generation Z to not only adapt to technological change but to shape it with empathy, cultural awareness and ethical integrity.

The study's exclusive focus on Iran limits its generalizability to other constrained contexts due to a lack of comparative data; Most studies are based in high-income regions, limiting their global relevance and overlooking underrepresented communities; Structural barriers, such as rigid subject boundaries and insufficient teacher training, further hinder the integration of AI literacy into interdisciplinary and culturally responsive education. These limitations highlight the need for more inclusive, globally informed research that connects technical, ethical and cultural dimensions of AI.

To advance AI literacy in education, it is essential to first establish a unified, multidimensional framework that integrates technical, ethical and cultural components; A shared framework ensures that AI literacy is not treated as a narrow technical skill but as a holistic competency relevant across disciplines.

Equally important is investing in teacher training and fostering cross-disciplinary collaboration. Encouraging collaboration between fields such as computer science, humanities and social studies can enrich instruction and help students connect AI concepts to real-world issues.

Expanding research in underrepresented and Global South contexts is critical. Most existing studies are concentrated in high-income regions, limiting their global applicability. Context-sensitive research will help tailor AI education to local needs, promote equity and ensure that all learners are empowered to engage critically and creatively with emerging technologies.