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AI Literacy

created 2026-05-07 · updated 2026-09-12 · ai literacy, higher ed, k 12, generative ai, llm, ai education, educational development, academic integrity

AI literacy — the knowledge, skills, and critical dispositions needed to understand, evaluate, and effectively use AI technologies in educational contexts. AI literacy spans foundational understanding of how AI works, practical competence in using AI tools, critical evaluation of AI outputs, and ethical awareness of AI's societal implications.

Questions to Consider

  • What does it mean to be 'AI literate'? Is it knowing how to use the tools, understanding how they work, or being able to critically evaluate their output — and which matters most for your own goals?
  • Self-reported AI literacy diverges sharply from measured performance — teachers overestimate their skills by about 40%. How confident are you in your own AI skills, and what evidence would you trust to actually test that confidence?
  • AI literacy is described as a metacognitive social practice, not a checklist of skills. Because LLMs are probabilistic and opaque, what does it mean to 'critically evaluate' output when you can't see inside the model?
  • There's a tension between critical-use literacy for academic settings and workflow-integration literacy for employment — educators and employers value different skills. Which kind of AI literacy is your context actually teaching?
  • Technical AI-literacy training alone, without self-efficacy and self-regulation support, can actually increase dependency on AI. How could learning to use AI make you more reliant on it rather than more capable?
  • AI literacy is also a recognition skill: spotting when an AI is agreeing with you because it's being sycophantic versus because you're right. Have you ever caught an AI just telling you what you wanted to hear?

Introduction

AI literacy has rapidly emerged as a core competency for learners, educators, and institutions as generative AI becomes embedded in education. Unlike general digital literacy, AI literacy requires understanding probabilistic systems that can hallucinate, exhibit bias, and shift Agency from human to machine — making Critical Thinking and Over-Reliance central to the construct.

Dimensions of AI literacy

AI literacy research in this knowledge base spans four interconnected dimensions:

Frameworks increasingly trace how these dimensions are enacted in practice: Dai & Chan (2026) show how postgraduate researchers enact all four dimensions when using generative AI across the research workflow and scaffold research-specific responsible-use guidelines from them, while Şan & Orhan Karsak (2026) use Word-Association mapping to show that Turkish undergraduates' AI cognition is instrumentally "black-box" dominated, with ethical-governance concepts structurally isolated (τ=−0.819) — evidence that credential design must deliberately bridge experiential tool use and ethical frameworks.

Foundational knowledge: Understanding what LLMs are, how they differ from rule-based systems, and their fundamental limitations. This includes awareness of model capabilities, training data biases, and the distinction between task-specific AI and general-purpose models. Research in Prompt Engineering examines how understanding prompt mechanisms affects effective AI use.

Practical competence: The ability to use AI tools effectively — from Prompt Engineering to interpreting outputs. Studies of student GenAI usage patterns reveal that tool access alone doesn't produce competence; structured practice and Scaffolding are essential. The vibe coding framework shows how K-12 teachers can develop practical AI literacy through guided tool creation.

Critical evaluation: The capacity to assess AI outputs for accuracy, bias, and appropriateness. Research on literacy assessment shows a 40% gap between self-reported and performance-based AI literacy — people consistently overestimate their evaluation skills. That divergence is the clearest case in the knowledge base of a self-report measure failing to track the competence it names. This connects to Over-Reliance research showing that students who Trust AI uncritically learn less. Recent conceptual work pushes evaluation toward verification: the PEARLS framework (Wang) treats AI output as a provisional knowledge claim whose warrant must be assembled and examined across six dimensions (Process, Evidence, Access, Reproducibility, Legitimacy, Source), and advances verification-driven learning as the mechanism by which learners build expertise while checking AI claims. Complementing this, the student-centered responsible-use framework (Alsammani) offers ten student-facing guidelines across Learning and Growth, Ethics and Integrity, and Awareness and Safety pillars that externalize metacognitive prompts at the point of decision. For secondary learners, the AI-Assisted Research Competency (AARC) framework (Beau, Flaquière & Lazar 2026) grounds this in virtue epistemology and AI intuition, defining research literacy as conducting inquiry with AI without surrendering authorship, judgment, verification, or responsibility — operationalized as an analytic rubric and a verify–cite–reflect commitment routine. At the Assessment end, Saleh (2026) proposes a human capability test that turns evaluation into a design principle: ask what a student must demonstrate independently, what can be strengthened through AI augmentation, and what the student must verify, defend, and take responsibility for.

Ethical and institutional awareness: Understanding AI's broader implications — from Academic Integrity to Equity In AI Education to Privacy. AI literacy at the institutional level involves policy development, Educational Development, and governance frameworks — institutional AI literacy is a matter of policy as much as pedagogy. The EPIQ-AI framework frames institutional AI literacy as a sociotechnical alignment challenge, not just individual training.

  • AI literacy as a governance capacity for sustainable development. Islam, Morshed, and Islam (2026) reconceptualize AI literacy as a governance-oriented capacity rather than a purely educational or technical skill, linking it to all seventeen UN Sustainable Development Goals. Their six-level AIRE Taxonomy (Recognize → Comprehend → Apply → Analyze → Integrate → Govern) extends Bloom's hierarchy by adding ethical synthesis and strategic foresight, positioning advanced competencies (Analyze–Govern) as the pathway from foundational literacy to institutional and policy-level governance — an "18th SDG" heuristic that treats literacy as a cross-cutting cognitive and ethical bridge. A survey of 300 professionals in a national context found strong technical awareness but limited ethical and governance readiness, with ethical reasoning and reflective thinking the strongest predictors of sustainable, trustworthy AI use and governance literacy the strongest predictor of AI–SDG nexus awareness (β = 0.64). This empirically grounds the knowledge base's emphasis on critical-use literacy and ties AI literacy directly to Sustainability and policy integration.

How AI literacy is developed

Research points to collaborative and active approaches as most effective. Rismanchian & Doroudi position AI literacy as more than applied skill: in their AI×Ed framework the learner is a distinct end user of AI (accessed through AI literacy and AI education), and they argue for renewed AI-literacy work that encourages reflection on learning itself — treating literacy as a route back into "AI as an analogy to human intelligence" research on how people learn, a strand the field largely abandoned. The ICAP framework (Interactive → Constructive → Active → Passive) provides a useful taxonomy of cognitive engagement: students learn AI literacy best when they co-construct knowledge rather than passively receive information. Designers should select the mode that fits the learning goal and favor the deeper (constructive and interactive) modes where possible. Practical activities — designing prompts, evaluating outputs in groups, debating AI Ethics — outperform lectures.

AI intuition as the experiential complement to literacy. A recurring gap is that K-12 frameworks assume learners approach AI through declarative, rule-based knowledge, when in practice they first develop a practical "feel" for how AI responds — experimenting with prompts, observing behaviour, adapting strategies — before they can articulate formal principles. Beau & Lazar (2026) formalise this as AI intuition: an experiential, inductive, often tacit understanding that develops through iterative interaction with AI systems and supports context-sensitive judgement under uncertainty. Their dual framework situates AI literacy (structured, largely static competencies) alongside AI intuition (a dynamic learning process), mapping both across the standard dimensions — understand concepts, use tools, evaluate critically, apply ethically, reflect — so learners combine conceptual clarity and Guardrails (literacy) with the practical wisdom to trust-but-verify and decide when to disengage (intuition). Because intuition cultivates "expert observation" of AI (e.g. stress-testing prompts to find where a model fails), the authors argue it is a safeguard against over-reliance and critical-thinking erosion; it is distinct from Prompt Engineering (which optimises outputs) in foregrounding epistemic judgement. This connects literacy to Experiential Learning, constructivism, and developmentally grounded classroom practice, and points teacher preparation toward facilitating inductive exploration rather than only delivering concepts.

Motivation is a precondition, not just an outcome. Liang et al. (2026) found, across 2,086 secondary students in a year-long AI curriculum, that students who transitioned into or remained in a Self-Determined motivational profile (high autonomy, competence, and relatedness need satisfaction) showed the greatest AI-literacy gains. AI literacy develops through sustained engagement, and that engagement is itself shaped by motivation and psychological-need support — so effective AI-literacy instruction should attend to learners' motivation, not only their skills.

A core applied aim of AI literacy is Reducing AI Misuse: teaching students to use AI ethically and productively rather than substituting it for their own cognitive work. Where AI literacy builds the capacity to evaluate and use AI critically, reducing misuse is the behavioral and structural payoff — combining guardrailed tool design, assessment redesign, and educative levers such as scaffolded think-first/AI-second sequences and prompting practice with deliberate Feedback. The two concepts are mutually reinforcing: AI literacy supplies the critical dispositions that make misuse-reduction interventions durable, while misuse-reduction evidence (e.g. the performance–learning gap) motivates why literacy must go beyond operational skill to critical judgment.

AI literacy also needs developmentally appropriate forms for the youngest learners. AI-Play translates AI literacy competencies into play-based, unplugged activities for Pre-K–K2 learners — organized around AI Body (AI as a system built from parts), AI Food (AI learns from examples), AI Brain (AI improves through patterns and feedback), and a Pre/Post-AI ethical lens — addressing a persistent lack of developmentally grounded AI literacy guidance for early childhood and making AI literacy accessible to non-technical educators and families. Complementing this, Vahedian Movahed & Martin (2025) found children aged 6–14 broadly trusted an age-tailored chatbot as an information source and most modeled it as "a smart computer program that learns" (nascent Machine Learning understanding), yet showed gaps in critical engagement and digital-safety awareness — evidence that young learners' trust can outpace their critical evaluation skills, making explicit Trust Calibration and safety instruction a necessary part of AI literacy for children.

Project-based learning as a delivery mechanism. Zhu & Kong (2026) developed and validated an AI project-based learning (AI-PBLS) scale and, in a Hong Kong sample of 1,027 secondary and university students (446 with complete data), used structural equation modeling to show that empowerment in using AI for problem solving and AI ethical awareness jointly mediate the relationship between perceived project-based learning and satisfaction with an AI literacy course. This positions PBL not merely as a delivery format but as a mechanism that builds learner confidence and ethical reasoning alongside competence, reinforcing the link between PBL and meaningful AI literacy development. It also supplies a validated measurement instrument for future measurement of AI literacy course experiences.

AI literacy as a core gap in Conversational AI frameworks. The umbrella review of conversational AI agents (Ganguly et al. 2025, 34 reviews) identifies limited AI literacy support as a major gap in CAI frameworks, and its ethical-use roadmap makes foundational assessment (including strengthening AI literacy) the first pillar alongside participatory design, ethical-use guidelines, and continuous evaluation of cognitive impact. It further finds that AI-literacy, training, and awareness rank among the most-emphasized ethical directions in the CAI literature.(Conversational AI Agents Umbrella Review 2026)

Effectiveness of AI literacy interventions. A three-level meta-analysis of 59 studies (172 effect sizes, 7,211 participants) estimates a large overall effect of AI literacy interventions (g = 0.837, p < .001) — but with a wide prediction interval [−0.292, 1.966], so effectiveness varies considerably across settings. Interventions in East Asia and Europe outperformed those in North America, and knowledge-focused interventions outperformed those targeting skills, attitudes, or ethics. The authors argue AI literacy education should therefore move beyond knowledge toward skills, practices, ethics, and attitudes, supported by integrated and reflective pedagogies (project- and problem-based, inquiry-based, experiential) and GenAI-supported tools — a shift that aligns with the participatory, producer-oriented forms of Computational Thinking described elsewhere in this knowledge base.

AI-supported critical media literacy in elementary school. Demir and Akar (2026) provide a concrete elementary-school demonstration of GenAI-supported literacy instruction: an 18-hour, 5E-model program for fourth-grade Turkish students in which ChatGPT and Grammarly were embedded phase-by-phase as pedagogical agents (ChatGPT for reflective questions and Q&A, Grammarly and Canva AI for content refinement, Padlet for peer feedback), aligned to the Turkish Language and Social Studies curricula. The AI-supported group showed large gains in media reading (+3.50), writing (+1.67), and total media literacy (+5.17, all p < .01) with between-group effect sizes of Cohen's d = 1.12–1.31, while the control group advanced only modestly. Qualitative analysis surfaced six domains of critical media literacy growth — digital self-protection and data privacy, purposeful and responsible media use, safe communication and boundary awareness, critical evaluation and misinformation awareness, online risk awareness, and media ethics/digital citizenship — evidence that developmentally appropriate, discipline-embedded GenAI use can build the critical-evaluation and ethical dimensions of AI literacy, not just operational skill.

Frameworks for structuring AI literacy. Several recent contributions offer structured progressions for building AI literacy. Marienko, Markova, and Semerikov (2026) propose a five-level framework (Awareness, Application, Evaluation, Creation, Ethics) integrated with three paradigms of AI in education (AI-directed, AI-supported, AI-empowered), developed through a mixed-methods study of Ukrainian secondary educators (national survey n = 2018; PD evaluation n = 1130). They found 84% of educators use AI but only 11% can identify specialized services beyond ChatGPT, and a professional-development intervention produced a 24% improvement in AI competence — evidence that targeted PD advances literacy beyond surface-level tool familiarity. The framework's grounding in constructivism, connectivism, and TPACK connects to TPACK and Teacher AI Competency. Complementing this, Moore et al. (2026) used a two-year DBR process with a youth and AI-expert advisory board to design a science-integrated ML curriculum for high school youth, finding ML-knowledge gains in both cohorts (Cohort 2 M2−M1 = 0.175 vs Cohort 1 0.076) and greater gains among female and non-White participants — evidence that participatory, discipline-integrated design can advance both AI literacy and equity.

AI-interaction literacy: the interactional dimension. Brunnström and Palmqvist (2026) propose a narrower, interactional competence — the ability to steer, evaluate, and learn from iterative interaction with GenAI — as a specific enactment of the applicational, evaluative, and integrational dimensions in broader frameworks such as the AI Literacy Heptagon. Their reflective demonstration shows what it consists of in practice: recognising that a fluent answer is pitched above one's own schema, requesting simplification, narrowing scope, and redirecting the system toward focused practice. Two design consequences follow — the skill is unevenly distributed, so unguided use may advantage already-confident students and widen gaps (Equity In AI Education), and it has to be taught explicitly rather than assumed, with teachers covering how to formulate productive prompts and when to stop using the tool (Self Regulated Learning).

Critical AI literacy: beyond skills to power and resistance

A distinct strand of the knowledge base treats AI literacy not merely as skills or critical evaluation but as a critical and political practice that interrogates power, authority, and whose knowledge counts. This connects AI literacy to Critical Pedagogy and Equity In AI Education:

  • "Resisting AI" as a literacy stance. Critical AI Literacy (CAIL) can encompass resisting AI — refusing the inevitability and techsolutionism of dominant discourse, and cultivating collective agency through dialogic, collaborative pedagogies.(Li Mroziak Reorienting Critical AI Literacy) This positions education as a space where communities imagine and build alternative futures, rather than merely adapt to a given technological order.
  • Community-based epistemologies. Community-based AI learning grounds AI engagement in learners' lived and community-based ways of knowing, redistributing AI's epistemic authority through epistemic fine-tuning, redistribution of authority, and situated discernment.(Ojeda Ramirez Community Based AI Learning)
  • Critical and feminist frames. Critical, feminist scholarship argues AI literacy should be framed within pedagogies of justice, resistance, and cultural Sustainability — asking whose knowledge AI produces, who benefits, and who is accountable when systems fail.(Avraamidou AI Colonization Science Education)
  • Situated curriculum devices. AI literacy can be built through situated, active teaching instruments (e.g., Episodes of Situated Learning) that develop AI competencies across levels rather than through abstract instruction.(Panciroli AI Literacy Episodes Situated Learning)
  • Regulatory competence, not acceptance. Kim (2026) reframes AI literacy in higher education as regulatory competence — an enacted practice of verifying, revising, selectively adopting, or rejecting AI output during academic work — showing that evaluative capacity and ethical awareness (not mere willingness to use AI) predict active, critical engagement.(AI Anxiety Strategic Regulation Writing 2026)
  • Recognizing sycophancy as a literacy skill. A core evaluative competency is recognizing when an AI is agreeing with the learner versus being correct. Contextual sycophancy shows AI literacy and prompting training reduce — but do not eliminate — sycophantic mirroring of user errors, and sycophantic AI is preferred by users precisely because it makes them feel understood. AI literacy must therefore teach learners to detect agreement-for-its-own-sake and to value corrective friction, connecting to Trust Calibration and Reducing AI Misuse.

These critical strands complement the operational and cognitive dimensions of AI literacy: where the latter ask "can the learner use and evaluate AI?", critical AI literacy asks "does the learner understand and challenge the power structures AI embodies?"

Whose AI skills count? The instructor–employer framing divide

A central open question in AI literacy is which skills matter and for whom. An Ithaka S+R study comparing how US instructors and employers prioritize the 26 skills of the HiBob AI Skills Framework found they agree on the importance of only one (setting realistic expectations for AI-augmented work). Instructors weight a critical, responsible-use orientation — recognizing AI's limits, transparency and attribution, human accountability, proactive output review — which aligns with academic values of attribution, review, and information literacy. Employers weight productivity-oriented skills — workflow evaluation and redesign, automation, and human–AI teaming — that reflect team-based workplace efficiency. Because only three of 26 skills are taught by half or more instructors, and those taught skew toward the critical-use categories, the report identifies a concrete AI skills gap: whole categories employers value are neither prioritized nor taught in college curricula.(Ithaka Sr AI Skills College Graduates 2026) This divide frames AI literacy as a contested construct — critical-use literacy for academic settings versus workflow-integration literacy for employment — a tension relevant to Framing AI Use For Students, Curriculum Design, and Professional Training.

Designing AI literacy interventions

The knowledge base's frameworks and empirical studies converge on a set of practical guidelines for educators and instructional designers building AI literacy interventions:

1. Use a structured competency framework as scaffolding, not a checklist. Mature frameworks give designers a shared vocabulary and a developmentally sequenced target. The SAIL framework organizes AI literacy into three domains (AI Concepts; Application and Technical Skills; AI Digital Citizenship) across four scaffolded levels — Understand and Explore → Apply and Integrate → Evaluate and Create → AI++. The AI Literacy Heptagon cross-cuts seven dimensions (technical knowledge, application, critical thinking, ethics, social impact, integration, legal/regulatory) with four Bloom-aligned proficiency levels, and stresses that emphasis must be adapted to disciplinary context — technical programs weight application, humanities programs weight ethical and social-impact reasoning.(The Scaffolded AI Literacy Sail Framework Results Of A Delphi Study For Equitabl)(AI Literacy Heptagon 2026)

2. Engage learners across ICAP modes. Applying the ICAP framework, effective instruction gives learners opportunities to engage at multiple cognitive levels — passive exposure (AI concept lectures), active manipulation (hands-on tool use), constructive generation (creating AI artifacts, self-explaining), and interactive dialogue (collaborative Problem Solving with peers and AI) — selecting the mode that fits the learning goal. A systematic review found successful collaborative AI-literacy interventions spanned all four ICAP modes.(Hingle Collaborative AI Literacy 2025)

3. Assess demonstrated competence, not self-perception. Self-reported AI literacy diverges sharply from measured performance — teachers overestimate their AI skills by ~40%, and performance-based measures correlate with classroom AI integration far better than self-reports (r≈0.72 vs 0.31). Design interventions around performance-based assessment and calibration rather than confidence surveys, and use diagnostic profiles (overestimators vs. true novices) to target support.(AI Literacy Assessment Misalignment)

4. Build metacognitive and critical dispositions, not just operational skill. AI literacy is better understood as a metacognitive social practice than a skills checklist: because LLMs are probabilistic and opaque, learners must monitor and adjust their strategies, cultivate scientific skepticism, and interrogate how algorithms shape knowledge production — not merely learn to operate tools. Participatory co-design and experimental, project-based spaces (not one-off tool training) are where this awareness grows.(Metacognitive AI Literacy Beyond Skills Gap 2026)

5. Embed literacy in the discipline and make it sustained. Movement toward higher stages of AI literacy (from uncritical use → informed use → critical evaluation → improvement) is most visible when experiences are sustained and discipline-embedded rather than delivered as standalone workshops. Design for repeated, contextual practice within authentic coursework.(AI Literacy Continuum Higher Education) A concrete model of discipline-embedded critical literacy is the task-based taxonomy of Dierickx et al. (2026) for journalism: it maps LLM-supported tasks across the four stages of the news workflow (newsgathering, sensemaking, editing, publication/distribution), each tied to a baseline prompt and a risk-and-mitigation strategy. By treating task definition and prompting as a situated form of professional judgement — not a neutral technical skill — it turns prompting itself into a vehicle for critical AI literacy (bias, hallucination, overreliance, and the enduring value of human editorial oversight), and its underlying logic transfers to other knowledge-intensive professions (law, medicine, public policy). Complementary to such discipline-specific taxonomies, the Dohn et al. (2026) taxonomy classifies GenAI learning activities along six dimensions, of which Epistemic Engagement (understanding / using / critiquing / constructing GenAI) directly operationalises AI literacy as the depth of learners' cognitive relationship with the technology — from passive exposure to active critique and construction.

6. Treat equity and the digital divide as design constraints. AI literacy is a mechanism for addressing the three-level digital divide (access, skills, outcomes): closing the device gap is insufficient unless skills and critical use are built so benefits distribute fairly. Interventions should plan explicitly for learners who enter with less prior AI access, and incorporate cultural and governance perspectives rather than treating literacy as culture-neutral.(The Scaffolded AI Literacy Sail Framework Results Of A Delphi Study For Equitabl)(Digital Divide)

7. Pair literacy with misuse-reduction levers. Because AI misuse actively harms durable learning, literacy instruction should be coupled with the structural and educative levers documented under Reducing AI Misuse — guardrailed "hint-not-answer" tool design, assessment redesign, and scaffolded think-first/AI-second sequences with deliberate prompting practice.

8. Start from learners' actual entry point. Learners enter with distinct orientations — avoidance driven by fear, mistrust, or lack of access, versus uncritical reliance that masks misunderstanding. A diagnostic, stage-based approach (rather than a uniform curriculum) lets designers meet students where they are and move them toward critical, responsible engagement.(AI Literacy Continuum Higher Education)

Measuring AI literacy

A distinct research thread treats AI literacy not only as a target for instruction but as a construct to be measured. The knowledge base's assessment strand distinguishes self-reported from performance-based literacy: self-reports diverge sharply from demonstrated competence (teachers overestimate by ~40%), and performance-based measures predict classroom AI integration far better than confidence surveys (r≈0.72 vs 0.31). Validated instruments are emerging to close this gap — the GLAT provides a psychometrically validated generative-AI literacy assessment, and diagnostic profiles (overestimators vs. true novices) let designers target support where it is needed. For design and research, this ties AI literacy to Educational Measurement and to Assessment broadly: a literacy framework is only as useful as the instruments used to track growth, and stage-based continua require reliable measurement to place learners along them.

Measurement also extends to the educators who mediate learners' engagement with AI. Most AI-literacy assessments target students or general users, leaving a gap in teacher education — a gap the Teachers' AI Literacy Scale (TAILS) addresses: grounded in the ED-AI framework with six dimensions (knowledge, evaluation, collaboration, contextualization, autonomy, and ethics), it was validated through exploratory and confirmatory factor analysis with preservice language teachers. Such instruments support measuring and developing the AI literacy of the educators who mediate learners' engagement with AI.

Connections across the knowledge base

AI literacy intersects with AI Tutoring (understanding when and how AI tutors are effective), Teacher AI Competency (educator preparedness), Academic Integrity (knowing what constitutes appropriate AI use), and AI Education broadly. It is both a prerequisite for effective AI use and an outcome of well-designed AI integration — students learn AI literacy BY using AI critically, not just by learning ABOUT AI.

AI literacy is double-edged for overreliance: Maizel et al. (2026) found the skill-based dimensions of AI literacy (using/understanding, detecting) were positively associated with reported AI dependency, while AI Self Efficacy and academic confidence were negatively associated — so technical AI-literacy training, absent self-efficacy and Self Regulated Learning scaffolds, can increase dependency. AI literacy here becomes an enabling capacity whose direction depends on complementary motivational resources.

  • Critique of AI output as a literacy practice: Hosseini (2026) treats evaluating AI-generated errors as a core AI-literacy skill, using failure-mode analysis and iterative prompt refinement in a database design course. The study found students overestimated their AI abilities (self-reported literacy weakly, negatively correlated with objective competency), and that critique-based learning strengthened calibration.

  • Socialist humanist AI literacy (2026): A literature review critiques compliance-oriented AI literacy and proposes a socialist-humanist framing of asynchronous AI literacy and fair use in higher education, linking the historical digital divide to modern AI literacy and calling for approaches that serve human flourishing and equity rather than mechanical policy compliance (Mechanical Compliance Human Flourishing AI Literacy 2026).

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