A comprehensive AI literacy framework with a four-level progression model provides educators with a structured pathway to develop students' technical competencies and ethical awareness, preparing K–12 learners to responsibly navigate an AI-driven world.
Objective
The primary goal of this study was to develop a conceptual framework for AI literacy specifically tailored to K–12 education, particularly focusing on high school students (grades 9–12). The researchers aimed to address the gap in understanding how students develop AI literacy skills by creating a structured learning progression that maps students' skill development from foundational understanding to advanced applications. The study sought to provide educators with a roadmap for designing curricula and assessment tasks that foster continuous, progressive skill growth while addressing inequities in access to AI education. Additionally, the research aimed to integrate both technical competencies and ethical awareness, recognizing that as AI becomes increasingly integrated into daily life, students need to develop competencies to recognize and mitigate potential inappropriate uses or unintended harm.
Methods
The researchers employed an evidence-centered design (ECD) approach involving a comprehensive literature review of existing AI literacy frameworks and competency models. They conducted a systematic analysis using keywords such as "AI literacy," "AI education frameworks," and "AI competencies" to search major academic databases including Google Scholar and ERIC, focusing on recent, peer-reviewed sources with relevance to K–12 education. The team used a design pattern document to provide structure for defining complex constructs and conducted an iterative coding process to extract competencies and indicators from each framework. They employed open coding to identify recurring patterns across frameworks, grouping them into broader categories such as foundational concepts, technical skills, cognitive and metacognitive skills, and ethical considerations. The framework was explicitly grounded within a broader digital literacy framework and informed by social constructivist theory, which emphasizes collaborative learning, scaffolded support, and contextual engagement. The researchers also used ChatGPT to evaluate consistency of language used to describe advancement across progress levels in the progression framework.
Key Findings
The study resulted in several significant contributions to AI literacy education. First, the researchers developed a comprehensive definition of AI literacy as "the ability to understand, interact with, and responsibly use AI systems to access, manage, and create information, while effectively collaborating, both by engaging AI in the process and by working with other humans through AI, to make ethical, informed decisions." The framework identifies four core subskills: (AI.1) using AI to access, manage, and evaluate information; (AI.2) using AI tools to communicate, enhance teamwork, and ensure responsible collaboration; (AI.3) using AI to create, personalize, and adapt content; and (AI.4) using AI with an understanding of ethical impacts and responsible decision-making. Each subskill includes specific indicators with detailed behavioral descriptions.
The proposed learning progression structures development across four proficiency levels: Level 1 (Emerging), where learners show initial awareness and engage with support; Level 2 (Developing), where learners approach tasks with greater initiative in familiar contexts; Level 3 (Proficient), where learners demonstrate deeper understanding and consistent performance; and Level 4 (Exemplary), where learners show advanced application with precision and adaptability. The study also established three design principles for task development: ensuring relevance to learners through authentic, contextually meaningful examples; minimizing barriers to resource access through inclusive and accessible materials; and providing opportunities for skill advancement through scaffolded learning experiences. An illustrative example task focusing on ethical decision-making with DALL-E image generation demonstrates how the framework can be applied in practice.
Implications
This research makes several important contributions to the field of AI in education. The framework provides educators with actionable guidance for curriculum integration, allowing them to embed AI literacy tasks into core subjects like science, mathematics, and social studies. The structured progression enables formative assessment by providing behavior indicators as benchmarks for evaluating student progress and tailoring instruction to individual needs. The framework supports differentiated instruction through scaffolding strategies appropriate to different proficiency levels, gradually reducing support as students develop competency. By emphasizing both technical skills and ethical considerations, the framework addresses a critical need identified in recent reviews for curricula that promote not only technical proficiency but also ethical reasoning and societal responsibility.
The research also has significant policy implications, aligning with global priorities from organizations like OECD and UNESCO to promote equitable, ethical, and sustainable AI education. The framework addresses educational disparities by providing clear pathways for AI skill development and supports the development of context-specific AI curricula. Furthermore, the framework's flexibility allows for application beyond formal classrooms, providing opportunities to examine how AI skills develop in informal learning environments and across different student populations, thereby fostering more inclusive AI education.
Limitations
The researchers acknowledge several important limitations. Most significantly, the proposed progression is theoretically grounded but has not yet been empirically validated, meaning studies are needed to verify whether it accurately reflects AI skill development in practice. The framework focuses particularly on high school students (grades 9–12), which may limit its direct applicability to younger learners without adaptation. The study recognizes that significant variability exists in how students apply AI skills across different contexts, influenced by factors such as access to resources, prior technological exposure, and individual differences. The researchers note that learning rarely follows a straightforward process, and students may advance in some subskills faster than others, following non-linear pathways.
The review conducted was limited to literature available at the time, and the rapid pace of AI technological advancement means the framework may require periodic updates to remain relevant. The framework's integration into traditional subjects requires further exploration to ensure AI skills support rather than compete with broader disciplinary goals. Additionally, while the framework addresses ethical considerations, some existing frameworks fail to provide concrete, practical activities that help students connect ethical discussions to real-world contexts, a gap that needs continued attention.
Future Directions
The researchers outline several critical areas for future investigation. Empirical validation of the proposed framework through pilot studies, longitudinal research, and classroom implementations is essential to refine its structure and assess effectiveness. Research should explore how the framework can be applied and adapted across diverse educational settings, including different grade levels, socioeconomic contexts, and geographic regions. Studies examining how AI literacy intersects with related competencies such as digital literacy, computational thinking, critical reasoning, and adaptability would provide valuable insights into the complementary nature of these skills.
Future work should investigate both the antecedents of AI literacy and the near- and long-term consequences of applying the skill in various contexts. Research exploring how integrating AI skills into core subjects impacts learning outcomes and interdisciplinary engagement would inform effective curriculum design. The framework's applicability in informal learning environments and across different student populations should be examined to understand how AI skills develop outside traditional classroom settings. Additionally, research should address concerns about overreliance on AI and its potential impact on cognitive functions such as processing speed, long-term memory retention, critical thinking, and problem-solving abilities. Given the rapid evolution of AI technologies, ongoing research is needed to keep pace with emerging tools, methodologies, and ethical considerations, ensuring the framework remains current and relevant for preparing students to navigate an AI-driven future.
Title and Authors
Title: "Preparing K–12 Students With AI Literacy: Proposed Framework, Progression, and Task Design Principles"
Authors: Srijita Chakraburty (Indiana University, School of Education), Teresa M. Ober (ETS Research Institute), and Lei Liu (ETS Research Institute)
Published On: November 2025
Published By: ETS Research Report Series (Research Report No. RR-25-14), Educational Testing Service