k-12-education
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- PaperEdArXiv (OSF Preprints)21 Jul 2026
Toward Responsible AI Implementation in K–12 Music Education: A Human-Centered Conceptual Framework Informed by Emerging U.S. Educational Priorities
Jose Gregorio Reinoso Yepez
A conceptual framework for responsible AI implementation in K–12 music education is developed, drawing on U.S. educational priorities and international governance. The framework emphasizes human-centered design, pedagogical integrity, teacher agency, AI literacy, and ethical governance. While focused on music, the principles may apply to other K–12 subjects.
Original abstract
Artificial intelligence (AI) is increasingly reshaping K–12 education, influencing curriculum design, instructional practice, and educational governance. Emerging U.S. priorities emphasize not only technological innovation but also responsible implementation, AI literacy, data privacy, and equitable access. These priorities are especially relevant to K–12 music education, where learning depends on cognitive, artistic, emotional, and social dimensions requiring sustained teacher guidance. Although AI-supported tools offer promising applications for adaptive practice, automated feedback, and AI-assisted composition, their value depends on implementation conditions that preserve pedagogical integrity and teacher agency. This conceptual article develops a Human-Centered Conceptual Framework for Responsible AI Implementation in K–12 Music Education, drawing on U.S. priorities, international governance frameworks, and contemporary scholarship. The framework positions AI as a support system within an educational ecosystem shaped by pedagogy, educator leadership, AI literacy, ethical governance, professional capacity building, and continuous improvement, offering a conceptual foundation for future research, policy, and practice in music education and other K–12 settings.
- NewsEdSurge29 Jun 2026
International Society for Transforming Education Expands its “AI-Ready Graduate” Framework
The International Society for Transforming Education released an expanded version of its 'Profile of an AI-Ready Graduate' framework, designed to help K-12 educators teach higher-order skills for working with AI. The updated framework defines six student roles—Learner, Researcher, Synthesizer, Problem Solver, Connector, Storyteller—with 30 specific skills, moving beyond basic AI literacy to practical applications. The framework is available for free download and aligns with existing educational standards.
Original abstract
<p>On June 28, the International Society for Transforming Education — the organization behind the editorially independent news site EdSurge — released an expanded version of its “<a href="https://iste-ascd.org/ai-ready-graduate">Profile of an AI-Ready Graduate</a>,” a framework designed to help K-12 educators teach students how to work with artificial intelligence.</p><p>The updated framework, designed with support from the nonprofit Britebound, goes beyond basic literacy to higher-order skills. It identifies six roles the organization says students should fill when using AI tools: Learner, Researcher, Synthesizer, Problem Solver, Connector and Storyteller.</p><p>“Today, we are releasing a fully fleshed out version, 30 skills aligned with each of these roles to help model using AI to support our uniquely human skills,” said Richard Culatta, CEO of the organization. “Humans have always used tools to accomplish human tasks. AI is no different, but when we teach AI as a way to support us being better at being human, it is far more relevant and far more meaningful than when we just talk about what AI is.”</p><p>The announcement was made at the organization’s annual conference in Orlando, Florida, one year after the initial rollout of the Profile. While the original framework focused on basic technical understanding of AI, the updated version shows what those skills look like in practice — with role-by-role descriptions, classroom examples and articulations for middle and high school. </p><p>The framework is intended to layer on to the work educators are already doing and aligns with the International Society for Transforming Education’s existing student standards and “Transformational Learning Principles.”</p><p>The updated Profile of an AI-Ready Graduate is available as a free download <a href="https://iste-ascd.org/ai-ready-graduate">here</a>.</p><p><em>(Editor’s note: EdSurge is an editorially independent newsroom of the International Society for Transforming Educati
- NewsEdSurge24 Jun 2026
Outgrowing the Chromebook: Why Advanced STEM Demands Better Student Tech
K-12 schools' one-to-one Chromebook programs support basic digital literacy but lack the computing power needed for advanced STEM coursework like engineering CAD and data science, prompting districts to evaluate more capable student devices.
Original abstract
<p>Across the United States, K-12 schools have spent the past decade building one-to-one device programs. These initiatives have established an essential baseline for digital access, making it easier for students to complete daily schoolwork across grade levels and subjects. By putting a device in the hands of every learner, districts have created a standard foundation for digital literacy, research and everyday classroom engagement.</p><p>As STEM programs continue to grow and mature, however, school leaders are beginning to encounter new questions about how well those devices support more advanced coursework. Pathways in fields like robotics, engineering, cybersecurity and data science increasingly rely on specialized professional applications that reach well beyond general-purpose classroom software.</p><p>In many cases, students can successfully complete introductory work on school-issued devices. But as instruction progresses, the tools required for STEM programs place different demands on student computing resources. As a result, educators and technology directors are taking a closer look at how hardware capacity can keep pace with shifting curricular needs.</p><h2>STEM Tools and Computing Demands</h2><p>While web-based applications work well for introductory coursework and daily assignments, many expanding STEM pathways introduce entirely different technical requirements. Courses in engineering, 3D modeling, cybersecurity and data science rely on industry-standard applications that demand substantial local computing capacity, robust memory and dedicated graphics processing.</p><p>A prime example is <a href="https://www.solidworks.com/"><u>SolidWorks</u></a>, a professional computer-aided design (CAD) platform used in both higher education and engineering industries. When students build detailed, multi-part models or run stress-test simulations, the performance of the device they’re using directly affects how efficiently they can work. Insufficient hardware can
- PaperComputers & Education21 Jun 2026
Giving parents HOPE: Predictors of parental confidence and support needs in elementary and secondary online learning
B.E.B. Sandberg, Richard E. West, Charles R. Graham, Qi Guo
Identifies predictors of parental confidence and support needs in K-12 online learning environments, offering insights for improving family engagement in digital education.
- PaperComputers and Education: Artificial Intelligence16 Jun 2026
Fostering machine learning literacy in senior primary education: Evaluating a structured pedagogical course design
Siu Cheung Kong, Qiaoyi Wang
A structured six-to-8 hour machine learning course for senior primary students (average age 11.36) significantly improved their understanding of ML concepts, including supervised learning and reinforcement learning, with a Wilcoxon effect size of 0.55. The pedagogy combined guided worksheets, hands-on activities, and iterative refinement within robot software, enhancing conceptual learning and engagement. Students even developed initial reflections on distinguishing between AI and human learning.
Original abstract
Current K–12 artificial intelligence (AI) literacy education emphasizes tool usage over fundamental concepts, yet AI literacy requires grasping how and why AI works – critical for an AI-driven society. This highlights the need for machine learning (ML) education for young learners. We designed and evaluated a six-to 8-h ML course for 752 senior primary students (average age 11.36) across seven Hong Kong primary schools. Pre- and post-test results showed significant improvement in ML understanding, with a Wilcoxon effect size of 0.55. Students comprehended supervised learning and reinforcement learning, including algorithms such as k-nearest neighbours and artificial neural networks, via training robots in competitive circuit tasks and real-time algorithm visualization. Thematic analysis of student interviews revealed that our structured pedagogical approach — blending guided worksheets, hands-on activities, and iterative refinement of data processing, parameter adjustment, and model training within the robots' software — enhanced students’ conceptual learning and engagement. Surprisingly, they developed initial reflections on distinguishing between AI and human learning. These findings suggest the feasibility and promise of teaching fundamental ML concepts to senior primary students through a structured course design. The study contributes to future research and practice in fostering ML literacy among young learners, providing actionable insights for educators to support and allocate resources.
- PaperETS Research Report Series21 Nov 2025
Preparing K–12 Students With AI Literacy: Proposed Framework, Progression, and Task Design Principles
Srijita Chakraburty, Teresa Ober, Lei Liu
Proposes a conceptual framework for AI literacy in K-12, including a hypothesized learning progression and assessment design principles. The framework integrates foundational knowledge, ethical awareness, and practical applications, with key competencies such as ethical decision-making and critical evaluation of AI outputs. Developed through evidence-centered design, it offers educators a structured pathway for scaffolded instruction.
Original abstract
This paper presents a conceptual framework for AI literacy, a hypothesized learning progression, and assessment design principles for advancing AI literacy among K–12 learners. Recognizing the importance of technical competencies alongside ethical awareness, the framework integrates foundational knowledge, societal implications, and practical applications of AI. Key competencies include ethical decision-making, AI-powered collaboration, and critical evaluation of AI outputs. Developed through an evidence-centered design (ECD) process involving a review of existing literature and frameworks, the proposed AI literacy framework and progression maps a hypothesized trajectory of students’ skill development, providing a structured pathway for improvement with behavior indicators connected to core AI literacy subskills. In this way, the framework and progression may offer educators a roadmap to apply scaffolded and differentiated teaching strategies that actively foster learners’ skill acquisition. To further support connections between assessment and instruction, we introduce three design principles for task design: ensuring relevance to learners, minimizing barriers to resource access, and providing opportunities for skill advancement. These design principles may guide the creation of activities that evaluate and enhance students’ AI literacy. By aligning scaffolded assessments and learning activities with the progression, this framework bridges instruction, assessment, and students’ skill development. It ultimately may be used to support students in developing skills to critically and ethically engage with AI technologies, preparing them to navigate the digital landscape by fostering inclusive instruction that deepens students’ understanding of AI concepts. Chakraburty, S., Ober, T. M., & Liu, L. (2025). Preparing K–12 students with AI literacy: Proposed framework, progression, and task design principles (Research Report No. RR-25-14). ETS. https://doi.org/10.64634/46jn1p41