A Guiding Framework for K-12 Teachers in Creating AI-powered Learning Technologies through Vibe Coding

• URL: http://arxiv.org/abs/2607.05406v1
• Date: Tue, 09 Jun 2026 00:08:23 GMT
• Title: A Guiding Framework for K-12 Teachers in Creating AI-powered Learning Technologies through Vibe Coding
• Abstract summary: Large language models generate code from natural language prompts.<n>"vibe coding" allows non-programmers to develop computational solutions.<n>We propose GAIDE, a framework that supports teachers in creating AI-powered learning technologies through vibe coding.
• Score: 4.176758013264753
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models generate code from natural language prompts, enabling "vibe coding," which allows non-programmers to develop computational solutions. Vibe coding for teachers amplifies the value of teachers-as-designers, improving technology integration while fostering AI literacy. However, structured guidance on supporting this process is lacking. We propose GAIDE (A Guiding Framework for AI-Integrated Design for Educators), a framework that supports K-12 teachers in creating AI-powered learning technologies through vibe coding. The initial framework, built on Design Thinking and INTERACT, was validated through a CORDTRA interaction analysis of three teachers and four faculty mentors in an eight-week workshop to derive the final framework. Additionally, the qualitative analysis of pre- and post-interviews found an enhancement of teachers' AI literacy. Findings highlight the potential of learning-by-creating for professional development.

Related papers

• Beyond AI Delegation: A Prompt Pattern Framework for Productive Struggle and Evaluative Judgement in Secure Coding Education [0.45706494756895794]
  Large language models make it easy for students to delegate writing, analysis, and problem-solving to automated systems.<n>We introduce a practical framework that helps educators keep GenAI in the course without removing the cognitive demands that make it worthwhile.
  arXiv  Detail & Related papers  (2026-05-23T07:35:49Z)
• Reshaping Undergraduate Computer Science Education in the Generative AI Era [85.78707817763434]
  Generative AI represents a turning point for Computer Science (CS) education.<n>To keep pace with advances in AI technology, CS curricula should consider a shift toward understanding and verifying AI-generated artifacts.<n>We believe that to effectively prepare future computer science graduates, capable of creating, solving problems, and managing, as well as co-creating, artifacts with AI.
  arXiv  Detail & Related papers  (2026-05-02T05:26:24Z)
• Generative Teaching via Code [43.45801837806965]
  TeachMaster orchestrates a collaborative team of agents--spanning planning, design, and rendering--to automate the production of interpretable, editable, and curriculum-ready educational videos.<n>Experiments validate that TeachMaster significantly boosts production efficiency without compromising structural coherence or visual fidelity.
  arXiv  Detail & Related papers  (2025-12-07T12:52:24Z)
• EduDial: Constructing a Large-scale Multi-turn Teacher-Student Dialogue Corpus [59.693733170193944]
  We present EduDial, a comprehensive multi-turn teacher-student dialogue dataset.<n>EduDial covers 345 core knowledge points and consists of 34,250 dialogue sessions generated through interactions between teacher and student agents.
  arXiv  Detail & Related papers  (2025-10-14T18:18:43Z)
• Generative AI in Training and Coaching: Redefining the Design Process of Learning Materials [44.99833362998488]
  We explore how AI integrates into the design process of learning materials, assessing its impact on efficiency, pedagogical quality, and the evolving role of human trainers and coaches.<n>Through qualitative interviews with professionals in education and corporate training, we identify the following key topics.<n>We derive how tools based on GenAI can successfully be implemented for trainers and coaches on an individual, organizational, systemic, and strategic level.
  arXiv  Detail & Related papers  (2025-08-06T03:42:43Z)
• Decoding Instructional Dialogue: Human-AI Collaborative Analysis of Teacher Use of AI Tool at Scale [9.092920230987684]
  The integration of large language models into educational tools has the potential to substantially impact how teachers plan instruction.<n>This paper presents a human-AI collaborative methodology for large-scale qualitative analysis of over 140,000 educator-AI messages.
  arXiv  Detail & Related papers  (2025-07-23T23:23:38Z)
• From Recall to Reasoning: Automated Question Generation for Deeper Math Learning through Large Language Models [44.99833362998488]
  We investigated the first steps for optimizing content creation for advanced math.<n>We looked at the ability of GenAI to produce high-quality practice problems that are relevant to the course content.
  arXiv  Detail & Related papers  (2025-05-17T08:30:10Z)
• Enhancing AI-Driven Education: Integrating Cognitive Frameworks, Linguistic Feedback Analysis, and Ethical Considerations for Improved Content Generation [0.0]
  This paper synthesizes insights from four related studies to propose a comprehensive framework for enhancing AI-driven educational tools.<n>We integrate cognitive assessment frameworks, linguistic analysis of AI-generated feedback, and ethical design principles to guide the development of effective and responsible AI tools.
  arXiv  Detail & Related papers  (2025-05-01T06:36:21Z)
• "From Unseen Needs to Classroom Solutions": Exploring AI Literacy Challenges & Opportunities with Project-based Learning Toolkit in K-12 Education [0.3994567502796064]
  There is a growing need to equip K-12 students with AI literacy skills that extend beyond computer science.<n>This paper explores the integration of a Project-Based Learning (PBL) AI toolkit into diverse subject areas, aimed at helping educators teach AI concepts more effectively.
  arXiv  Detail & Related papers  (2024-12-23T03:31:02Z)
• From chalkboards to chatbots: SELAR assists teachers in embracing AI in the curriculum [41.94295877935867]
  SELAR is a framework designed to help teachers integrate artificial intelligence into their curriculum. In this paper, we assess the effectiveness of the framework through additional workshops organized with lecturers from the Hague University of Applied Sciences.
  arXiv  Detail & Related papers  (2024-10-17T05:40:59Z)
• Enhancing Instructional Quality: Leveraging Computer-Assisted Textual Analysis to Generate In-Depth Insights from Educational Artifacts [13.617709093240231]
  We examine how artificial intelligence (AI) and machine learning (ML) methods can analyze educational content, teacher discourse, and student responses to foster instructional improvement. We identify key areas where AI/ML integration offers significant advantages, including teacher coaching, student support, and content development. This paper emphasizes the importance of aligning AI/ML technologies with pedagogical goals to realize their full potential in educational settings.
  arXiv  Detail & Related papers  (2024-03-06T18:29:18Z)
• Creation and Evaluation of a Pre-tertiary Artificial Intelligence (AI) Curriculum [58.86139968005518]
  The Chinese University of Hong Kong (CUHK)-Jockey Club AI for the Future Project (AI4Future) co-created an AI curriculum for pre-tertiary education. A team of 14 professors with expertise in engineering and education collaborated with 17 principals and teachers from 6 secondary schools to co-create the curriculum. The co-creation process generated a variety of resources which enhanced the teachers knowledge in AI, as well as fostered teachers autonomy in bringing the subject matter into their classrooms.
  arXiv  Detail & Related papers  (2021-01-19T11:26:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.