responsible-ai
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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
The paper proposes a Human-Centered Conceptual Framework for responsible AI implementation in K-12 music education, drawing on emerging U.S. educational priorities such as AI literacy, data privacy, and equitable access. It argues that AI tools can support adaptive practice and composition, but their value depends on preserving pedagogical integrity and teacher agency. The framework positions AI as a support system within an ecosystem shaped by pedagogy, educator leadership, ethical governance, and professional capacity building.
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.
- PaperComputers and Education: Artificial Intelligence14 Jul 2026
Balancing AI responsibility with privacy, safety, and utility: Unlearning in large language models for mathematics education
Chenglu Li, Gökhan Gülfidan, Yinqi Zhang-Kopf
This study applies gradient-based LLM unlearning to reduce personally identifiable information (PII) and harmful content in math tutoring models while maintaining performance on math tasks. Results show substantial decreases in PII and harm rates without sacrificing utility, demonstrating a path toward responsible AI in education.
Original abstract
Online mathematics learning platforms are increasingly adopting large language models (LLMs) to provide scalable, on-demand support, but these models may reproduce private information from training data or generate harmful language. This raises concerns about responsibility in educational settings regarding the use of pre-trained models. LLM unlearning is an emerging area for reducing a model’s ability to produce specific unwanted content and remains underexplored in educational research. This study aims to investigate how LLM unlearning reduces the model's reliance on personally identifiable information (PII) and inappropriate content in the math tutoring context, while maintaining the model's utility on both single-label and multi-label downstream math tasks. We applied a gradient-based LLM unlearning approach to three different models, which were pre-trained on approximately 3 million data points from an Algebra I online discussion forum between students and professional tutors. PII and harmful content were detected on this training data and used for unlearning in two different orders (PII and harmful content unlearning). Then, the generated outputs from these two unlearning models were compared with those of the pre-trained model in terms of PII-containing output rate and harmful rate. Moreover, unlearned models were evaluated on two different math classification tasks. The results showed that the rates of PII-containing output rate and harmfulness substantially decreased compared to the pre-trained models, and the utility of the unlearned model was still maintained. These findings demonstrate how LLM unlearning can be applied to pre-trained models to behave them more responsibly, while maintaining strong model performance on math-related tasks.