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The rapid growth of generative AI in education has deepened the need for both responsible AI practices and AI literacy. However, these domains are often addressed separately, leaving a gap in guidance for educators, designers, developers, and researchers. This paper presents a conceptual framework that connects responsible AI principles with AI literacy and introduces its application through a practical checklist as a toolkit, with a hypothetical case example, which offers actionable prompts for responsible use of AI in educational and design contexts. Together, the TEACH-RAI framework and toolkit aim to provide both theoretical grounding and accessible resources to foster responsible engagement with AI in the educational context. 

As generative artificial intelligence (AI) continues to transform education, most existing AI benchmarks focus primarily on technical metrics (e.g., speed, accuracy) while overlooking human identity, agency, and ethical considerations. In this paper, we present TEACH-AI (Trustworthy and Effective AI Classroom Heuristics)—a domain-independent, pedagogically grounded, and stakeholder-aligned benchmark framework with measurable indicators and a practical toolkit to guide the design, development, and evaluation of generative AI systems in educational contexts. Built on an extensive literature review and synthesis, the ten-component assessment framework and toolkit checklist provide a foundation for scalable and value-aligned AI evaluation in education. The framework rethinks “evaluation” through sociotechnical, educational, theoretical, and applied lenses, engaging designers, developers, researchers, and policymakers across AI and education. Our work invites the community to reconsider what constitutes “effective” AI in education and to design model evaluations that promote co-creation, inclusivity, and long-term human, social, and educational impact. 

Not AvailableThe intersection of dance and artificial intelligence presents fertile ground for exploring human-machine interaction, co-creation, and embodied expression. This paper reports on a seven month four-phase collaboration with fifteen dancers from a university dance department, encompassing a preliminary study, redesign of LuminAI-a co-creative AI dance partner-, a contextual diary study, and a culminating public performance. Thematic analysis of responses revealed LuminAI’s impact on dancers’ perceptions, improvisational practices, and creative exploration. By blending human and AI interactions, LuminAI influenced dancers’ practices by pushing them to explore the unexpected, fostering deeper self-awareness, and enabling novel choreographic pathways. The experience reshaped their creative sub processes, enhancing their spatial awareness, movement vocabulary, and openness to experimentation. Our contributions underscore the potential of AI to not only augment dancers’ immediate improvisational capabilities but also to catalyze broader transformations in their creative processes, paving the way for future systems that inspire and amplify human creativity. 

Expressive computer science (CS) learning environments teach coding through the creation of an artifact, such as audio or video output. EarSketch is an expressive CS learning environment designed to teach computing through music production, mixing and arranging sounds using code. In this paper, we explore the accessibility challenges of using EarSketch for learners who are Blind and Visually Impaired (BVI). We present key findings from co-design studies with teachers and students at an institution specializing in BVI education, focused on gathering both groups’ unique perspectives about EarSketch’s ability to support teachers’ curricula, students’ workflows using the system with accessibility software, and challenges faced by users who are BVI. 

This study conducts a novel approach to redesign EarSketch, an expressive computer science (CS) learning environment that inte- grates music composition into computing education, with a specific focus on inclusivity for blind and visually impaired (BVI) learners. This approach centers on the participation of teachers, students, and the community as co-designers, leveraging their insights and ex- periences to enhance the program’s accessibility and effectiveness. By actively involving the stakeholders in the development process, the study aims to address the unique educational challenges and needs of learners who are visually impaired more effectively. The participatory design approach is expected to not only maintain the intrinsic appeal of EarSketch but also to expand its accessibility, ensuring that it becomes a more inclusive tool in computer sci- ence education. The ultimate goal is to establish a more adaptable and inclusive educational paradigm within STEAM, particularly in computing education and music, that is responsive to the diverse needs of all students, including those with visual impairments. The contributions of this paper are design recommendations based on our data that can be applied to the design of EarSketch and other expressive CS environments for BVI learners.