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Empathy is a driving force in our connection to others, our mental well-being, and resilience to challenges. With the rise of generative artificial intelligence (AI) systems, mental health chatbots, and AI social support companions, it is important to understand how empathy unfolds toward stories from human versus AI narrators and how transparency plays a role in user emotions. We aim to understand how empathy shifts across human-written versus AI-written stories, and how these findings inform ethical implications and human-centered design of using mental health chatbots as objects of empathy. We conducted crowd-sourced studies with 985 participants who each wrote a personal story and then rated empathy toward 2 retrieved stories, where one was written by a language model, and another was written by a human. Our studies varied disclosing whether a story was written by a human or an AI system to see how transparent author information affects empathy toward the narrator. We conducted mixed methods analyses: through statistical tests, we compared user's self-reported state empathy toward the stories across different conditions. In addition, we qualitatively coded open-ended feedback about reactions to the stories to understand how and why transparency affects empathy toward human versus AI storytellers. We found that participants significantly empathized with human-written over AI-written stories in almost all conditions, regardless of whether they are aware (t =7.07, P<.001, Cohen d=0.60) or not aware (t =3.46, P<.001, Cohen d=0.24) that an AI system wrote the story. We also found that participants reported greater willingness to empathize with AI-written stories when there was transparency about the story author (t =-5.49, P<.001, Cohen d=0.36). Our work sheds light on how empathy toward AI or human narrators is tied to the way the text is presented, thus informing ethical considerations of empathetic artificial social support or mental health chatbots.

Purpose This study evaluates the effectiveness of a machine learning (ML) integrated science curriculum implemented within the Science Research Mentorship Program (SRMP) for high school youth at the American Museum of Natural History (AMNH) over 2 years. The 4-week curriculum focused on ML knowledge gain, skill development, and self-efficacy, particularly for under-represented youth in STEM. Background ML is increasingly prevalent in STEM fields, making early exposure to ML methods and artificial intelligence (AI) literacy crucial for youth pursuing STEM careers. However, STEM fields, particularly those focused on AI research and development, suffer from a lack of diversity. Learning experiences that support the participation of under-represented groups in STEM and ML are essential to addressing this gap. Results Participant learning was assessed through pre- and post-surveys measuring ML knowledge, skills, and self-efficacy. Results from the implementation of the curriculum show that participants gained understanding of ML knowledge and skills ( p  < 0.001, d  = 1.083) and self-efficacy in learning ML concepts ( p  = 0.004, d  = 0.676). On average, participants who identified as female and non-white showed greater learning gains than their white male peers (ML knowledge: p  < 0.001, d  = 1.191; self-efficacy: p  = 0.006, d  = 0.631), decreasing gaps in ML knowledge, skills, and self-efficacy identified in pre-survey scores. Conclusions The ML-integrated curriculum effectively enhances students’ understanding and confidence in ML concepts, especially for under-represented groups in STEM, and provides a model for future ML education initiatives in informal science settings. We suggest that policy makers and school leaders take into account that high school age youth can learn ML concepts through integrated curricula while maintaining an awareness that curriculum effectiveness varies across demographic groups.

The rapid expansion of artificial intelligence (AI) necessitates promoting AI education at the K-12 level. However, educating young learners to become AI literate citizens poses several challenges. The components of AI literacy are ill-defined and it is unclear to what extent middle school students can engage in learning about AI as a sociotechnical system with socio-political implications. In this paper we posit that students must learn three core domains of AI: technical concepts and processes, ethical and societal implications, and career futures in the AI era. This paper describes the design and implementation of the Developing AI Literacy (DAILy) workshop that aimed to integrate middle school students’ learning of the three domains. We found that after the workshop, most students developed a general understanding of AI concepts and processes (e.g., supervised learning and logic systems). More importantly, they were able to identify bias, describe ways to mitigate bias in machine learning, and start to consider how AI may impact their future lives and careers. At exit, nearly half of the students explained AI as not just a technical subject, but one that has personal, career, and societal implications. Overall, this finding suggests that the approach of incorporating ethics and career futures into AI education is age appropriate and effective for developing AI literacy among middle school students. This study contributes to the field of AI Education by presenting a model of integrating ethics into the teaching of AI that is appropriate for middle school students.

Artificial Intelligence (AI) is revolutionizing many industries and becoming increasingly ubiquitous in everyday life. To empower children growing up with AI to navigate society’s evolving sociotechnical context, we developed three middle school AI literacy curricula: Creative AI, Dancing with AI, and How to Train Your Robot. In this paper we discuss how we leveraged three design principles—active learning, embedded ethics, and low barriers to access – to effectively engage students in learning to create and critique AI artifacts. During the summer of 2020, we recruited and trained in-service, middle school teachers from across the United States to co-instruct online workshops with students from their schools. In the workshops, a combination of hands-on unplugged and programming activities facilitated students’ understanding of AI. As students explored technical concepts in tandem with ethical ones, they developed a critical lens to better grasp how AI systems work and how they impact society. We sought to meet the specified needs of students from a range of backgrounds by minimizing the prerequisite knowledge and technology resources students needed to participate. Finally, we conclude with lessons learned and design recommendations for future AI curricula, especially for K-12 in-person and virtual learning.

Children's early development of creativity contributes to their learning outcomes and personal growth. However, as children enter formal schooling systems, their creativity declines. Although Artificial Intelligence (AI)-powered tools for K-12 learning hold immense potential for reducing barriers to creative expression, access to these AI tools and AI knowledge among K-12 students and educators remains inequitable to children from groups underrepresented in STEM. In this thesis, I explore how AI, as an emerging creative medium, can be made more accessible to all young creators. I explore two mechanisms of making a mode of creation more accessible: Creative AI literacy materials for diverse classrooms and AI agentic interactions for scaffolding creative expression for diverse learners.Utilizing literacy as a mode of making Creative AI tools accessible, I outline the design and evaluation of various Creative AI curricula that I have developed for diverse groups of K-12 students and teachers. To adapt AI learning to art classrooms, I co-developed the AI and Art curriculum with creative educators, designed specifically for use in creative classrooms with creative educators and learners. I implemented the curriculum with 94 middle and high school students across six week-long sessions. I report findings from teacher co-design sessions and students’ learning experiences. Teachers designed learning objectives and AI tools for their classrooms. Students gained knowledge and skills in art concepts, AI concepts, and the application of art in AI. Students also demonstrated significant shifts in their attitudes towards using AI in the creative process, and their sense of belonging in both AI and art communities was heightened. I discuss how AI curricula can be adapted to diverse disciplines and how art can serve as a meaningful avenue for students to engage with AI concepts.Utilizing social interaction from AI agents as a mode of fostering creative expression in children with neurodevelopmental disorders, I designed and applied inclusive child-robot interactions for collaborative creativity, where 32 elementary school children and a social robot collaboratively created picture stories. The robot provided creativity scaffolding during different parts of the creative storytelling process through social interactions such as feedback, question-asking, divergent thinking, and positive reinforcement, while personalizing the scaffolding to meet the unique needs of neurodivergent children. I investigated the impact of the social robot on children’s exhibited creativity and their emergent creative collaborative interactions in storytelling over multiple sessions. Inclusive design practices eliminated creative barriers for children with neurodevelopmental disorders, and the robot's creativity scaffolding interactions positively influenced children’s creative product and creative process in storytelling. I propose Inclusive Co-creative Child-robot Interaction (ICCRI) guidelines for fostering creativity in children with neurodevelopmental disorders, and accommodating diverse creator styles in complex, open-ended creative tasks.In this thesis, I contribute curricula, learning tools, child-robot interactions, and findings from examining long-term child-AI co-creative interactions. I discuss design implications for integrating AI tools, curricula and agents in creative learning environments. This thesis is a step towards empowering all children with powerful modes of creation, while helping them be responsible creators, thinkers and citizens in an AI-driven future.

Children's creativity - the ability to come up with novel, surprising, and valuable ideas - has been known to contribute to their learning outcomes and personal growth. Standardized ways to measure creativity and divergent thinking reported that as children enter elementary school, their creativity slumps and thinking becomes more convergent, especially around the 4th grade. One cause for this is school curricula become more structured and lose the aspect of creative play. This is especially concerning for kids growing up in the era of Artificial Intelligence, where mechanical and repetitive jobs that require structured thinking move to machines. To be successful in this world of intelligent agents, we must empower children not only to understand how these intelligent agents work, but also to be able to think creatively about generating new artifacts in consort with such agents, which requires imaginative novel thought.In this thesis, I explore whether a social robot's interaction with children can be an effective way to help children think more creatively. I suggest two ways in which robots used as pedagogical tools can help children think more creatively are: 1. through artificial creativity demonstration, such as showing the use of novel ideas, and 2. through offering creativity scaffolding, such as asking reflective questions, validating novel ideas, and engaging in creative conflict.I designed four collaborative game-based activities that involve child-robot interaction and afford different forms of creative expression: 1. Droodle Game, which affords verbal creativity, 2. Magic Draw, which affordsfigural creativity, 3. WeDo Construction with Jibo, which affords construction creativity and 4. Escape Adventure, which affords divergent thinking and creative problem solving. I designed the behavior of the robot such that it either scaffolds the child for creative thinking, or the robot gives the appearance of creative thinking by artificially emulating human creativity. I evaluated the role of the social robot in influencing children's creativity by running comparative studies between children playing these creativity games while interacting with the robot with creativity-inducing behaviors (creative condition), and without creativity-inducing behaviors (non-creative condition). Children who interacted with the creative robot exhibited higher levels of creativity than children who interacted with a non-creative control robot. I conclude that children can model a social robotic peer's creative expression via social emulation. When scaffolded for creativity, children exhibited higher levels of creativity. This enabled me to develop a robot scaffolding paradigm which fosters creativity in young children.This thesis contributes design guidelines for child-robot interactions which promote creative thinking, and provides evidence that these creativity inducing behaviors exhibited by social robots can foster creativity in young children.

Art created using generated Artificial Intelligence has taken the world by storm and generated excitement for many digital creators and technologists. However, the reception and reaction from artists have been mixed. Concerns about plagiarizing their artworks and styles for datasets and uncertainty around the future of digital art sparked movements in artist communities shunning the use of AI for generating art and protecting artists' rights. Collaborating with these tools for novel creative use cases also sparked hope from some creators. Artists are an integral stakeholder in the rapidly evolving digital creativity industry and understanding their concerns and hopes inform responsible development and use of creativity support tools. In this work, we study artists' sentiments about AI-generated art. We interviewed 7 artists and analyzed public posts from artists on social media platforms Reddit, Twitter and Artstation. We report artists' main concerns and hopes around AI-generated artwork, informing a way forward for inclusive development of these tools.

We present MAATS, a Multi Agent Automated Translation System that leverages the Multidimensional Quality Metrics (MQM) framework as a fine-grained signal for error detection and refinement. MAATS employs multiple specialized AI agents, each focused on a distinct MQM category (e.g., Accuracy, Fluency, Style, Terminology), followed by a synthesis agent that integrates the annotations to iteratively refine translations. This design contrasts with conventional single-agent methods that rely on self-correction. Evaluated across diverse language pairs and Large Language Models (LLMs), MAATS outperforms zero-shot and single-agent baselines with statistically significant gains in both automatic metrics and human assessments. It excels particularly in semantic accuracy, locale adaptation, and linguistically distant language pairs. Qualitative analysis highlights its strengths in multi-layered error diagnosis, omission detection across perspectives, and context-aware refinement. By aligning modular agent roles with interpretable MQM dimensions, MAATS narrows the gap between black-box LLMs and human translation workflows, shifting focus from surface fluency to deeper semantic and contextual fidelity.

Independent learners often struggle with sustaining focus and emotional regulation in unstructured or distracting settings. Although some rely on ambient aids such as music, ASMR, or visual backgrounds to support concentration, these tools are rarely integrated into cohesive, learner-centered systems. Moreover, existing educational technologies focus primarily on content adaptation and feedback, overlooking the emotional and sensory context in which learning takes place. Large language models have demonstrated powerful multimodal capabilities including the ability to generate and adapt text, audio, and visual content. Educational research has yet to fully explore their potential in creating personalized audiovisual learning environments. To address this gap, we introduce an AI-powered system that uses LLMs to generate personalized multisensory study environments. Users select or generate customized visual themes (e.g., abstract vs. realistic, static vs. animated) and auditory elements (e.g., white noise, ambient ASMR, familiar vs. novel sounds) to create immersive settings aimed at reducing distraction and enhancing emotional stability. Our primary research question investigates how combinations of personalized audiovisual elements affect learner cognitive load and engagement. Using a mixed-methods design that incorporates biometric measures and performance outcomes, this study evaluates the effectiveness of LLM-driven sensory personalization. The findings aim to advance emotionally responsive educational technologies and extend the application of multimodal LLMs into the sensory dimension of self-directed learning.

Can visual artworks created using generative visual algorithms inspire human creativity in storytelling? We asked writers to write creative stories from a starting prompt, and provided them with visuals created by generative AI models from the same prompt. Compared to a control group, writers who used the visuals as story writing aid wrote significantly more creative, original, complete and visualizable stories, and found the task more fun. Of the generative algorithms used (BigGAN, VQGAN, DALL-E, CLIPDraw), VQGAN was the most preferred. The control group that did not view the visuals did significantly better in integrating the starting prompts. Findings indicate that cross modality inputs by AI can benefit divergent aspects of creativity in human-AI co-creation, but hinders convergent thinking.