Diana Akrong
Diana Akrong is a UX Researcher in the Research and Machine Intelligence department. She and her team, People + AI Research (PAIR), are devoted to advancing the research and design of people-centric AI systems.
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An experimental evaluation of an AI-powered interactive learning platform
Nicole Miller
Yael Haramaty
Lidan Hackmon
Lior Belinsky
Abraham Oritz Tapia
Lucy Tootill
Scott Siebert
Frontiers in Artificial Intelligence (2026) (to appear)
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Generative AI, which is capable of transforming static content into dynamic learning experiences, holds the potential to revolutionize student engagement in educational contexts. However, questions still remain around whether or not these tools are effective at facilitating student learning. In this research, we test the effectiveness of an AI-powered platform incorporating multiple representations and assessment through Learn Your Way, an experimental research platform that transforms textbook chapters into dynamic visual and audio representations. Through a between-subjects, mixed methods experiment with 60 US-based students, we demonstrate that students who used Learn Your Way had a more positive learning experience and had better learning outcomes compared to students learning the same content through a digital textbook. These findings indicate that AI-driven tools, capable of providing choice among interactive representations of content, constitute an effective and promising method for enhancing student learning.
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"Everyone wants to do the model work, not the data work": Data Cascades in High-Stakes AI
Nithya Sambasivan
Shivani Kapania
Hannah Highfill
Praveen Kumar Paritosh
SIGCHI, ACM (2021)
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AI models are increasingly applied in high-stakes domains like health and conservation. Data quality carries an elevated significance in high-stakes AI due to its heightened downstream impact, impacting predictions like cancer detection, wildlife poaching, and loan allocations. Paradoxically, data is the most under-valued and de-glamorised aspect of AI. In this paper, we report on data practices in high-stakes AI, from interviews with 53 AI practitioners in India, East and West African countries, and USA. We define, identify, and present empirical evidence on Data Cascades---compounding events causing negative, downstream effects from data issues---triggered by conventional AI/ML practices that undervalue data quality. Data cascades are pervasive (92% prevalence), invisible, delayed, but often avoidable. We discuss HCI opportunities in designing and incentivizing data excellence as a first-class citizen of AI, resulting in safer and more robust systems for all.
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Artificial intelligence (AI) offers opportunities to solve complex problems facing smallholder farmers in the Global South. However, there is currently a dearth of research and resources available to organizations and policy-makers for building farmer-centered AI systems. As technologists, we believe it is our responsibility to draw from and contribute to research on farmers’ needs, practices, value systems, social worlds, and daily agricultural ecosystem realities. Drawing from our own fieldwork experience and scholarship, we propose concrete future directions for building AI solutions and tools that are meaningful to farmers and will significantly improve their lives. We also discuss tensions that may arise when incorporating AI into farming ecosystems. We hope that a closer look into these research areas will serve as a guide for technologists looking to leverage AI to help smallholder farmers in the Global South.
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AI is powerful and has the potential to deliver many benefits to the Nigerian economy. As such, the government needs to play an important role in partnering with industry and the community to ensure its deployment is safe, fair, and produces positive outcomes. Given the early stage of AI development in Nigeria, we believe it’s important to make sure that policy makers have a clear and consistent understanding of the current state of AI in Nigeria-- the state of current laws and regulations as it applies to AI, current applications of AI, and the challenges AI presents on a policy level. We also present areas where the government, in collaboration with wider civil society and AI practitioners, can play a crucial role in advancing AI in Nigeria. We hope this paper can help in evolving the discussion to address policy ideas and implementation of AI in Nigeria.
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