Abstract
One of the primary obstacles to improving middle school math achievement is lack of equitable access to high-quality learning opportunities. Human delivery of high-dosage tutoring can bring significant learning gains, but students, particularly economically disadvantaged students, have limited access to well-trained tutors. Augmenting human tutor abilities through the use of artificial intelligence (AI) technology is one way to scale up access to tutors without compromising learning quality. This workshop aims to highlight the challenges and opportunities of AI-in-the-loop math tutoring and encourage discourse in the AIED community to develop human-AI hybrid tutoring and teaching systems. We invite papers that provide clearer understanding and support the progress of human and AI-assisted personalized learning technologies. The structure of this full-day hybrid workshop will include presentations of accepted papers, small or whole group discussion, and a panel discussion focusing on common themes related to research and application, key takeaways, and findings imperative to increasing middle school math learning.
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Aleven, V. et al. (2023). Towards the Future of AI-Augmented Human Tutoring in Math Learning. In: Wang, N., Rebolledo-Mendez, G., Dimitrova, V., Matsuda, N., Santos, O.C. (eds) Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky. AIED 2023. Communications in Computer and Information Science, vol 1831. Springer, Cham. https://doi.org/10.1007/978-3-031-36336-8_3
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DOI: https://doi.org/10.1007/978-3-031-36336-8_3
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