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Creating effective educational materials generally requires expensive and time-consuming studies of student learning outcomes.
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The expertise reversal effect
S. Kalyuga, P. Ayres, P. Chandler, and J. Sweller · 2003
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Evaluating a simulated student using real students data for training and testing
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Learning by teaching simstudent: Technical accomplishments and an initial use with students
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How do instructional designers evaluate? a qualitative study of evaluation in practice
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Constitutional ai: Harmlessness from ai feedback
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J. Achiam, S. Adler, S. Agarwal, L. Ahmad, I. Akkaya, F. L. Aleman, D. Almeida, J. Altenschmidt, S. Altman, S. Anadkat, et al · 2023
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M. Pankiewicz and R. S. Baker · 2023
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Learning gain differences between chatgpt and human tutor generated algebra hints
Z. A. Pardos and S. Bhandari · 2023
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Generative agents: Interactive simulacra of human behavior
J. S. Park, J. O’Brien, C. J. Cai, M. R. Morris, P. Liang, and M. S. Bernstein · 2023
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Using large language models to simulate multiple humans and replicate human subject studies
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Out of one, many: Using language models to simulate human samples
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Sparks of artificial general intelligence: Early experiments with gpt-4
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Solving math word problems by combining language models with symbolic solvers
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Large language models as simulated economic agents: What can we learn from homo silicus?
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" teach ai how to code": Using large language models as teachable agents for programming education
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T. Phung, V.-A. Pădurean, A. Singh, C. Brooks, J. Cambronero, S. Gulwani, A. Singla, and G. Soares · 2023
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Comparing different approaches to generating mathematics explanations using large language models
E. Prihar, M. Lee, M. Hopman, A. T. Kalai, S. Vempala, A. Wang, G. Wickline, A. Murray, and N. Heffernan · 2023
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Automatic prompt optimization with" gradient descent" and beam search
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Rehearsal: Simulating conflict to teach conflict resolution
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R. E. Wang and D. Demszky · 2023
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Step-by-step remediation of students’ mathematical mistakes
R. E. Wang, Q. Zhang, C. Robinson, S. Loeb, and D. Demszky · 2023
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Large language models as optimizers
C. Yang, X. Wang, Y. Lu, H. Liu, Q. V. Le, D. Zhou, and X. Chen · 2023
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Self-taught optimizer (stop): Recursively self-improving code generation
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Do language models exhibit the same cognitive biases in problem solving as human learners?
A. Opedal, A. Stolfo, H. Shirakami, Y. Jiao, R. Cotterell, B. Schölkopf, A. Saparov, and M. Sachan · 2024
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Physics on autopilot: exploring the use of an ai assistant for independent problem-solving practice
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