Learning progression-derived rubrics produce AI feedback on student science writing that matches expert rubric quality in key dimensions.
Focus on Formative Feedback
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LLM-based multimodal feedback matches educator feedback in learning outcomes but exceeds it in student perceptions of quality, engagement, and reduced cognitive load.
The chapter synthesizes the history of adaptive learning systems and examines how AI can provide instructional intelligence and real-time adaptivity in serious games while highlighting challenges such as explainability and limited long-term outcome data.
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Using Learning Progressions to Guide AI Feedback for Science Learning
Learning progression-derived rubrics produce AI feedback on student science writing that matches expert rubric quality in key dimensions.
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LLM-based Multimodal Feedback Produces Equivalent Learning and Better Student Perceptions than Educator Feedback
LLM-based multimodal feedback matches educator feedback in learning outcomes but exceeds it in student perceptions of quality, engagement, and reduced cognitive load.
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AI-Enabled Serious Games: Integrating Intelligence and Adaptivity in Training Systems
The chapter synthesizes the history of adaptive learning systems and examines how AI can provide instructional intelligence and real-time adaptivity in serious games while highlighting challenges such as explainability and limited long-term outcome data.