Learning desk

MultiAgent EDU StackGather good sources. Teach what matters.
T3Mermaid to ASCII art (mermaid-ascii)T3Kimi K3, and what we can still learn from the pelican benchmarkT3Firefox in WebAssemblyT3Spot birds not golfT3[AINews] Kimi K3 2.8T-A50B: the largest open model ever released; Opus 4.8-class at Sonnet 5 pricingT1From physical surfaces to human-centric heat stress: LST and UTCI heat mapping reveals nonlinear effects of urban morphologyT1DualHNIE: Dual-Channel Hypergraph Learning for Node Importance Estimation in Heterogeneous Knowledge GraphsT1GenTL: A General Transfer Learning Model for Building Thermal DynamicsT1A short review on the maximum clique problem algorithms with classical, AI, and quantum methodsT1Man, Machine, and Masterpiece: Artistic Ownership in the AI EraT1HABIB_TAZ at SemEval-2026 Task 11: Disentangling Formal Logic from Content via Synthetic Training and Multi-Objective OptimizationT1How Well Does AI-Generated Feedback Work? Intrinsic and Extrinsic Evaluation across more than 20,000 EFL Essay DraftsT3Mermaid to ASCII art (mermaid-ascii)T3Kimi K3, and what we can still learn from the pelican benchmarkT3Firefox in WebAssemblyT3Spot birds not golfT3[AINews] Kimi K3 2.8T-A50B: the largest open model ever released; Opus 4.8-class at Sonnet 5 pricingT1From physical surfaces to human-centric heat stress: LST and UTCI heat mapping reveals nonlinear effects of urban morphologyT1DualHNIE: Dual-Channel Hypergraph Learning for Node Importance Estimation in Heterogeneous Knowledge GraphsT1GenTL: A General Transfer Learning Model for Building Thermal DynamicsT1A short review on the maximum clique problem algorithms with classical, AI, and quantum methodsT1Man, Machine, and Masterpiece: Artistic Ownership in the AI EraT1HABIB_TAZ at SemEval-2026 Task 11: Disentangling Formal Logic from Content via Synthetic Training and Multi-Objective OptimizationT1How Well Does AI-Generated Feedback Work? Intrinsic and Extrinsic Evaluation across more than 20,000 EFL Essay Drafts
← Dispatches

Educators’ Discussion with a Generative Artificial Intelligence Trainer

Primary research

#304

T1digested
Topic
AI Pedagogy and Assessment
First seen
2026-07-16 23:33:00
Last seen
2026-07-16 23:33:00

Source raw items (1)

  • Semantic Scholar2026-07-16 23:31:49
    Educators’ Discussion with a Generative Artificial Intelligence Trainer

    Educators have investigated the impact of generative Artificial Intelligence (genAI) tools, such as ChatGPT and Large Language Models (LLMs), on curriculum development in K-12 schools. Still, little is known about the development of these tools and their impact on the future of education. Therefore, a teacher educator conducted a duoethnography to investigate these problems with a genAI trainer, specifically a Reinforcement Learning from Human Feedback (RLHF) Administrator, to answer the following questions. How does an RLHF administrator train genAI tools? What should mathematics educators know based on this training? What impact could this new technology have on the future of education? The data indicate that errors and error-finding are central parts of training LLMs. Since humans train the genAI, it inherits their biases. We learned that genAI was created to be people pleasing which can override its ability to find truth. We learn how it supports error analysis in Large Language Models, and how the public should view these tools. Finally, we describe what RLHF administrators view as the role of genAI in education, what the public should be aware of, and the future of the genAI market. The data highlights the need for teachers to consider the impact of profit motivation, regulation, and economic implications on genAI tools.