Tootfinder

I am so glad that other people are doing the hard work of proving that calling LLM's "AI" is bullshit and having them write code is just plain dumb. https://infosec.exchange/@teriradichel/115033555848878022

Enhancing Critical Thinking in Generative AI Search with Metacognitive Prompts
Anjali Singh, Zhitong Guan, Soo Young Rieh
https://arxiv.org/abs/2505.24014 …

Enhancing Critical Thinking in Generative AI Search with Metacognitive Prompts
The growing use of Generative AI (GenAI) conversational search tools has raised concerns about their effects on people's metacognitive engagement, critical thinking, and learning. As people increasingly rely on GenAI to perform tasks such as analyzing and applying information, they may become less actively engaged in thinking and learning. This study examines whether metacognitive prompts - designed to encourage people to pause, reflect, assess their understanding, and consider multiple perspec…

Integrating Universal Generative AI Platforms in Educational Labs to Foster Critical Thinking and Digital Literacy
Vasiliy Znamenskiy, Rafael Niyazov, Joel Hernandez
https://arxiv.org/abs/2507.00007

Integrating Universal Generative AI Platforms in Educational Labs to Foster Critical Thinking and Digital Literacy
This paper presents a new educational framework for integrating generative artificial intelligence (GenAI) platforms such as ChatGPT, Claude, and Gemini into laboratory activities aimed at developing critical thinking and digital literacy among undergraduate students. Recognizing the limitations and risks of uncritical reliance on large language models (LLMs), the proposed pedagogical model reframes GenAI as a research subject and cognitive tool. Students formulate discipline-specific prompts a…

EduThink4AI: Translating Educational Critical Thinking into Multi-Agent LLM Systems
Xinmeng Hou, Zhouquan Lu, Wenli Chen, Hai Hu, Qing Guo
https://arxiv.org/abs/2507.15015

EduThink4AI: Translating Educational Critical Thinking into Multi-Agent LLM Systems
Large language models (LLMs) have demonstrated significant potential as educational tutoring agents, capable of tailoring hints, orchestrating lessons, and grading with near-human finesse across various academic domains. However, current LLM-based educational systems exhibit critical limitations in promoting genuine critical thinking, failing on over one-third of multi-hop questions with counterfactual premises, and remaining vulnerable to adversarial prompts that trigger biased or factually in…