Assessing higher-order thinking skills in large language models (LLMs) remains a fundamental challenge, especially in tasks that go beyond surface-level accuracy. In this work, we propose THiNK (Testing Higher-order Notion of Knowledge), a multi-agent, feedback-driven evaluation framework grounded in Bloom's Taxonomy. THiNK frames reasoning assessment as an iterative task of problem generation, critique, and revision, encouraging LLMs to think-aloud through step-by-step reflection and refinement. This enables a systematic evaluation of both lower-order (e.g., remember, understand) and higher-order (e.g., evaluate, create) thinking skills. We apply THiNK to seven state-of-the-art LLMs and perform a detailed cognitive analysis of their outputs. Results reveal that while models reliably perform lower-order categories well, they struggle with applying knowledge in realistic contexts and exhibit limited abstraction. Structured feedback loops significantly improve reasoning performance, particularly in higher-order thinking. Qualitative evaluations further confirm that THiNK-guided outputs better align with domain logic and problem structure. The code of our framework provides a scalable methodology for probing and enhancing LLM reasoning, offering new directions for evaluation grounded in learning science, which is available at our GitHub repository.

A Chatbot is becoming a mainstream tool that organizations use to streamline and automate communication. It's a hot skill whether your experience level is from a basic to advanced in python programming, chatbots and the mix of AI and machine learning make it incredibly versatile and inexpensive. Start here and learn the basics of how it works. A Chatbot is becoming a mainstream tool that organizations use to streamline and automate communication. It's a hot skill whether your experience level is from a basic to advanced in python programming, chatbots and the mix of AI and machine learning make it incredibly versatile and inexpensive. Start here and learn the basics of how it works.

In a recent survey, it is estimated that Machine Learning and AI alone have at least 1.4 million open jobs at the moment.

Pramod, 40, a software engineer with a large IT company, is having a mid-life crisis. Bored of writing hundreds of lines of code every day, he wants to quit the monotonous job and shift to a more challenging role that also gives him a good package. However, he is clueless on the right skill sets he needs to acquire. Pramod is not alone in this predicament. There are thousands of such mid-level software professionals who either want to make a course correction or have been told by their organisation to acquire new skills if they are to remain relevant.

AI, computational linguistics, computational vision, machine learning, and natural language processing skills receive some of the most eye-popping equity premiums. The following is a guest post by Kyle Holm (Partner, Pre-IPO Compensation Practice Leader at Radford) and Kelsey Owen (Director, Pre-IPO Compensation Practice at Radford). The race to build computers that act, see, speak, and think like humans is as competitive as ever. While some worry that artificial intelligence (AI) will someday lead to robots rampaging their way to world domination, AI-related startups have not stopped attracting intense interest from talent and capital. Just weeks ago, China-based AI startup SenseTime Group received one of the largest venture capital investment in the AI space -- a $600M Series C investment led by Alibaba.