Large language models (LLMs) increasingly serve as educational tools, yet evaluating their teaching capabilities remains challenging due to the resource-intensive, context-dependent, and methodologically complex nature of teacher-student interactions. We introduce EducationQ, a multi-agent dialogue framework that efficiently assesses teaching capabilities through simulated dynamic educational scenarios, featuring specialized agents for teaching, learning, and evaluation. Testing 14 LLMs across major AI Organizations (OpenAI, Meta, Google, Anthropic, and others) on 1,498 questions spanning 13 disciplines and 10 difficulty levels reveals that teaching effectiveness does not correlate linearly with model scale or general reasoning capabilities - with some smaller open-source models outperforming larger commercial counterparts in teaching contexts. This finding highlights a critical gap in current evaluations that prioritize knowledge recall over interactive pedagogy. Our mixed-methods evaluation, combining quantitative metrics with qualitative analysis and expert case studies, identifies distinct pedagogical strengths employed by top-performing models (e.g., sophisticated questioning strategies, adaptive feedback mechanisms). Human expert evaluations show 78% agreement with our automated qualitative analysis of effective teaching behaviors, validating our methodology. EducationQ demonstrates that LLMs-as-teachers require specialized optimization beyond simple scaling, suggesting next-generation educational AI prioritize targeted enhancement of specific pedagogical effectiveness.

The US Court of Appeals has upheld previous rulings that AIs cannot hold patents for inventions. AIs are increasingly being used to make new discoveries but, under most patent laws, a human must be listed as the patent holder for inventions. Dr Stephen Thaler created a device called DABUS that consists of neural networks and has been used to invent an emergency warning light, a food container that improves grip and heat transfer, and more. Thaler believes that AIs should be patent holders and has launched numerous cases in at least 15 countries to argue the case. So far, the cases in the UK, US, and New Zealand have all been rejected.

The emergence of Artificial Intelligence (AI) is a topical subject and one which is now part of our everyday lives. The possibilities offered by AI technology are immeasurable, but with this also come challenges, in particular with Intellectual Property (IP) rights. In a new series entitled'Tech Trends', the World Intellectual Property Organisation (WIPO) have released a report which examines the trends of AI over the last number of years and how the intellectual property landscape can learn and adjust to the constant evolution of AI. You can find this report here if you would like to know more. In previous articles we have seen how AI is driving developments in both technology and business, its rapid growth has revolutionised many aspects of our lives from how we work, travel and communicate.

Technology innovators intensified efforts over the last year to secure intellectual property rights on transformative technologies ranging from AI to blockchain platforms. While IBM maintained its overwhelming lead in U.S. patent awards, Chinese manufacturers continue to make steady progress gaining control of IP while the growth rate for patent awards in machine learning soared. An annual patent survey released by IFI Claims Patent Services found that machine-learning patent applications are among the fastest growing categories. Larry Cady, a senior analyst with IFI, estimates the patent category that includes machine learning and neural networks grew at a 34 percent annual rate over the last five years. "I don't see that trend slowing," Cady added in an interview.