Large Language Models (LLMs) are increasingly applied to domains that require reasoning about other agents' behavior, such as negotiation, policy design, and market simulation, yet existing research has mostly evaluated their adherence to equilibrium play or their exhibited depth of reasoning. Whether they display genuine strategic thinking, understood as the coherent formation of beliefs about other agents, evaluation of possible actions, and choice based on those beliefs, remains unexplored. We develop a framework to identify this ability by disentangling beliefs, evaluation, and choice in static, complete-information games, and apply it across a series of non-cooperative environments. By jointly analyzing models' revealed choices and reasoning traces, and introducing a new context-free game to rule out imitation from memorization, we show that current frontier models exhibit belief-coherent best-response behavior at targeted reasoning depths. When unconstrained, they self-limit their depth of reasoning and form differentiated conjectures about human and synthetic opponents, revealing an emergent form of meta-reasoning. Under increasing complexity, explicit recursion gives way to internally generated heuristic rules of choice that are stable, model-specific, and distinct from known human biases. These findings indicate that belief coherence, meta-reasoning, and novel heuristic formation can emerge jointly from language modeling objectives, providing a structured basis for the study of strategic cognition in artificial agents.

Despite the increasing use of large language models (LLMs) in education, concerns have emerged about their potential to reduce deep thinking and active learning. This study investigates the impact of generative artificial intelligence (AI) tools, specifically ChatGPT, on the cognitive engagement of students during academic writing tasks. The study employed an experimental design with participants randomly assigned to either an AI-assisted (ChatGPT) or a non-assisted (control) condition. Participants completed a structured argumentative writing task followed by a cognitive engagement scale (CES), the CES-AI, developed to assess mental effort, attention, deep processing, and strategic thinking. The results revealed significantly lower cognitive engagement scores in the ChatGPT group compared to the control group. These findings suggest that AI assistance may lead to cognitive offloading. The study contributes to the growing body of literature on the psychological implications of AI in education and raises important questions about the integration of such tools into academic practice. It calls for pedagogical strategies that promote active, reflective engagement with AI-generated content to avoid compromising self-regulated learning and deep cognitive involvement of students.

Ukraine has launched one of its largest drone attacks on Moscow, as it presses on with a major incursion into Russia's Kursk region, Russian authorities said. Russia's Ministry of Defence said on Wednesday that air defence forces shot down 11 drones over Moscow and its surrounding region, with some reportedly downed over the city of Podolsk some 38km (24 miles) south of the Kremlin. "This is one of the largest ever attempts to attack Moscow with drones," Moscow Mayor Sergei Sobyanin said on the Telegram messaging app. No damage or casualties were reported, he said in an earlier post. Drone attacks on Moscow are rare.

"Artificial intelligence is the future, not only for Russia, but for all humankind. It comes with colossal opportunities, but also threats that are difficult to predict. Whoever becomes the leader in this sphere will become the ruler of the world."1 "A people that no longer can believe anything cannot make up its mind. It is deprived not only of its capacity to act but also of its capacity to think and to judge. And with such a people you can then do what you please."2

Strategic thinking has long been viewed as essential for leaders of organizations. The ability to anticipate and plan for the future, to think critically and creatively about complex problems, and make effective decisions in the face of uncertainty and change is more necessary than ever. These capabilities will be dramatically augmented and magnified by artificial intelligence systems such as ChatGPT. With the ability to process large amounts of data, identify patterns, and make predictions, AI will provide fresh insights and perspectives that were previously unavailable to company executives. This will enable them to make more informed and accurate decisions – and to anticipate and plan for the future more effectively.

Summary: Whether you're a data scientist building an implementation case to present to executives or a non-data scientist leader trying to figure this out there's a need for a much broader framework of strategic thinking around how to capture the value of AI/ML. There are many articles written from a tools perspective about how to take advantage of specific capabilities of AI. Those encompass for example chatbots from NLP or image classification based on CNNs. To be clear, I'm talking about the expanded definition of AI that should more correctly be called AI/ML since the more mature field of machine learning is full of good implementation lessons ranging from marketing to fraud to forecasting. But whether you're a data scientist building an implementation case to present to executives or a non-data scientist leader trying to figure this out there's a need for a much broader framework of strategic thinking around how to capture the value of AI/ML.

Summary: Whether you're a data scientist building an implementation case to present to executives or a non-data scientist leader trying to figure this out there's a need for a much broader framework of strategic thinking around how to capture the value of AI/ML. There are many articles written from a tools perspective about how to take advantage of specific capabilities of AI. Those encompass for example chatbots from NLP or image classification based on CNNs. To be clear, I'm talking about the expanded definition of AI that should more correctly be called AI/ML since the more mature field of machine learning is full of good implementation lessons ranging from marketing to fraud to forecasting. But whether you're a data scientist building an implementation case to present to executives or a non-data scientist leader trying to figure this out there's a need for a much broader framework of strategic thinking around how to capture the value of AI/ML.

The aim of this Foresight Brief is therefore merely to initiate a debate.

While robots have taken many of the jobs of the manually skilled, Pluribus and its future generations are coming for the jobs at the other end of the spectrum--the brilliant, the cunning, the creative. Have we reached an artificial intelligence (AI) milestone overload? Are we so jaded about momentous breakthroughs in AI capabilities that we no longer acknowledge them with the appropriate awe AI demands? One would think so after the July performance of Carnegie Mellon and Facebook's Pluribus went virtually unnoticed. You should, because this valedictorian of machine learning is a serious threat to your livelihood.

While robots have taken many of the jobs of the manually skilled, Pluribus and its future generations are coming for the jobs at the other end of the spectrum--the brilliant, the cunning, the creative. Have we reached an artificial intelligence (AI) milestone overload? Are we so jaded about momentous breakthroughs in AI capabilities that we no longer acknowledge them with the appropriate awe AI demands? One would think so after the July performance of Carnegie Mellon and Facebook's Pluribus went virtually unnoticed. You should, because this valedictorian of machine learning is a serious threat to your livelihood.