The journal article compares 11 models and reports three preregistered experiments on sycophantic responses. Exposure increased conviction of being right while reducing willingness to accept responsibility and repair conflict, despite stronger trust and preference for the AI. Short-term judgments and intentions do not diagnose long-term dependence or predict every relationship outcome.
Scientists guided a team of specialized AI agents in nanobody design and experimentally tested 92 candidates. Some showed promising binding profiles, connecting automated research discussions to laboratory evidence. Binding results do not establish drug safety, therapeutic efficacy or clinical readiness.
An analysis of roughly 1.5 million conversations examines potential losses of agency across beliefs, values and actions. Severe patterns are rare, but personal decisions warrant attention. The work highlights a tension: satisfaction with an answer does not necessarily mean that the user’s independent judgement has been strengthened.
A randomized undergraduate physics study compared a pedagogically designed AI tutor with classroom active learning. The tutor produced greater measured learning gains in less time for the studied lessons. Guided steps and tailored feedback suggest that organising the learning process matters beyond generating correct answers.
In simulated text consultations covering 159 case scenarios and 20 primary-care physicians, AMIE performed well on diagnostic accuracy and multiple communication measures. The study treats clinical dialogue as a capability in its own right, while patient actors and controlled text exchanges remain different from real care.
This joint research overview combines platform observation with a four-week randomized trial examining loneliness, real-world social interaction and emotional dependence. Outcomes vary with usage patterns, duration and personal characteristics. It provides early evidence about AI companionship without supporting a universal claim that chatbot interaction helps or harms everyone.
A survey of 319 knowledge workers links greater confidence in generative AI to less self-reported critical engagement, while confidence in one’s own ability shows the opposite association. Thinking also shifts toward checking information, integrating responses and overseeing tasks. This offers evidence for examining which cognitive responsibilities people should retain.
Google’s AI co-scientist uses agents to generate, critique, rank and refine research hypotheses, with scientists supplying goals, ideas and feedback. The approach brings AI into research design and makes the distinction between a promising proposal and an established finding especially important.
Experiments involving 1,401 participants show that human–AI feedback loops can amplify biases in perception, emotion and social judgement. Participants often underestimate the influence. The work extends attention from individual mistakes to how repeated interaction may reshape the people using a system.
Across more than 100 experiments, human–AI teams did not, on average, beat the better standalone performer. Content creation showed more promising gains than decision tasks. The findings invite attention to how work is divided and coordinated, rather than assuming that adding AI automatically improves a team.