Health misinformation spreading online poses a significant threat to public health. Researchers have explored methods for automatically generating counterspeech to health misinformation as a mitigation strategy. Existing approaches often produce uniform responses, ignoring that the health literacy level of the audience could affect the accessibility and effectiveness of counterspeech. We propose a Controlled-Literacy framework using retrieval-augmented generation (RAG) with reinforcement learning (RL) to generate tailored counterspeech adapted to different health literacy levels. In particular, we retrieve knowledge aligned with specific health literacy levels, enabling accessible and factual information to support generation. We design a reward function incorporating subjective user preferences and objective readability-based rewards to optimize counterspeech to the target health literacy level. Experiment results show that Controlled-Literacy outperforms baselines by generating more accessible and user-preferred counterspeech. This research contributes to more equitable and impactful public health communication by improving the accessibility and comprehension of counterspeech to health misinformation

However, true AGI, as indicated by levels 5 and 6, is the future prospect for machine intelligence. These levels demand general problem-solving, versatility, and cognitive skills that are beyond a human's ability in all spheres of interaction. As of now, currently existing forms of AI do not meet these criteria. This study has derived a detailed comparison and evaluation of the LLMs with regard to educational achievement and literacy against human standards. Based on the quantitative data, AI models were found to be superior to human mean scores on all the cognitive tasks, with the differences being significant in the undergraduate knowledge and the advanced reading condition. These findings lay a strong ground on which one can determine the present state of AI in comparison to human benchmarks. This leads to the following discussion on the outlined results, where further illustrations and detailed analysis of the implications of advanced AI capabilities will be discussed.

In this context, the proliferation of fake news and its impact on society has become a matter of serious concern, and it is critical to understand the mechanisms involved. In this study, we specifically explore how the proliferation of fake news is affected by the strategic behavior and interaction dynamics of individuals. In a scenario where a single werewolf is present, we show that certain agents can have a significant impact on group dynamics by manipulating the flow of information. This result suggests a role for "opinion leaders" or "influencers" in the spread of fake news, and the detection of these agents and the mitigation of their influence may be key to understanding and controlling the dynamics of information dissemination. We have developed models of interactions between individuals and the propagation of information using the framework of incomplete information games and unfolding Figure 1: Network Graph with a Single Werewolf games. In particular, we used the concepts of cocktail party effect and repetition dilemma to analyze how the complexity many other agents an agent interacts with, and the repetition of the decisions agents face and their position in the dilemma represents the balance between an agent's incentives social network affect the spread of fake news and the gains to act cooperatively and non-cooperatively. of individual agents.