As stories of human-AI interactions continue to be highlighted in the news and research platforms, the challenges are becoming more pronounced, including potential risks of overreliance, cognitive offloading, social and emotional manipulation, and the nuanced degradation of human agency and judgment. This paper surveys recent research on these issues through the lens of the psychological triad: cognition, behavior, and emotion. Observations seem to suggest that while AI can substantially enhance memory, creativity, and engagement, it also introduces risks such as diminished critical thinking, skill erosion, and increased anxiety. Emotional outcomes are similarly mixed, with AI systems showing promise for support and stress reduction, but raising concerns about dependency, inappropriate attachments, and ethical oversight. This paper aims to underscore the need for responsible and context-aware AI design, highlighting gaps for longitudinal research and grounded evaluation frameworks to balance benefits with emerging human-centric risks.

Pillay is an editorial fellow at TIME. Pillay is an editorial fellow at TIME. What is the split between your individual and enterprise customers? It was [previously] lower on the enterprise side. At the beginning of 2024, it was 90/10.

The proliferation of artificial intelligence (AI) systems, from virtual assistants [Kepuska and Bohouta, 2018] to recommendation engines [Gomez-Uribe and Hunt, 2015] and autonomous vehicles [Shladover, 2018], has reshaped human-machine interaction. Y et, African languages, with over 2,000 spoken across the continent [Eberhard et al., 2023], remain severely underrepresented in NLP due to their low-resource status [Ahia and Boakye, 2023, Nekoto et al., 2020]. This exclusion risks exacerbating the digital divide, limiting access to AI-driven services in critical domains like education, healthcare, and governance [Ndichu et al., 2024, Joshi et al., 2020]. Shona, a Bantu language spoken by millions in Zimbabwe and southern Zambia, exemplifies this challenge. Existing Shona corpora primarily consist of formal texts, such as news articles or religious documents [Eberhard et al., 2023], while everyday communication, particularly among younger speakers, is dominated by slang, code-mixing with English, and informal expressions [Eisenstein, 2013]. Standard NLP models, trained on formal data, struggle to process these dynamic linguistic patterns, hindering the development of culturally relevant conversational AI.

The emergence of large language models (LLMs) has unlocked boundless possibilities, along with significant challenges. In response, we developed a mental health support chatbot designed to augment professional healthcare, with a strong emphasis on safe and meaningful application. Our approach involved rigorous evaluation, covering accuracy, empathy, trustworthiness, privacy, and bias. We employed a retrieval-augmented generation (RAG) framework, integrated prompt engineering, and fine-tuned a pre-trained model on novel datasets. The resulting system, Mentalic Net Conversational AI, achieved a BERT Score of 0.898, with other evaluation metrics falling within satisfactory ranges. We advocate for a human-in-the-loop approach and a long-term, responsible strategy in developing such transformative technologies, recognizing both their potential to change lives and the risks they may pose if not carefully managed.

There are widespread fears that conversational AI could soon exert unprecedented influence over human beliefs. Here, in three large-scale experiments (N=76,977), we deployed 19 LLMs-including some post-trained explicitly for persuasion-to evaluate their persuasiveness on 707 political issues. We then checked the factual accuracy of 466,769 resulting LLM claims. Contrary to popular concerns, we show that the persuasive power of current and near-future AI is likely to stem more from post-training and prompting methods-which boosted persuasiveness by as much as 51% and 27% respectively-than from personalization or increasing model scale. We further show that these methods increased persuasion by exploiting LLMs' unique ability to rapidly access and strategically deploy information and that, strikingly, where they increased AI persuasiveness they also systematically decreased factual accuracy.

The growing prevalence of conversational voice interfaces, powered by developments in both speech and language technologies, raises important questions about their influence on human communication. While written communication can signal identity through lexical and stylistic choices, voice-based interactions inherently amplify socioindexical elements - such as accent, intonation, and speech style - which more prominently convey social identity and group affiliation. There is evidence that even passive media such as television is likely to influence the audience's linguistic patterns. Unlike passive media, conversational AI is interactive, creating a more immersive and reciprocal dynamic that holds a greater potential to impact how individuals speak in everyday interactions. Such heightened influence can be expected to arise from phenomena such as acoustic-prosodic entrainment and linguistic accommodation, which occur naturally during interaction and enable users to adapt their speech patterns in response to the system. While this phenomenon is still emerging, its potential societal impact could provide organisations, movements, and brands with a subtle yet powerful avenue for shaping and controlling public perception and social identity. We argue that the socioindexical influence of AI-generated speech warrants attention and should become a focus of interdisciplinary research, leveraging new and existing methodologies and technologies to better understand its implications.

Cinematographers adeptly capture the essence of the world, crafting compelling visual narratives through intricate camera movements. Witnessing the strides made by large language models in perceiving and interacting with the 3D world, this study explores their capability to control cameras with human language guidance. We introduce ChatCam, a system that navigates camera movements through conversations with users, mimicking a professional cinematographer's workflow. To achieve this, we propose CineGPT, a GPT-based autoregressive model for text-conditioned camera trajectory generation. We also develop an Anchor Determinator to ensure precise camera trajectory placement.

As generative artificial intelligence (AI) enables the creation and dissemination of information at massive scale and speed, it is increasingly important to understand how people perceive AI-generated content. One prominent policy proposal requires explicitly labeling AI-generated content to increase transparency and encourage critical thinking about the information, but prior research has not yet tested the effects of such labels. To address this gap, we conducted a survey experiment (N=1601) on a diverse sample of Americans, presenting participants with an AI-generated message about several public policies (e.g., allowing colleges to pay student-athletes), randomly assigning whether participants were told the message was generated by (a) an expert AI model, (b) a human policy expert, or (c) no label. We found that messages were generally persuasive, influencing participants' views of the policies by 9.74 percentage points on average. However, while 94.6% of participants assigned to the AI and human label conditions believed the authorship labels, labels had no significant effects on participants' attitude change toward the policies, judgments of message accuracy, nor intentions to share the message with others. These patterns were robust across a variety of participant characteristics, including prior knowledge of the policy, prior experience with AI, political party, education level, or age. Taken together, these results imply that, while authorship labels would likely enhance transparency, they are unlikely to substantially affect the persuasiveness of the labeled content, highlighting the need for alternative strategies to address challenges posed by AI-generated information.

It is a call for education and research in this space so that we can harness this irresistible force for more good than harm, and provides some early themes for designers to consider. We firstly discuss where and how LLMs have been used in school educational settings, and then explore the new opportunities that recently released models offer. A small-scale investigation reveals potentially large impacts on how children learn, and we highlight key things that we as a community need to be aware of. 2 A SIMPLE GUIDE TO LARGE LANGUAGE MODELS Large Language Models -- think ChatGPT, Gemini, GPT-3, CoPilot -- are immense deep learning neural networks with exceptional numbers of parameters, which are trained to pre dict sequences of words, having been trained on most of the contents of the Internet. If I asked you to complete the sentence Twinkle, twinkle, little star, how I wonder what you ..... it is quite likely that, if you have been brought up in a Wester n culture, you will recognise the nursery rhyme and complete the line with .....are LLMs do this, but on a massive scale. As the LLM has processed m uch of what has ever been written, it has ingested a large number of sequences of words, and compresses them to c reate an internal representation. An LLM can be seen as the JPEG of the web -- it is a lossy compressed version of the internet.

In this paper, we present ConvoGen: an innovative framework for generating synthetic conversational data using multi-agent systems. Our method leverages few-shot learning and introduces iterative sampling from a dynamically updated few-shot hub to create diverse and realistic conversational scenarios. The generated data has numerous applications, including training and evaluating conversational AI models, and augmenting existing datasets for tasks like conversational intent classification or conversation summarization. Our experiments demonstrate the effectiveness of this method in producing high-quality diverse synthetic conversational data, highlighting its potential to enhance the development and evaluation of conversational AI systems.