Generative artificial intelligence (GenAI) systems generate artwork likely to be copyrightable if made by human. In lieu of discussing whether machine-enabled artworks should be copyrighted, a question that has received a large attention in academic scholarship, this paper proposes to analyse the economic implications of GenAI. Although the valuation of their outputs is still mostly unknown, preliminary studies show that, all other things being equal, humans’ works are evaluated at a significant higher value than machine-enabled ones. Yet, to be properly valued, human-made and machine-enabled products must be distinguishable. They are not. This indistinguishability creates an asymmetry in information that in turn leads to a lemons problem, defined as a market erosion of good-quality products. Against that background, this paper proposes a solution in light of European Union rules of origin and the so-called substantial transformation test. This solution can then be used in copyright law to identify where the human author has been able to make free and creative choices (or lack thereof) required to satisfy the criterion of originality and, hence, copyrightability.

The advent of generative artificial intelligence (AI) models holds potential for aiding teachers in the generation of pedagogical materials. However, numerous knowledge gaps concerning the behavior of these models obfuscate the generation of research-informed guidance for their effective usage. Here, we assess trends in prompt specificity, variability, and weaknesses in foreign language teacher lesson plans generated by zero-shot prompting in ChatGPT. Iterating a series of prompts that increased in complexity, we found that output lesson plans were generally high quality, though additional context and specificity to a prompt did not guarantee a concomitant increase in quality. Additionally, we observed extreme cases of variability in outputs generated by the same prompt. In many cases, this variability reflected a conflict between outdated (e.g. reciting scripted dialogues) and more current research-based pedagogical practices (e.g. a focus on communication). These results suggest that the training of generative AI models on classic texts concerning pedagogical practices may bias generated content toward teaching practices that have been long refuted by research. Collectively, our results offer immediate translational implications for practicing and training foreign language teachers on the use of AI tools. More broadly, these findings highlight trends in generative AI output that have implications for the development of pedagogical materials across a diversity of content areas.

With the recent advances in artificial intelligence (AI), patients are increasingly exposed to misleading medical information. Generative AI models, including large language models such as ChatGPT, create and modify text, images, audio and video information based on training data. Commercial use of generative AI is expanding rapidly and the public will routinely receive messages created by generative AI. However, generative AI models may be unreliable, routinely make errors and widely spread misinformation. Misinformation created by generative AI about mental illness may include factual errors, nonsense, fabricated sources and dangerous advice. Psychiatrists need to recognise that patients may receive misinformation online, including about medicine and psychiatry.