todoELE

Estamos como en Poltergeist: delante de la televisión sin sintonizar, y diciendo eso de «ya están aquí«… No, no es malo, no es negativo, no lo vamos a prohibir, ni a regular de manera que no ocurra. Es, simplemente, inevitable. O desarrollamos una forma de distribuir la riqueza que se adapte a esos nuevos tiempos que ya están aquí, o tendremos un problema muy serio. Y no será un problema de la tecnología: será completamente nuestro.

This working paper discusses the risks and benefits of generative AI for teachers and students in writing, literature, and language programs and makes principle-driven recommendations for how educators, administrators, and policy makers can work together to develop ethical, mission-driven policies and support broad development of critical AI literacy

Recent advances in generative pre-trained transformer large language models have emphasised the potential risks of unfair use of artificial intelligence (AI) generated content in an academic environment and intensified efforts in searching for solutions to detect such content. The paper examines the general functionality of detection tools for artificial intelligence generated text and evaluates them based on accuracy and error type analysis. Specifically, the study seeks to answer research questions about whether existing detection tools can reliably differentiate between human-written text and ChatGPT-generated text, and whether machine translation and content obfuscation techniques affect the detection of AI-generated text. The research covers 12 publicly available tools and two commercial systems (Turnitin and PlagiarismCheck) that are widely used in the academic setting. The researchers conclude that the available detection tools are neither accurate nor reliable and have a main bias towards classifying the output as human-written rather than detecting AI-generated text. Furthermore, content obfuscation techniques significantly worsen the performance of tools. The study makes several significant contributions. First, it summarises up-to-date similar scientific and non-scientific efforts in the field. Second, it presents the result of one of the most comprehensive tests conducted so far, based on a rigorous research methodology, an original document set, and a broad coverage of tools. Third, it discusses the implications and drawbacks of using detection tools for AI-generated text in academic settings.

As initial fear of AI among educators turns more and more into curiosity, the question I get asked most is: how can I assess learning if ChatGPT can write assignments? This week, I’ll share a simple, three-step guide to get you started on designing assessments for the post-AI classroom, plus some tips on how to use ChatGPT to help you to rapidly redesign your assessments.