Artificial intelligence and language studying blend seamlessly when it comes to online education. Already a key component of sectors such as eCommerce, FinTech, and marketing, AI is quickly becoming standard practice in education.
Online education has become a revolution en masse. With the accelerated introduction of AI to foreign language learning, we are now experiencing a similar paradigm shift. AI-powered language studying combined with the use of neural network capabilities is resulting in a new era of education for students and teachers alike. But how exactly can you use AI to improve learning outcomes for your students?
Observatorio IA - educación
Newark public schools are cautiously trying out a new automated teaching aid from Khan Academy. The preliminary report card: “could use improvement.”
Se suele decir que durante un curso escolar o universitario los profesores se deben adaptar a multitud de cambios. Esa frase, sin embargo, nunca había conocido un curso como este. A finales de noviembre, OpenAI liberó a todo el mundo ChatGPT. Una avanzada inteligencia artificial que, a través de su modelo de lenguaje, podía llegar a manos de cualquier con una conexión a internet. Y sí, eso incluye a millones de profesores y alumnos, ante los cuales se abría un nuevo mundo con la entrada de ChatGPT en la educación.
We believe teachers can use ChatGPT to increase their students’ motivation for learning and actually prevent cheating. Here are three strategies for doing that.
Podemos ver ChatGPT como el último golpe a nuestros sistemas educativos o como la chispa que cambiará la educación para mejor. Creo que tanto los humanos como la tecnología darían la misma respuesta a esta pregunta.
As a linguist who studies the effects of technology on how people read, write and think, I believe there are other, equally pressing concerns besides cheating. These include whether AI, more generally, threatens student writing skills, the value of writing as a process, and the importance of seeing writing as a vehicle for thinking.
The use of ChatGPT as a search engine has made the need for critical thinking more pressing than ever, writes Enrique Dans.
How can we effectively communicate to education professionals that generative AI will enhance their work rather than replace them?
Rania Abdelghani, Yen-Hsiang Wang, Xingdi Yuan, Tong Wang, Pauline Lucas, Hélène Sauzéon, Pierre-Yves Oudeyer
arXiv
(01/05/2023)
In order to train children's ability to ask curiosity-driven questions, previous research has explored designing specific exercises relying on providing semantic and linguistic cues to help formulate such questions. But despite showing pedagogical efficiency, this method is still limited as it relies on generating the said cues by hand, which can be a very costly process. In this context, we propose to leverage advances in the natural language processing field (NLP) and investigate the efficiency of using a large language model (LLM) for automating the production of the pedagogical content of a curious question-asking (QA) training. We study generating the said content using the "prompt-based" method that consists of explaining the task to the LLM in natural text. We evaluate the output using human experts annotations and comparisons with hand-generated content. Results suggested indeed the relevance and usefulness of this content. We also conduct a field study in primary school (75 children aged 9-10), where we evaluate children's QA performance when having this training. We compare 3 types of content : 1) hand-generated content that proposes "closed" cues leading to predefined questions; 2) GPT-3-generated content that proposes the same type of cues; 3) GPT-3-generated content that proposes "open" cues leading to several possible questions. We see a similar QA performance between the two "closed" trainings (showing the scalability of the approach using GPT-3), and a better one for participants with the "open" training. These results suggest the efficiency of using LLMs to support children in generating more curious questions, using a natural language prompting approach that affords usability by teachers and other users not specialists of AI techniques. Furthermore, results also show that open-ended content may be more suitable for training curious question-asking skills.
ChatGPT might overtime become a universal knowledge engine, but the unique aspects of learning through interaction with content and engagement with peers will provide nuance in developing (tuning) models.
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