As the world continues to embrace the rapid developments in artificial intelligence (AI) including large language models (LLMs), it is important for educators to be informed and keep up with these changes. The accelerated deployment of the technologies into the digital ecosystems we depend on will only become more prevalent in the months and years to come. AI is increasingly becoming an integral part of our daily lives and its impact on education is becoming more significant, especially as our students and colleagues are leveraging this technology to support their own learning. In order to provide a platform and point of reference to support the purposeful integration of AI and LLMs into the classroom, it is important to stay informed and develop a rich understanding of the knowledge-base and ethical implications of these tools. With this in mind, the purpose of this series is to support readers with a repository of resources on AI and LLMs cohabitation in an educational setting.
2023 started with significant developments in the field of AI and LLMs such as ChatGPT, Claude, and Bard. These models are a product of deep learning algorithms that allow users to input text and receive relevant responses based on the model's predictions. Deep learning algorithms are computer programs that can learn from and improve their performance based on large amounts of data, enabling them to identify complex patterns and relationships.
This technology is said to have the potential to transform the way we approach education by enabling students and educators to enhance their writing, research, and responses to prompts. For example, students can input a writing prompt or a research question into one of these models and receive feedback and suggestions to improve their writing or receive relevant information on their research topic. This opens up new possibilities for students to learn and engage with content in a more efficient and accurate manner.
However, it's important to approach these LLMs with caution and ensure that we're using them in an ethical and responsible manner. As educators, we have a responsibility to guide students in using these models effectively and to develop critical thinking skills to evaluate the information they receive from them.
As educators plan to create effective and engaging learning experiences for students, many educators are increasingly leveraging the potential of AI and LLMs. An important approach is to utilize these models with a cohabitation mindset, where AI and LLMs are integrated into the classroom to work alongside teachers and students. This approach offers a wealth of possibilities, from AI-powered chatbots that can provide students with instant feedback to LLMs that can help students with their writing by identifying grammatical errors and providing suggestions for improvement.
Another approach in leveraging AI cohabitation to support learning is through personalized learning. AI algorithms can analyze student data to support educators in creating customized learning experiences tailored to individual needs, preferences, and strengths. This allows for more differentiated learning and ensures that every student receives the appropriate level of support and challenge.
Additionally, AI and LLMs can be used to support teachers in grading, assessment, and report card writing, potentially saving time and bringing efficiencies to these workflows. By automating some aspects of grading, teachers can focus on more meaningful tasks such as providing feedback and supporting student learning.
There is no doubt that integrating and cohabitating with AI and LLMs in teaching practices has the potential to lead to effective and efficient workflows for teaching and learning, benefiting both teachers and students. In cohabitating with these technologies, educators can amplify the work they do with personalized learning experiences and prepare students for a future where AI and LLMs cohabitation will continue to play an increasingly important role. As educators, it is important to engage and explore this new dynamic to keep up with the latest advancements and leverage these resources to ensure that our students are prepared for the challenges and opportunities of tomorrow.
A point of reflection and question to frame one’s own thinking in the role and place of AI and LLMs in teaching and learning is to ask, "What is the value-added proposition of schools in the Age of AI?"
Resources that unpack these technologies and provide some frame of reference to understand these large language models are:
- How GPT4.0 and Other Large Language Models Work
Creating an understanding of how LLMs can generate realistic language when prompted. Exploring the question of, “Do they truly comprehend the content they read.” And recent advancements and limitations in LLMs that should also be considered.
To follow along with exploring and examining AI and LLMs cohabitation in education, follow my new blog series on TIE. In my next article, we will explore a set of resources on supporting educators with the many different terms and definitions for AI and LLMs, which often can be complicated to understand.
John Mikton is the primary technology for learning coordinator at the International School of Geneva – La Châtaigneraie . He is a trainer and course designer at the Principal Training Center and the Teacher Training Center, as well as a Farai Education Group consultant and coach. He has 29 years of experience as an educator in education and media technology in international schools in Africa, Asia, and Europe. Of which, 18 years have been in school leadership as an information technology director, director of e-learning, head of education and media technology, and deputy principal.
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