ChatGPT Custom Model and Its Great Potentials in Personalised Education
This week's post will be a brief one, focusing on the recent highlights from OpenAI's inaugural dev keynote. While numerous exciting features were unveiled, I'd like to delve into two key aspects, namely the Custom Model and Assistant API, and explore their potential impact on future education.
GPT Custom Model
OpenAI introduced a groundbreaking feature that empowers users to create their own personalised GPT model tailored to specific needs. By providing natural language instructions to ChatGPT, users can shape their custom GPT model, defining traits, personalities, and instructions for end-user assistance. The customisation extends to generating a unique profile avatar for the GPT assistant.
In the realm of education, this feature opens up the possibility for any teacher to craft their own AI teaching assistant. Imagine a math teacher utilising this technology to construct a GPT model well-versed in the syllabus they are teaching. Such a model could proficiently address student queries, rectify assignment errors, and offer guidance on effective learning strategies.
What adds an extra layer of amazement is the custom model's capacity to absorb knowledge. As demonstrated in the keynote, educators can upload lecture notes to the GPT model, enriching it with subject-specific information. This tailored approach allows teachers to create multiple models, each geared towards a specific learning objective, providing contextual responses that consider what students have learned and what might be too advanced for their current level.
Assistant API
The keynote also showcased the Assistant API, a developer-centric tool with immense potential. While primarily geared towards developers, its implications for education are noteworthy.
The Assistant API combines various features into a streamlined interface, enabling the invocation of functions within applications, dynamic code generation and execution, retrieval of information from custom-fed materials, and integration with OpenAI's existing text-to-speech and speech-to-text APIs.
For educators, this API presents an opportunity to usher in smart applications that elevate personalised learning. The information retrieval capability allows the AI to discern individual learning patterns by analysing data collected during interactions with the application. Similar to the custom model, the Assistant API allows the creation of multiple assistants, each tailored to an individual learner. This translates to a virtual private tutor, attuned to a student's strengths and weaknesses, delivering personalised content for effective learning. For visually impaired learners, the assistant can seamlessly integrate with the speech-to-text API for understanding and respond using the text-to-speech API.
In essence, the Assistant API transforms the dream of creating virtual personalised tutors into a reality.
Conclusion
While this post is concise, the excitement I feel after witnessing the keynote is immense. The rapid advancement of AI technology makes its integration into education almost inevitable. The future of education appears dynamic and distinct from traditional methods. AI assistants pave the way for EdTech tools with unprecedented personalisation. Each learner can access a personalised AI assistant, dedicated to understanding their unique learning patterns and providing specific, effective advice or feedback to enhance their educational journey.
For me, the prospect of incorporating these technologies into my ongoing development project - Taboo AI, is thrilling, as it promises to elevate English learning to new heights of personalisation and enjoyment!
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