Next generation ESD tool for your Class

Enabled ESD education from
early childhood.

 Creating an ESD (Education for Sustainable Development) education chat using GPT-4, we have diligently engaged the collaboration of numerous ESD experts over a span of more than two years in the development and compilation of the dataset.

 As a result, we have taken into consideration the following key aspects to enable the model to furnish accurate and beneficial information, thereby supporting the objectives of ESD education.

  • By incorporating specialized knowledge and orientation encompassing concepts, principles, and terminology related to ESD into the model, we have facilitated the provision of precise information.
  • Our model now comprehends the context of ESD education, thereby enabling it to generate appropriate responses to user inquiries and requests.
  • Rigorous selection of learning data from dependable sources has been undertaken to ascertain the accuracy and reliability of the information presented.
  • Information delivery is structured to avoid misconceptions concerning pivotal topics and concepts within ESD education by providing pertinent contextual details.
  • In alignment with the educational objectives of ESD, our model generates responses that offer users opportunities to enhance their understanding.
  • An adept management of biases ensures the equitable provision of information from a diverse array of perspectives.
  • Rigorous selection of learning data from dependable sources has been undertaken to ascertain the accuracy and reliability of the information presented.
  • When necessary, we provide users with references to reputable sources that offer accurate information to supplement their inquiries.
  • Continual monitoring of model training and response generation permits us to effect ongoing enhancements as needed.
  • Our commitment to safeguarding personal information and privacy ensures the ethical utilization of data.

 In adhering to these considerations, the production of an ESD education chat ensures the maximization of educational value and the assurance of accurate information delivery.

Our ESD education-focused dataset
development process:

Creating an ESD (Education for Sustainable Development) education chat using GPT-4, we have diligently engaged the collaboration of numerous ESD experts over a span of more than two years in the development and compilation of the dataset. As a result, we have taken into consideration the following key aspects to enable the model to furnish accurate and beneficial information, thereby supporting the objectives of ESD education

Building the ESD SMART dataset involves the following process:

  • 1ESD-related materials
    ⇒ Gather ESD-related materials: records of ESD classes, lectures, teaching materials, textbooks, and online courses.
    ⇒ Collect dialogues from ESD communities, forums, social media, etc.
  • 2Data Preprocessing
    ⇒ Clean data: remove irrelevant information, special characters, and duplicates.
    ⇒ Normalize text: ensure consistent formatting and structure.
  • 2Formatting in Dialogue Style
    ⇒ Transform data into dialogues: alternate user utterances and model responses.
    ⇒ Optionally, add context or speaker identifiers.
  • 4Incorporating ESD Domain Knowledge
    ⇒ Integrate ESD concepts, terms, and crucial information into the dataset.
    ⇒ Ensure accurate representation of ESD principles.
  • 5Balanced Data
    ⇒ Balance sentiment: include positive, negative, and neutral tones.
    ⇒ Reflect diverse perspectives within ESD & SDGs.
  • 6Data Splitting
    ⇒ Divide into sets: create training, validation, and test subsets.
    ⇒ Allocate data appropriately to ensure effective model training.
  • 7Model Evaluation and Refinement
    ⇒ Converted to JSON, CSV, or other suitable format for storage and use without loss of accuracy and essence.
  • 8Model Training
    ⇒ Train GPT-4 model using the ESD-focused dataset.
    ⇒ Explore methods to emphasize accurate ESD information transmission.
  • 9Model Evaluation and Refinement
    ⇒ Continuously assess response quality during training.
    ⇒ Adjust dataset or training parameters for enhanced model performance.

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