Karthik Ranganathan, SupportNinja, is passionate about the democratization of data and the ethical use of AI; a tech leader and innovator.
Given that we have five generations in the workplace today, we need a different approach to e-learning that takes into account not just the generational gap, but also our different learning styles.
While the CPRD segment has embraced mass personalization effectively, employee engagement is still far behind. The baseline still seems to be that what is good for the geese is good for the gander. In this article, I will explore the use of ChatGPT and other large language models (LLMs) as an enabler for e-learning transformation. Ideally, you will be able to accomplish this without completely rewriting your entire curriculum.
ChatGPT As An Enabler
Here are some uses cases where LLMs can be an effective enabler of learning transformation:
1. Personalized Learning ExperiencesLMs can be used to create personalized learning experiences for each learner. This is done by analyzing the learner’s past performance, interests and goals, then tailoring the content and delivery of the learning experience accordingly. This can help learners stay engaged and motivated, and it can also help them learn more effectively.
2. Interactive And Engaging LessonsLLMs can be used to create interactive and engaging lessons. This is done by using these models’ ability to generate natural language conversations. These models can create virtual tutors that listen to each learner’s individual questions. They then can construct feedback for those learners and help them approach new problems in the future. This can make learning more fun and engaging, and it can also help learners retain the information they learn.
3. Clearly Defined Learning ObjectivesLLMs can be used to help instructional designers clearly define learning objectives. This is done by using their ability to understand and summarize complex concepts. For example, LLMs can be used to create a list of learning objectives for a new course or help instructional designers identify the key concepts that need to be covered in a course. This can help ensure that courses are well designed and that learners achieve the desired learning outcomes.
4. Structuring Conversational InteractionsLLMs can be used to structure conversational interactions between learners and instructors. This is done by using their ability to understand and respond to natural language questions. For example, LLMs can be used to create chatbots that interact with learners. This can help learners get the help they need when they need it, and it can also help instructors manage their time more effectively.
5. Giving Feedback And AssessmentLLMs can be used to give feedback and assessment to learners. This is done by using their ability to understand and evaluate learners’ responses. For example, ChatGPT can be used to grade learners’ quizzes, provide feedback on learners’ assignments or help learners identify their strengths and weaknesses. This can help learners improve their learning, and it can also help instructors track learner progress.
Best Practices For Using LLMsHere are some best practices for the use of LLMs:
• Be aware of the limitations of LLMs. LLMs are still under development, and they can be prone to errors. They may not be able to understand complex or nuanced questions, and they may generate text that is factually incorrect or biased.
• Be aware of the potential for bias. LLMs are trained on massive data sets of text, and these data sets can contain biases. This means that LLMs may generate text that is biased, even if you give them clear and specific prompts.
Additionally, there are some additional practices for the use of LLMs in a controlled environment:
• Evaluate the model’s performance. Before you deploy an LLM, it is important to evaluate its performance. This will help you to identify any areas where the model is not performing well, and it will also help you to determine the best way to use the model.
• Monitor the model’s output. Once you have deployed an LLM, it is important to monitor its output. This will help you to identify any potential problems with the model, and it will also help you to track the model’s performance over time.
• Keep the model up to date. As new data becomes available, it is important to update the model with this data. This will help to improve the model’s performance and accuracy.
These are just a few of the many ways that LLMs can be used in e-learning. As these models continue to develop, we can expect to see even more innovative and creative use cases for this powerful AI tool.
Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Read the full article here
