Module 1: Leveraging LLMs: Prompt Engineering, from Definition to Evaluation

• Articulate the process of next-word prediction in large language models leading to sentence completion
• Apply prompt engineering strategies to optimize the performance of Large Language Models (LLMs) in generating desired educational outputs
• Differentiate between zero-shot and few-shot prompting, explaining appropriate scenarios for their utilization
• Demonstrate how to refine prompts through additional instructions or context to shape the output of Large Language Models
• Critically evaluate the output from Large Language Models for reliability and accuracy, identifying potential instances of hallucination

Module 2: Top-Down Design: Implementing Educational Tasks with LLMs and Learning Science Principles

• Divide an educational task into subtasks as part of top-down design based on the concept of LLM chains
• Employ LLMs to generate tailored learning interactions and enhance existing learning materials in line with learning science principles
• Contextualize educational problems to match individual student interests in intelligent tutoring systems using large language models

Module 3: Integrating LLMs in Educational Tools: Practical Examples

• Differentiate between LLM APIs and conversational chatbots justifying the use of APIs in educational contexts despite the convenience of chatbots
• Analyze the implications of hyperparameter tuning in prompt engineering for optimizing the LLM outputs
  • Temperature
  • Top-k
  • Top-p
  • Number of tokens
  • Repetition Penalty
  • Presence Penalty
  • Number of Examples
  • Query Format
  • Output Format
  • Instruction Style
  • Keyword Selection
• Utilizing DSPy for programming foundational models with minimal code to solve a given task
  • Curating your educational dataset for training and validation
  • Defining metrics for quality evaluation
• Create effective distractors for multiple-choice questions using LLMs that align with learning outcomes and target students’ misconceptions

Module 4: Course Project or Final Exam

At the end of the course, you’ll have an opportunity to do a two-step project from three choices based on area of your interest i.e Educational Tool Design (UX/UI), Educational Tool Development (Technical), or Prompt Engineering Template (Instructional Design). That will provide you with a nice experience to apply the fundamentals you will learn in the modules to a larger, more authentic, context. It will be graded by the instructor and you will receive personalized feedback.

None

Instructional designers, researchers developers, product/UX designers, and teachers who want to leverage large language models. Anyone interested in edtech.