The Rise in AI for Teachers: An eLearning from a Graduate Capstone Project

This is eLearning module was designed to give teachers the basic understanding of generative AI and its benefits in education. The module was created for a designed-based research project completed as part of a graduate program at Western Governors University. The eLearning is designed around the use of choice boards and giving the learner the freedom of how they want to learn the content.

You may view the full capstone report here.

The Problem and Solution

Problem: The teachers at Jefferson Elementary, a pseudonym, wanted help shortening their prep time for lessons. I spoke to several teachers about the use of artificial intelligence (AI) in education and most were unaware of the help it can give teachers. They had a knowledge gap in the basics of AI in an education setting.

Solution: The solution was to create a 30 minute eLearning that covers foundational concepts of AI and give the teachers a way to practice and apply those skills in order to transfer them to their professional career. The eLearning was created using various choice boards where the teachers were able to pick either videos, articles, or infographics to learn the content. They were then able to apply their new knowledge using different AI tools for teachers to create materials for their lessons.

Learning Goal & Objectives

The learning goal of the module is learners will be able to explain generative AI and how it can be used in education. There are four learning objectives tied to the learning goal:

1. Learners will be able to explain what generative AI is;

2. Learners will be able to explain at least two ways to use generative AI in the classroom;

3. Learners will be able to create prompts using chatbots like ChatGPT; and

4. Learners will be able to create materials for lessons using generative AI tools.

The learning goals were measured using a pre-test and post-test.

Results

Descriptive statistics was used to analyze the quantitative data from both the pre-test and post-test. The learning management system Canvas calculated the average for all participants for the two tests. The average score for the pre-test is 49% and the average score for the post-test is 72%. I then subtracted the average score of the pre-test from the average score of the post-test to get determine the average growth percentage of the participants. On average, participants knowledge about AI increased by 23%.

Coding was used to analyze the qualitative data collected from the post-survey questions. I reviewed all the responses on the post-survey. After this, the I identified main ideas from the qualitative data collected. I then generated two themes based off the main ideas. The two the themes are as follows:

1. Choice boards made learning the material relevant and applicable to participants. They are also able to transfer the knowledge into their classroom.

2. Choice boards allowed the participants to be in charge of what they wanted to learn and how they learned it.

Audience: Teachers that want to shorten their lesson preparation time.

Tools Used: Canvas LMS, Synthesia, Google Docs, Google Slides, Canva, Chat GPT, Brisk Teaching


Artifacts from My Process Completing a Needs Analysis

I approached completing a needs analysis by first creating an empathy map with the help of the subject matter expert. The empathy map helped me know who the learners were. I did this by answering six questions:

-        What does the learner know?

-        How does the learner feel?

-        What does the learner do?

-        What can the learner do?

-        What are the learner's preferences?

-        What are the learner’s barriers?

After completing the empathy map, I began working on learner personas for each type of learner that would be completing the course or eLearning. I used the empathy map to help inform these.