A ChatGPT Prompt for Learner Equity

How to use the combined power of learning science + ChatGPT to deliver increased belonging & equity in your classroom

Belonging is the perception of being seen, heard, accepted, valued and included. It’s also one of our most basic human needs.

Over the last twenty years or so, researchers have proven conclusively that a sense of belonging within a learning experience has powerful positive effects on learning outcomes.

Research shows that learners of all ages try harder, persist more and achieve better learning outcomes when they feel a sense of belonging with their instructors and fellow learners.

The sense of connection, comfort, confidence and security that comes from feeling like an integral part of a larger whole means that learners are more likely to feel that their contributions are appreciated and respected. This, in turn, drives learner motivation, persistence and ultimately, achievement.

By the same rule, learners are significantly more likely to be distracted, to disengage and under-achieve when they experience feelings of anonymity, isolation and alienation.

Learners from marginalised or underrepresented backgrounds - students of colour, students with disabilities, LGBTQ+ students, and students from low-income backgrounds - are far more more likely to lack a sense of belonging than their less-marginalised peers.

Other at-risk groups include students who are struggling academically and students who do not speak the primary language.

Design for Belonging

What strategies can we use to drive a sense of belonging in our learning experiences?

While belonging is a complex socio-emotional concept, research shows that three belonging interventions in particular have a positive impact on a student outcomes:

1. 🔍 Do Your Discovery

A simple but effective strategy: take time to understand who your learners are. Do your research identify at-risk groups and be intentional about optimising your learning design for belonging for every student.

2. 💬 Design Collaborative Projects

Where possible, foster positive relationships among mixed groups of students by creating opportunities for them to work together, collaborate and share their perspectives.

• Design group activities which encourage and celebrate the varied perspectives which come from diversity of experience and identity.

• Encourage open and respectful dialogue about identity and make sure all students feel heard and respected.

Example: Jigsaw Project

Divide students are into mixed groups and give each group a different resource on the topic being covered. Each group must become an expert on their resource and then teach the other groups about the content, including a consideration of the identity of the author and how this might have impacted their experience and perspective.

3. 📚 Diversify Your Curriculum

Perhaps the most powerful belonging intervention available to you as an educator is diversifying your curriculum.

Diversifying and decolonizing your curriculum means intentionally including a diverse range of perspectives and experiences in the materials, texts and/or resources that you use in your classroom.

This includes centering the experiences of marginalised and under-represented groups and challenging dominant narratives and stereotypes. By doing this we drive a sense of belonging by reflecting a relevant and meaningful view of the world back to all students.

Using generative AI to promote belonging & equity in the classroom

Using ChatGPT to Design for Belonging

So, we know the formula for belonging and we know that a sense of belonging drives student outcomes - so why don’t we see more instructional design for belonging?

One of the biggest barriers to implementing belonging interventions is that they are time consuming. Profiling learners, reviewing activities and identifying and incorporating new materials and resources into the curriculum is both complex and time consuming.

So, can ChatGPT help? I did some research and the answer is yes!

Disclaimer: the following ChatGPT 3.5 prompt comes with the usual disclaimer that ChatGPT needs you. As is always the case, doubt and verify everything ChatGPT says. AI is your PA, not your professor. It’s also limited to insights which stop in 2021, so you need to fill in the knowledge gap since then.

Disclaimer aside, the following prompt has yielded some pretty solid results for me. In each of my tests I picked reading lists at random from existing courses and in each case ChatGPT was able to identify biases and suggest alternatives.

Here’s the prompt I used:

Role: you are an expert in belonging, equity and inclusion.

Context: Learners try harder, persist & learn more when: 1. they are not distracted by a sense of isolation, alienation or exclusion 2. the learning experience actively addresses and challenges stereotypes 3. the learning experience provides a range of resources to reflect diversity of thought, experience & perspective on the topic to avoid cultural, political, racial or other biases.

Task: review the following reading list and diversify and decolonise it. Do this by profiling the author and summarising cultural, political, racial and other biases that they may bring to the topic. Suggest alternative relevant resources on the topic written by a greater and more diverse range of authors. For each suggested alternative, explain the new perspective it brings. The goal is to ensure that avoid cultural, political, racial and other biases in my selection of resources for this course. Only include resources which are from reliable sources and have over 200 citations on google scholar. Cite your sources and state how many citations the source has on Google Scholar.

[paste your reading list]

Here’s an example of one of ChatGPT’s responses:

I’m also working on some prompts which profile learners’ identities and generate both lesson plans and suggested content to optimise for belonging. Watch this space!

That’s all, folks! Give the prompt a go and let me know how well it works for you.

Happy designing,
Phil 👋

PS: If you want to dig into some of the research on belonging, check out the following:

Boaler & Greeno (2000), Identity, Agency & Knowing in Mathematical Worlds.

Godenow (1993), Classroom Belonging among Early Adolescent Students: Relationships to Motivation and Achievement.

Steele et al (1995), Stereotype threat and the intellectual test performance of African Americans.