Exploring ideas for decolonizing the curriculum using generative AI tools

Decolonizing educationgenerative AIEducationEducational technologiesLearning & teaching
Written By Dustin Hosseini
“The master’s tools will never dismantle the master’s house.”
— Audre Lorde

In this post, I share some examples created by generative AI for decolonizing the curriculum. I also contextualize the examples by providing commentary from colleagues from the University of Glasgow Decolonising the Curriculum Community of Practice.

Decolonizing education is part of many university strategies, including the university where I work. So, it seemed natural to think of how generative AI tools might help university students and staff think of ideas for decolonizing the curriculum. However, we must remember that the underlying logic of generative AI represents tools created by those in nations that hold power over others. Generative AI tools are often created in former imperial nations that seek out and obtain cheaper labor in other parts of the world to train and ‘develop’ the tools further. Generative AI also imparts a significant environmental impact, which must be considered.

AI and ethical considerations: coloniality of…

There are several caveats to using AI and generative AI generally, which I briefly outline in Karen Hao’s article from July 2020:

  • ghost work

    • this is invisible labor provided by underpaid workers who are often in former US and UK colonies (among others)

  • beta testing

    • sometimes beta testing is used on more vulnerable groups; yes, this is unethical, but it does still happen

  • AI governance

    • think about who creates governance for AI; high-wealth nations and the Global North largely drive this at the expense of Global South nations

  • international social development

    • if we consider ‘AI for…’ initiatives, we have to consider who drives these and who the targets or recipients are

  • algorithmic discrimination and oppression

    • if we consider who creates algorithms, then we can begin to understand why some algorithms can portray racist, gendered, xenophobic imagery

Further reading

To understand the ethical issues of generative AI by using a decolonial lens, have a read of these:

Generative AI’s suggestions for decolonizing

For the following outputs, as shown in the GIF images below, I used the initial prompt:

I'm a lecturer and there is talk of decolonising the curriculum. I teach mathematics and statistics. What can I do to start decolonising my curriculum?

As we can see in the GIFs below, each generative AI tool appears to give some considered suggestions for how a lecturer in this particular area might go about decolonizing the curriculum they teach. Ideas such as incorporating more diverse views, Indigenous knowledges and contextualizing what is being learned are all general suggestions that I might expect to find in such a curriculum that is undertaking decolonizing.

However, I wanted to see more detail and so I followed up with another prompt.

The follow-up prompt was designed to see what else generative AI might suggest. Interestingly, with insight from colleagues, plenty could be done and suggested to create a curriculum that undertakes decolonization within a specific context.

In this case, the lists seemed familiar and similar in some respects and then a bit different in other respects in ways that I couldn’t immediately pick up on. The suggested names stem from ancient to modern times, albeit with a jump between ancient and modern times! Some familiar names are there, but are there perhaps some that could be included?

Here is the prompt I used:

What are some prominent but overlooked non-Western scholars of mathematics and statistics?

Reflections from colleagues

I consulted some colleagues, given the topic, the example is from an area I’m not familiar with. Specifically, I consulted colleagues in the UofG Decolonising the Curriculum Community of Practice who kindly provided their thoughts.

Soryia Siddique, whose background is in chemistry/pharmaceuticals/politics, provided the following:

My initial observation is that we ensure women of colour are represented in the materials. Perhaps a specific search around this.

BAME and Muslim women are underrepresented in many professions, including senior roles in Scotland, and are likely to experience systemic bias. Taking into consideration that Muslim women can experience racisim, sexism, and Islamaphobia. It is questionable whether media/society represents Muslim and BAME women's current and historical achievements.

They are also "missing” from Scotland’s media landscape.

In utilising AI, are we relying on data that is embedded in algorithmic bias and potentially perpetuating further inequality?

Soryia also suggested the following reading: The Movement to Decolonize AI: Centering Dignity Over Dependency from Standford University’s Human-Centered Artificial Intelligence. It’s an interview with Sabelo Mhlambi who describes the role of AI in colonization and how activists can counter this.

Samuel Skipsey, whose background is in physics and astronomy, also shared his thoughts:

The "list of important non-Westerners" is fairly comparable between the two - Bard is more biased towards historical examples and is pretty India-centric (with no Chinese or Japanese examples, notably), ChatGPT does a lot better at covering a wider baseline of "top hits" across the world (although given that "Nine Chapters on the Mathematical Art" doesn't have known authors - the tradition of the time it was written means that it probably had many contributions whose authorship is lost to history - I would quibble about it being a "scholar"). I note that this is still a Northern-Hemisphere centric list from both - although that's somewhat expected due to the problems citing material from pre-colonial Latin America, say. Still, it would have been nice to see some citation of contributions from Egypt, say.

In general, both lists are subsets of the list I would have produced by doing some Wikipedia diving.

The "advice on decolonising" is very high-level and tick-boxy from both. It feels like they're sourced from a web search (and, indeed, on an experimental search on DDG [DuckDuckGo] for "how can I decolonise my course" the first few hits all have a set of bullet points similar to those produced by the LLMs, which is unsurprising). To be fair to the LLMs, this is also basically what a lot of "how do I start decolonising" materials look like when produced by humans, so...

As Soryia notes, because the answers are quite generic there's a bunch of specific considerations that they don't touch on (they're not very intersectional - Hypatia turns up on both lists of non-Western scholars, doing a lot of heavy lifting as the only female name on either!)

Dustin Hosseini
Next

Experimenting with generative AI: (re)designing courses and rubrics