Understanding Content Prioritization and Its Hidden Impact on Learning
Imagine walking into a school library and asking for resources on biology. But instead of curated content aligned with your curriculum, the librarian hands you materials based on how many students skimmed them or how long they stared at the cover. That’s the digital reality of content prioritization.
As we rely more on search engines and AI tools for knowledge, it’s important to ask:
Who decides what content appears first? And how is this shaping the way we teach, learn, and understand the world—especially in science and education?
What Is Content Prioritization?
Content prioritization is how digital platforms like Google, YouTube, or Instagram decide what you see first. Algorithms sort and rank content based on a mix of popularity, perceived relevance, user behaviour, and more. But these systems often reflect existing biases, and their decisions are far from neutral.
Safiya Umoja Noble (2018) explains that such algorithmic systems, especially Google Search, amplify dominant narratives while pushing marginalized perspectives into the background.
For instance:
When I searched for “cell biology animations” for a diverse high school class, the first videos often came from big EdTech publishers or YouTube influencers—but many lacked inclusive or culturally relevant analogies.
A search for “Indian scientists” returned mostly historical male figures like C.V. Raman or Jagadish Chandra Bose, while brilliant modern-day contributors—especially women and underrepresented researchers—were missing from top results.
Educational searches like “interactive tools for biology” often prioritize tools with the best SEO, not necessarily the best pedagogical value or accessibility for diverse classrooms.
These examples show that what gets seen isn’t always what’s most accurate, inclusive, or useful.
How These Algorithms Work (in Simple Terms)
Content prioritization algorithms weigh signals such as:
- PageRank (how many sites link to a page)
- Engagement (likes, clicks, time spent)
- Search history and location
- Recency and updates
If a resource has been widely shared, linked, or liked, it’s pushed to the top—creating a visibility loop where the “rich get richer.” But this doesn’t always reflect quality, diversity, or educational value.
As Noble (2018) puts it, when companies like Google become the “largest digital repository in the world”, they effectively control access to knowledge (p. 157)—and that has consequences, especially for communities already underrepresented in science and education.
Why This Matters in Education
As a biology teacher and a student in educational technology, I see firsthand how students often rely on the first page of Google or top YouTube results when researching.
One example that stuck with me was when a student searched “Why is the sky blue?” and ended up watching a highly entertaining—but scientifically flawed—video because it ranked higher than actual physics-based explanations. Another searched for “GMO pros and cons” and was flooded with corporate-sponsored content that didn’t present a balanced view.
For learners in diverse classrooms—some of whom may be multilingual, neurodiverse, or coming from different learning backgrounds—this creates barriers. Algorithms are not designed to prioritize inclusive or adaptive pedagogy; they’re designed to increase clicks.
This matters not just in K–12 classrooms but also in higher education, where students conducting literature reviews or online research often miss out on open-access or indigenous knowledge sources because those don’t rank well in standard search engines.
PageRank’s Quiet Influence on My Life
Google’s PageRank algorithm changed the way we navigate information. It assumes that the more links a page gets, the more “important” it is. But importance isn’t the same as accuracy, equity, or relevance.
In my personal and professional life, PageRank:
- Prioritizes commercial tools over open educational resources when I search for LMS platforms—even though the open tools may better serve the communities I work with.
- Bumps down newer blogs or collaborative projects like The Speaking Tree because they don’t yet have enough inbound links—even though the content is original, thoughtful, and inclusive.
- Surfaces mainstream biology content for my students while suppressing local, decolonized science perspectives I actively try to incorporate.
Can I Impact PageRank?
Not directly—but yes, in small, meaningful ways.
Here’s what I’ve started doing:
- Publishing blog posts and learning content under shared, high-authority domains like Medium or university repositories.
- Encouraging my network of educators to cross-link resources from our platforms—boosting visibility for content that might otherwise stay hidden.
- Teaching my students digital literacy by asking: “Why do you think this showed up first? Who benefits from you clicking this link?”
I also build community-focused projects like The Speaking Tree to promote collaborative knowledge sharing—a kind of counter-algorithmic move where we decide what’s important, not just what’s viral.
The Bigger Picture
As Noble (2018) powerfully argues, content prioritization algorithms are not neutral—they’re reflections of who holds power and whose knowledge is valued. And when we depend on systems like Google to shape what we learn, we risk deepening the very inequities we seek to challenge.
Educators, technologists, and learners must become more than passive consumers of search results. We need to be critical curators, actively amplifying underrepresented voices and challenging the way content is ranked, framed, and delivered.
Because the question isn’t just “What did you learn today?”—it’s “Who decided that was what you should learn?”
Appendix
- Algorithmic Bias in Educational Systems
Tetteh, G. K. (2025). Algorithmic bias in educational systems. World Journal of Advanced Research and Reviews, 17(1), 236–240. https://journalwjarr.com/sites/default/files/fulltext_pdf/WJARR-2025-0253.pdf - Impact on Minoritized Students
Taylor, J. (2024, September 22). Algorithmic bias continues to impact minoritized students. Diverse: Issues In Higher Education. https://www.diverseeducation.com/reports-data/article/15679597/algorithmic-bias-continues-to-impact-minoritized-students - Social Media Algorithms and Misinformation
Rodriguez, L., & Ahmed, S. (2024). The influence of social media algorithms on racial and ethnic misinformation: Patterns and impacts. ResearchGate. https://www.researchgate.net/publication/387503351_The_Influence_of_Social_Media_Algorithms_on_Racial_and_Ethnic_Misinformation_Patterns_and_Impacts - Digital Transformation and Marginalized Communities
Alcaraz-Domínguez, S., Martín-García, A. V., & Moral-Rodríguez, M. E. (2025). Addressing the social impact of digital transformation: A project with marginalized communities. Frontiers in Education, 10, Article 1534104. https://doi.org/10.3389/feduc.2025.1534104 - Algorithmic Bias in Student Progress Monitoring
Mohamed, A. A., & Naseem, A. (2024). Bias in AI student monitoring algorithms: An analysis of age, disability, and gender. Computers and Education: Artificial Intelligence, 5, 100144. https://doi.org/10.1016/j.caeai.2024.100144 - AI and Racial Justice
Royster, R., & Mertens, J. (2024, August 1). AI and racial justice: Navigating the dual impact on marginalized communities. Nonprofit Quarterly. https://nonprofitquarterly.org/ai-and-racial-justice-navigating-the-dual-impact-on-marginalized-communities/
References
Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. NYU Press.
