Curating Tomorrow: AI, Authority, and the Ethics of Visibility

In the previous modules, we examined how traditional institutions such as universities and publishers once held the authority to curate knowledge, and how this role is now increasingly assumed by AI-driven algorithms that shape visibility, influence learning, and redefine epistemic authority.

But what comes next?

In this module, we explore how to balance algorithmic curation with human expertise, ethical oversight, and inclusive design—so that digital education remains equitable, credible, and transparent.

Looking Ahead

Using futuring methods, you will critically engage with scenarios that imagine the possible, probable, and preferable futures of knowledge dissemination:

Can we design AI-powered education systems that prioritise accuracy, diversity, and fairness, rather than virality or corporate gain?
What might the future of knowledge look like in 10, 20, or 50 years?
How do we strike a meaningful balance between automation and human judgement?

Through reflection, analysis, and speculative design, you will assess what roles AI, institutions, and learners might play in shaping the future of knowledge—and consider how to preserve epistemic integrity in an age of intelligent systems.

As we move toward a future increasingly integrated with AI, the boundaries between human and machine epistemic authority are becoming more porous. Unlike traditional knowledge systems that valued permanence, peer review, and archival stability, today’s AI-powered platforms are governed by predictive algorithms, constant optimisation, and commercial imperatives.

Yet this transformation is not solely technological—it is ideological. It marks a shift in how knowledge is defined, who curates it, and how it is legitimised and circulated in digital education.

The future of knowledge, as imagined by technologists and educational researchers, envisions dynamic systems in which AI takes on editorial and even pedagogical roles (Youvan, 2025). While this offers speed, scalability, and personalisation, it simultaneously disrupts traditional notions of authority, visibility, and trust in educational contexts.

As Knox, Williamson, and Bayne (2020) argue, automation in education introduces “machine behaviourism”—a logic that privileges prediction and control over critical thinking or learner autonomy. Likewise, Williamson (2017) reminds us that data-driven platforms may narrow the scope of inquiry, privileging what can be measured over what matters.

This module invites you to critically examine these shifts and explore how we might design future-facing knowledge infrastructures that balance AI innovation with human values. How can we ensure that automation supports, rather than replaces, epistemic diversity? What role should educators, students, and policy-makers play in shaping ethical, inclusive systems that protect the integrity of knowledge in the digital age?

Future Worlds: Designing Knowledge Systems for 2050

In this speculative activity, you will use futuring methods to imagine and analyse how knowledge might be curated in the year 2050. What systems might dominate? What values will guide them? And what will the consequences be for education, truth, and epistemic integrity?

Step 1: Choose a Future Scenario

Select one of the following imagined futures where different actors take charge of knowledge curation:

AI Supremacy: AI systems fully control knowledge visibility—from academic publications to public discourse. Human editors are obsolete.
Human Revival: A global backlash removes AI from content curation. Traditional institutions (universities, publishers, governments) reclaim sole gatekeeping power.
Hybrid Model: AI tools assist human experts, but final editorial decisions rest with people. Knowledge systems balance automation with human oversight.
Decentralised & Open: A blockchain-based infrastructure enables collective, transparent knowledge curation without a central authority.

Step 2: Develop Your Scenario

In a short paragraph, describe your chosen future:

What are its advantages and disadvantages?
Who holds power over visibility and legitimacy?
How does this affect education, equity, and truth?

Post your response as a new entry on this Padlet.
Be sure to read and comment on at least one peer’s future scenario.

Ethical AI: Should Machines Decide What We Learn?

As AI increasingly influences what we see, read, and engage with, educators and learners must grapple with a central ethical question: Should machines have the power to decide what counts as legitimate knowledge?

In this activity, you will take a position on the role AI should play in knowledge curation and engage in a critical dialogue with your peers.

Step 1: Take a Position

Choose one of the following stances:

Position 1: AI should be the primary curator of knowledge – It offers speed, scale, and removes human biases.
Position 2: AI should assist, but humans must retain final control – Accountability and ethics require human judgement.
Position 3: AI should be heavily restricted in curating knowledge – To prevent algorithmic bias and corporate manipulation.

Step 2: Discussion & Debate

Share one argument in favour and one argument against your chosen position.
Respond to at least one peer’s post with a supporting point, counterargument, or relevant example. Submit your contribution in this shared Google Doc.

The Future of Learning: Who Decides What We Know?

As we look ahead to the year 2050, the landscape of education has changed dramatically. AI now plays a central role in how knowledge is curated, delivered, and validated. But who—or what—should control these learning systems?

In this activity, you’ll choose from four imagined future models of education, explore their benefits and risks, and reflect critically on whether they represent desirable futures.

Step 1: Choose a Future Knowledge Model

Pick one of the following scenarios where knowledge is structured and taught:

AI-Powered Schools: Artificial Intelligence tailors education to each student’s learning history and preferences, delivering fully automated, personalised content.
Human-Expert Institutions: Traditional educators and universities remain the sole knowledge gatekeepers, using minimal AI for support.
Open-Source Learning: Education is democratised. Communities crowdsource content, and public voting decides what is included—like a Wikipedia for schools.
Corporate Learning Platforms: Tech companies curate and deliver learning via algorithmic feeds, replacing traditional curricula with optimised engagement-driven content.

Step 2: Answer These Key Questions

Reflect on your chosen model by thinking of the following:

What are the benefits of this system?
Does it make learning more personalised, accessible, or efficient?
What are the challenges or risks?
Might it introduce new forms of bias, misinformation, or inequality?
Would you want to learn in this system?
Why or why not? What values does it promote—or neglect?

Readings & Reflections

As AI takes on curatorial and editorial functions in education, who or what should shape the future architecture of knowledge, and how should we ensure it remains accountable?

"The Future of Knowledge: AI as a Living Publication and the Evolution of Research Dissemination"
Youvan D. C. — Open License: ResearchGate (CC BY-NC-ND 4.0)
"The Digital Transformation of Knowledge Order: A Model for Knowledge Gatekeeping"
Journal of Documentation — Open License: Emerald Insight (CC BY-NC-ND 4.0)

As we turn to the future, it becomes increasingly clear that AI is not only reshaping how knowledge is delivered, but also how it is defined, stored, and validated. In Youvan’s (2025) compelling concept of AI as a “living publication”, research dissemination is imagined as a continuously evolving process, driven by real-time data integration and adaptive learning systems. Knowledge is no longer a fixed entity but a fluid, dynamic stream; updated, refined, and recirculated by AI. While this model enhances accessibility and responsiveness, it also raises fundamental concerns: Who curates the evolution? What values are embedded in these updates? And what happens to minority or dissenting knowledge that does not “fit” algorithmic models?

The Journal of Documentation article (2024) advances this discussion by outlining a model of digital knowledge gatekeeping. It proposes that contemporary gatekeeping is increasingly distributed across humans, platforms, and automated systems, creating new tensions between control and openness. Traditional gatekeepers, editors, educators, librarians once ensured epistemic accountability through human judgment. Today, AI-enabled platforms often take over that role, but their operations are guided by predictive algorithms and commercial incentives rather than educational or scholarly values.

These readings invite us to ask whether it is possible, or desirable for AI to act as a steward of knowledge. Can we embed epistemic integrity into future knowledge infrastructures, or will speed and scale always overtake care and critique? As educators and digital professionals, we must begin to design for futures that restore human agency in the face of automated epistemologies. We need systems that not only respond to user preferences but also challenge, surprise, and deepen understanding.

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