Intellectual Property and Open Education
Over the past few weeks, I’ve become more interested in how digital literacy extends beyond just using technology and into understanding how information, data, and ownership actually functions for modern systems. As someone studying computer science and learning more about how companies operate in a data-driven environment, I’ve started to think more critically about who owns the content we interact with, how it’s used, and what responsibilities come with creating and sharing it. This module’s focus on open education, intellectual property, and copyright pushed that thinking further, especially in terms of how these ideas apply to the tools and platforms I use on a regular basis.
Part of this module was the conversation with Cable Green, where he spoke about open education, licensing, and how access to knowledge is evolving. His insights helped frame a lot of what I was already noticing in my own work. One of the biggest takeaways for me is the distinction between access and ownership. Open education promotes accessibility; open textbooks, shared resources, and collaborative platforms. However, that doesn’t mean ownership disappears. As he emphasized, intellectual property “still exists in these systems; it’s just structured differently through things like Creative Commons and open licenses”.
Another key point I found in this conversation surrounds the unclear ownership that occurs in newer technologies, especially Artificial Intelligence. These discussions around generative AI models being trained on copyrighted data without clear permission raises many questions on already existing data and where we draw the line between inspiration, reuse, and ownership. This idea is something that will directly impact how current and future systems are built and regulated moving forward in this new age of growing data and technology.
This is where digital literacy becomes more than just knowing how to use tools. It’s about understanding the systems behind them; who owns the data, how it’s being used, and what rights you have as both a creator and a user. Online users are constantly interacting with other people’s work, whether it’s libraries, datasets, and especially AI-generated content. As highlighted in the UNESCO Media and Information Literacy framework, most digital content is automatically protected by copyright, meaning that even in open environments, users still have a responsibility to recognize authorship and follow usage rights rather than assuming content is free to use.
What I find most interesting is that open education and open-source culture are both trying to solve the same problem: making knowledge more accessible without removing accountability. But there’s still tension there. The more open systems become, the more important it is to understand the rules that govern them—otherwise it’s easy to misuse content, even unintentionally. As I started looking more into current discussions around this, I came across an article highlighting how major tech companies are being accused of scraping millions of copyrighted songs to train AI models. What stood out to me wasn’t just the scale, but what it represents. If AI systems are being built on datasets this large without clear consent, it shows how far ahead the technology is moving compared to the policies that are supposed to regulate it.
From my perspective, this makes the idea of “ownership” feel a lot less defined. It’s not just about whether something is open or closed anymore, rather how data is collected, reused, and transformed at a scale that’s hard to track. At the same time, what makes this issue even more complex is how quickly it’s evolving. There are now dozens of ongoing lawsuits involving AI companies, artists, authors, and media organizations, all trying to define what ownership actually looks like in this space. From cases involving generated code to lawsuits over scraped images, books, and news articles, it’s clear that the boundaries of copyright are still being actively negotiated. A continuously updated list of these cases shows just how widespread and unresolved this issue is, highlighting that this isn’t a single problem, but an entire shift in how intellectual property is understood in the age of AI
Reflecting on this, it changes how I think about my own work. Whether it’s a project, a portfolio, or an online repository, there’s always a layer of ownership and responsibility behind it. It’s not just about building something, but rather understanding where it comes from, how it’s shared, and what rights are attached to it.
References
UNESCO. (2024). Unit 7: Intellectual Property and Authorship Recognition. https://www.unesco.org/mil4teachers/en/module3/unit7
Simpson, W. (2025). “The largest intellectual property theft in human history”: Big tech companies accused of scraping millions of copyrighted songs to train AI models. MusicRadar. https://www.musicradar.com/music-industry/the-largest-intellectual-property-theft-in-human-history-big-tech-companies-accused-of-scraping-millions-of-copyrighted-songs-to-train-ai-models
University of British Columbia. (2026). AI Copyright Lawsuits. https://wiki.ubc.ca/AI_Copyright_Lawsuits
Week 5: Intellectual Property, Copyright, Open Licensing, and more. EDCI 136 – Digital Literacy. University of Victoria. https://edtechuvic.ca/edci136/2025/02/03/week-5-intellectual-property-open-licensing-curation-and-more/