LYNX LIFE LIBRARY

The rapid evolution of artificial intelligence (AI) in recent years has ignited a critical debate about the merits of open-source versus closed-source AI systems. These two approaches to AI development—open-source and closed-source—each carry distinct implications for innovation, accessibility, and societal impact. As AI becomes increasingly integrated into education, industry, and the economy, understanding these differences is essential. Moreover, as we learn to collaborate with AI, the choice between open and closed systems has profound implications for ensuring that humans—particularly students—remain central to the process.

Open-source AI refers to systems whose source code is publicly available, allowing anyone to inspect, modify, and distribute it. Examples like DeepSeek and Meta Llama embody this philosophy, fostering transparency and community-driven innovation. In contrast, closed-source AI, such as ChatGPT or Gemini, operates under proprietary models, where the underlying code is owned and controlled by a single entity. This fundamental distinction creates ripple effects across accessibility, customization, and ethical considerations.

One of the most significant benefits of open-source AI is its democratizing effect. By making AI tools accessible to a broader audience, open-source systems empower students, researchers, and small businesses to experiment and innovate without the barriers of high costs or restrictive licenses. This aligns with the idea that humans must work *with* AI, not simply rely on it. Open-source AI encourages users to understand the technology, adapt it to their needs, and contribute to its improvement. For students, this means gaining hands-on experience with AI development, fostering critical thinking, and ensuring they remain active participants in the process rather than passive consumers.

Closed-source AI, while often more polished and user-friendly, can create a dependency on the provider. Users may not fully understand how the system operates, limiting their ability to customize or troubleshoot it. For example, at CU Denver, students have access to a free, protected version of Microsoft Copilot through their university credentials. As a primarily closed-source product backed by a large company, there is a dedicated department at CU Denver and Microsoft to assist with troubleshooting and fixes when issues arise. However, because the code cannot be inspected, this can lead to questions about transparency and may not fully support the experience students desire when engaging with AI tools.

The economic implications of open-source AI are also noteworthy. In the U.S., open-source systems can stimulate competition and innovation by lowering entry barriers for startups and smaller enterprises. This can lead to a more diverse and resilient AI ecosystem, reducing reliance on a handful of dominant players. Conversely, closed-source AI may concentrate power and economic benefits within a few corporations, potentially stifling competition and innovation.
That said, open-source AI, such as DeepSeek and Meta Llama, is not without challenges. However, these challenges also present opportunities for collaboration, as interested parties work together to address shared concerns. This collective spirit reinforces the principle that humans must remain at the center of AI development, deployment, and use. Choosing between open-source and closed-source AI is not just a technical decision but a philosophical one. By keeping humans central to the process, we can ensure that AI serves as a tool for empowerment rather than a replacement for human ingenuity.

At the heart of the debate between open- and closed-source AI is a crucial idea: understanding how an AI tool works is essential for critically evaluating its output, rather than accepting it at face value. This is a cornerstone of AI literacy. The Learning Resources Center at CU Denver supports students in developing this literacy by teaching them how to use AI for studying, time management, and other student success skills. Through workshops and online modules, students are introduced to a variety of tools and encouraged to read terms of service, privacy policies, and other documentation to understand how these tools operate. Ultimately, it is up to each student to balance the benefits and concerns that arise from using any kind of artificial intelligence.

While open and closed AI might seem like a binary choice, some tools blend both approaches. For instance, Goblin.tools uses a combination of open-source and closed-source AI on the backend to “help neurodivergent people with tasks they find overwhelming or difficult.” This tool assists with meal planning, decision-making, topic explanations, effective communication, brain dump organization, and task and time estimates. Similarly, Microsoft Copilot, though primarily closed-source, incorporates some open-source elements in its development. It can help students plan meals based on available ingredients or organize tasks over several weeks to ensure a class project is completed on time. Completely open-source tools can also perform these tasks, and if students want to delve deeper, they can even use open-source code to create their own chatbots. While most large language models can handle a wide range of tasks, the output always deserves careful scrutiny to ensure quality. For students, experimenting with AI tools is a great way to deepen their understanding and improve their AI literacy.

The Learning Resources Center has taken steps to explore AI tools on behalf of students, testing platforms like Zoom AI Companion and Microsoft Copilot, which the university supports. Given how rapidly the AI landscape evolves, it can be challenging to keep up with the latest tools and their functionalities. The Learning Resources Center is available to assist students individually, offering guidance on how AI can be used to support student success. Additionally, the LRC provides resources for studying and time management that don’t rely on AI. Whether students need help with AI tools or traditional study strategies, the LRC is a valuable resource. If they don’t have the answer, they’ll investigate. Students are encouraged to visit the LRC on the first floor of the Learning Commons or reach out via email at lrc@ucdenver.edu.
In the end, the choice between open-source and closed-source AI is about more than just functionality—it’s about fostering a deeper understanding of the technology and ensuring that humans remain at the forefront of its development and use. By embracing both the opportunities and challenges of AI, we can create a future where technology empowers rather than overshadows human potential.