Rethinking Distributed Learning and AI : LTEC 6040 Final Reflection
I thought I had a strong understanding of online and distributed learning. I have taught for more than 10 years in both settings and I am currently working with an AI tutoring system, so I expected this course to be more of a review. It was not. This course pushed me to be more precise about what distributed learning actually is and what good design requires. My favorite part of the course was the reading. Minds Online was especially useful because it connected cognitive principles directly to instructional decisions. The idea that effective technology depends on alignment with learning goals, structured practice, and timely feedback stood out to me . I see this clearly in my own teaching. When those elements are present, students improve. When they are not, technology adds unnecessary complexity instead of support.
One of the most useful in-class discussions was the distinction between distributed learning, distance learning, and online learning. Early on, I treated these as mostly interchangeable. After working through the definitions and examples, I now see distributed learning as a design approach rather than a delivery method. It removes limits on time, place, and pacing and gives more control to the learner. That clarified something important for me. When students are working across different schedules and environments, instruction has to be more structured and more intentional. Directions must be clear, expectations must be explicit, and interaction cannot be assumed. This connects directly to our discussion of cognitive load. Technology does not improve learning on its own. It can overwhelm students if it introduces unnecessary steps or distractions. This is something I am actively applying to both my classroom instruction and my research, especially as I continue to think through how the role of the online instructor is changing.
Our work with learning theories reinforced this idea. Looking at cognitivism, connectivism, and heutagogy side by side made it clear that no single theory explains learning in distributed environments. Cognitivism focuses on how students process information and manage cognitive load. Connectivism focuses on networks, access to information, and learning through connections. Heutagogy focuses on learner control and self-direction. The key takeaway for me is that effective instructional design requires combining these perspectives. It was emphasized that the “magic” of online teaching is intentionally mixing these lenses rather than defaulting to just one. That idea aligns with what I see in practice. Students need structure, but they also need flexibility and opportunities to take ownership of their learning. The digital library work supported this as well. Using tools like Perplexity, Elicit, and Research Rabbit gave me more efficient ways to locate, organize, and connect research. That is directly useful for my dissertation work and has already improved how I approach literature searches.
I was not able to watch the documentary Ghost in the Machine with the class, but I watched it later on my own. The film, directed by Valerie Veatch, takes a critical stance on AI and argues that modern systems are connected to historical ideas about intelligence, efficiency, and ranking, including eugenics. The historical examples presented in the film are real and serious. The misuse of intelligence testing and policies like forced sterilization are important to acknowledge. Where I struggled was with how directly the film connected those ideas to modern AI systems. The connection felt stretched at times. Bias in AI is a real issue, but I am not convinced that current systems are a direct continuation of those earlier frameworks in the way the documentary suggests. At the same time, the film was still valuable because it pushed me to think more carefully about the assumptions built into AI systems. Even if the historical connection is not as direct, the broader point remains important. AI is not neutral. It reflects the data, design choices, and priorities of the people who build it and the information it draws from, and this connects directly to my own work. I am doing research with an AI tutor that focuses on structured, step-by-step problem solving rather than giving quick answers. Before this course, I was primarily focused on whether it improves performance and reduces cognitive load. Now I am also thinking about how the system shapes student thinking, what assumptions are built into it, and how transparent it should be. The documentary did not change my view of AI in a negative way, but it did make it more critical and more grounded. I still see strong value in AI as a support tool, especially in structured learning environments, but I see more clearly that it needs to be designed intentionally and evaluated beyond just whether it works.
One area of the course that could be improved is the assessments. There were a few instances where quizzes or exams included errors and flawed questions. This did not affect my grade, but it did stand out. In a course focused on distributed and online learning, clarity in assessment is especially important because students rely heavily on written instructions and question design. This is a relatively small issue, but it connects directly to the principles we discussed throughout the course.
This course helped me connect theory to practice in a clear and practical way. It strengthened how I think about instructional design in distributed environments and made me more intentional about how I use technology. It also pushed me to think more carefully about the role of AI in learning. I am leaving with a clearer framework for design and better questions about how to use technology in a way that actually supports student learning.
