Back in 2017, I had a post entitled “Defining Online: Ask the Machines?“. The post discussed the implications of AI developing knowledge that humans may not fully comprehend, based on Dave Weinberger’s Backchannel article. Key points (as summarized by Claude for me) included:
- Machine learning was creating complex models beyond human understanding, challenging our traditional approach of simplifying the world.
- Examples like Google’s AlphaGo demonstrated the machine’s ability to excel at tasks without being able to explain its methods to humans.
- I questioned whether our historical pursuit of simplicity in understanding the universe was misguided, given the complexity of interrelated variables in the real world.
- Tom Friedman’s book, Thank You For Being Late: An Optimist’s Guide to Thriving in the Age of Accelerations, was referenced, suggesting that Mother Nature’s adaptability and resilience could be a model for facing the future, as it was able to”crunch the numbers” without simplifying models.
- I tied these ideas to the challenges of defining “online learning” in higher education, noting the difficulty in categorizing various forms of e-learning.
I concluded by wondering if machine intelligence could analyze existing data to provide new insights into online learning that might be difficult for humans to understand…and expressed amazement at living in an era where such possibilities could be contemplated.
I am amazed no more (or even more!).
AI has certainly progressed significantly beyond what even Weinberger envisioned in 2017. Large language models like Claude that I use or ChatGPT4o can now engage in complex dialogues, answer questions, and even assist in academic research.
And after the pandemic, the notion that online learning can be defined has blurred. I might have been asking the right question in 2017…but new questions are now required.
- How do we ensure equity and accessibility in an increasingly AI-driven educational environments?
- What are the implications of AI-generated content for academic integrity and authorship?
- How can we effectively teach critical thinking and information literacy in an age of sophisticated AI language models?
- What role should human educators play as AI becomes more capable of delivering personalized instruction?
- How do we balance the benefits of data-driven personalization with privacy concerns in educational settings?
- What new skills and literacies are required for students to thrive in a world where AI is ubiquitous?
- How can we design educational systems that are as adaptable and resilient as the natural systems described by Friedman?
- What are the long-term implications of relying on AI systems whose decision-making processes we don’t fully understand?
And to answer those new questions, I suspect many of us will indeed be asking the machines!
While many of the ideas in my 2017 post remain relevant, the rapid advancement of AI and machine learning, coupled with the global shift towards digital learning accelerated by the pandemic, has dramatically reshaped the landscape of online education. The questions we face now are less about defining online learning and more about navigating the complex interplay between human and artificial intelligence in education.
{Graphics: DALL-E}