Human Learning vs. Machine Learning: how do they differ?

Learning as a Lens in the Generation of AI

Over the last few months, I’ve worked more with AI and reflected on the differences between human and machine learning. This journey has caused me to revisit the four big ideas about learning that I have embraced since I started faculty development training in 2015.

Learning is a Cyclical Process

Both AI and humans approach learning cyclically but in different ways. For AI, it’s about continuously adding data to its database. Human learning, however, is cyclical because our context and we as individuals change over time. Our evolving experiences and environments influence each cycle of learning. In AI, the cyclical process merely accumulates more information, while in humans, it represents a transformative journey where both the context and the learner evolve, reshaping the learning experience.

Learning Depends on the Action of the Learner

The rise of AI might most challenge the idea that learning depends on the learner’s actions. While AI can automate many tasks, the quality of its outputs still heavily depends on how humans structure their queries and interact with them. Active learning, engagement, practice, and reflection remain crucial for humans. This principle is fundamental and must be upheld as a cornerstone of meaningful learning in the age of AI.

Learning Must Be Transferred from One Context to Another

Traditional learning environments often fail to facilitate this transfer effectively, leaving gaps between theoretical knowledge and practical application. Human learning excels when it connects classroom knowledge to real-world scenarios, enhancing its relevance and utility. This capability to transfer learning underscores the adaptability and applicability of human knowledge in varied situations. Facilitating this transfer allows human-focused learning to differentiate itself from machine learning.

Learning Results in Change

As I delve deeper into distinguishing human learning from machine learning, I must emphasize that learning results in change, particularly in the learner. While AI can process and analyze vast amounts of data, it doesn’t facilitate the transformative shift that human learning brings. Human learning impacts our behaviors, perspectives, and abilities, driving personal and professional growth. This concept might need to be relabeled as “learning results in change to the learner” to highlight its importance in human transformative learning.

Transformative Change

Understanding the nuances of learning in the age of AI is crucial. While machine learning offers valuable tools and efficiencies, it does not replicate the transformative power of human learning. Recognizing and embracing the cyclical nature of learning, the active role of the learner, the importance of contextual transfer, and the transformative change that learning brings, we can better appreciate the unique strengths of human learning. At Learning Forte, these principles guide our efforts to enhance learning experiences and highlight the distinct value of human growth and development.

written by Stacy Williams-Duncan
June 2024