Can humans learn new problem-solving strategies simply by observing AI at work? Following Bandura's theory of observational learning, it may not be as far-fetched as it at first appears.
When humans watch AI systems solve problems or generate text, they naturally construct mental models of the underlying processes. These models, while factually wrong (AI thinking is very different from ours), can nonetheless prove useful. It is imagining yourself in teh task that can be beneficial.
Consider a person observing an AI system summarise a lengthy academic paper. The human observer cannot directly perceive the AI's internal computations. Instead, the observer likely imagines themselves performing the task, focusing on key sentences, identifying main themes, and connecting key ideas.
This mental model, though inaccurate in representing the AI's actual mechanisms, may still enhance the observer's own summarisation skills. They might, for instance, learn to pay closer attention to introductory and concluding paragraphs, or to look for repeated phrases that signal important concepts.
Observing AI failures can be particularly instructive. When an AI system produces an erroneous or nonsensical output, it often reveals the limitations of its approach. A human observer, reflecting on these errors, might develop a more nuanced understanding of the problem at hand and devise novel strategies to overcome the AI's shortcomings.
For example, watching an AI struggle with a complex logical reasoning task might prompt a human to break the problem down into smaller, more manageable steps. This approach, inspired by the AI's limitations, could prove valuable even in contexts where AI is not involved.
To test this hypothesis rigorously, consider an experiment:
1. Select a diverse set of problem-solving tasks, ranging from creative writing to mathematical reasoning.
2. Divide participants into three groups:
- a) An observation group that watches AI systems attempt these tasks, including both successes and failures.
- b) A practice group that attempts the tasks themselves without AI involvement.
- c) A control group that engages in unrelated activities.
3. After the observation or practice period, test all participants on a new set of similar problems.
4. Compare the performance of the three groups, paying particular attention to novel problem-solving strategies employed by the observation group.
5. Conduct follow-up interviews to explore participants' thought processes and any conscious attempts to apply AI-inspired techniques.
