The Engineered Student: On B. F. Skinner’s Teaching Machine and Hurry Up And Wait - The Challenge of AI Workflows

The Engineered Student: On B. F. Skinner’s Teaching Machine and Hurry Up And Wait - The Challenge of AI Workflows

In the world of education, the concept of the "engineered student" has been a topic of discussion for decades. B. F. Skinner, a renowned behaviorist, proposed the idea of using teaching machines to manipulate student behaviors and enhance learning outcomes. However, his theory clashed with certain classroom practices that violated the core principles of his behaviorist theory.

Skinner believed that immediate feedback was crucial for effective learning. In his teaching machine, students were not told immediately whether they had answered questions correctly or incorrectly. This lack of immediate feedback hindered the application of operant conditioning, a method of administering rewards or punishments to shape behaviors. Skinner argued that behaviors could be manipulated and strengthened through "schedules of reinforcement," as outlined in his book co-written with Charles Ferster in 1957.

Behaviorists emphasized the importance of analyzing behaviors rather than speculating on inward motivations or sensations. Scientific study, according to Skinner, required a focus on activities and actions rather than subjective experiences. However, the traditional classroom setup often neglected this aspect of behaviorism, relying more on subjective assessments and delayed feedback.

Interestingly, the challenge of implementing AI workflows in various industries bears some resemblance to Skinner's dilemma. AI advancements have revolutionized certain parts of workflows, but the overall process remains inefficient due to delays in other steps. This phenomenon has been aptly described as a "hurry up and wait" situation, where accelerated processes are followed by prolonged waiting periods.

In large companies, layers of approval and sign-offs often slow down workflow efficiency. Even if generative AI is used to speed up content creation, the content may end up sitting idle for days, awaiting approval. This realization prompted the need for teaching machines to not only create but also review content. The concept of machine approvals for customer-facing content emerged as a solution to the "hurry up and wait" problem.

While some creation platforms are incorporating similar functionality, a centralized and independent review process seems more rational. By focusing on machine approvals, companies can bypass the waiting period and keep the workflow moving. However, this approach requires additional capital and competition across multiple product categories.

To tackle the challenge of AI workflows, it is essential to identify the slowest step in a particular workflow and focus on improving its efficiency. Even if the subsequent steps in the process still rely on human involvement, speeding up the slowest step can bring significant value and overall acceleration to the workflow.

In conclusion, the concept of the "engineered student" and the challenge of AI workflows share common themes of efficiency, feedback, and enhancing overall outcomes. By embracing the core principles of behaviorism, such as immediate feedback and behavior analysis, educators can create more effective learning environments. Similarly, in the context of AI workflows, identifying and addressing bottlenecks can lead to improved efficiency and productivity. By focusing on the slowest step and considering centralized review processes, companies can overcome the "hurry up and wait" dilemma.

Actionable Advice:

1. Incorporate immediate feedback into educational practices to align with behaviorist principles and enhance student learning outcomes.
2. Identify the slowest step in AI workflows and prioritize its improvement to achieve overall efficiency gains.
3. Explore the implementation of centralized and independent review processes to avoid delays and accelerate workflow completion.

By incorporating these actionable advice, educators and professionals can navigate the challenges associated with creating efficient systems that prioritize feedback and enhance productivity.