When we talk about "low-quality AI," we're referring to AI systems that are less sophisticated, less accurate, or more limited in their capabilities. These systems, interestingly, can sometimes lead to more critical and independent thinking from users. Here are some examples of low-quality AI:

1. Early Chatbots: Simple rule-based chatbots that follow predetermined scripts often provide responses that are clearly artificial or off-topic. Users quickly learn to be skeptical of these responses.

2. Basic Grammar Checkers: Early versions of tools like Grammarly or built-in word processor spell-checkers often make obvious mistakes or miss context-dependent errors. This encourages users to double-check and think critically about language use.

3. First-Generation Voice Assistants: Early versions of Siri, Alexa, or Google Assistant frequently misunderstood commands or provided irrelevant information, prompting users to rephrase or seek information elsewhere.

4. Rudimentary Translation Tools: Early online translation services often produced word-for-word translations that ignored context and idioms, requiring users to analyze and correct the output critically.

5. Basic Recommendation Systems: Early product recommendation algorithms on e-commerce sites often suggested items based on simplistic criteria, leading to clearly irrelevant suggestions that users learned to ignore or question.

6. Primitive Image Recognition: Early image recognition AI often made obvious misclassifications, making users skeptical of AI interpretations of visual data.

7. Simple Automated Customer Service Systems: Basic automated phone or chat systems that struggle with complex queries, forcing users to think creatively about expressing their needs.

8. Early Predictive Text: Primitive predictive text on mobile phones often suggests words clearly out of context, making users more aware of their language choices.

EXAMPLE: WRITING SKILLS

I recommend that all my students use grammar checkers like Grammarly or Hemmingway. Surprisingly, those who engage with these tools show more improvement in their writing skills over the semester. They learn to catch errors the AI misses and develop a keener eye for style and structure.

EXAMPLE: The Data Analysis Project

On a business analytics team, one colleague used a cutting-edge AI for data interpretation, while another used a more basic statistical tool. The colleague with the basic tool had a deeper understanding of the underlying data principles and produced a more insightful, albeit less polished, analysis.

The Takeaway?

These examples illustrate a crucial point: when AI is clearly imperfect, users remain engaged and critical. They're forced to use their own judgment, fact-check, and think independently. This "productive friction" can lead to deeper learning and skill development.

However, it's important to note that the goal isn't to use inferior tools purposely. Instead, we should strive to use AI to complement and enhance human skills rather than replace them. As educators and professionals, our challenge is to manage the power of advanced AI while maintaining the critical thinking and independence that interaction with simpler systems naturally encourages.