The Search Evolution: Balancing Inquiry and Generation in AI

The Search Evolution: Balancing Inquiry and Generation in AI

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These conversions bring a big smile to my face as I feel that they really affirm all the hard work I put into this Substack week-in and week-out.

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As an educator who has spent the past two years implementing AI in secondary education, I've watched the evolution of AI search capabilities with both excitement and concern. Yesterday's simultaneous announcements from major AI players about their search capabilities marks a pivotal moment in how we access and process information. The convergence of these announcements - OpenAI's ChatGPT Search, Google's Gemini API grounding, and rumors of Meta's entry into the space - presents an opportunity to examine how these tools might reshape our relationship with information discovery and knowledge creation.

Before we get deeper into the analysis, I like to offer my readers a bit of a user guide.

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Using ChatGPT Search: A Quick Guide

Credit: Based on Sabrina Ramonov's technical overview (check her prompt library out!!!)

Two Ways to Search

1. Automatic: ChatGPT searches when it needs current info

2. Manual: Click "Search the web" icon to force a search

Verifying Sources

• Look for "Sources" button at bottom of responses

• Click through to original sources

• Multiple sources indicate comprehensive search

Best For:

• Current events and news

• Weather and sports updates

• Local information (restaurants, services)

• Fact-checking and verification

Pro Tips:

1. Refine through conversation: Start broad, then narrow down

2. Check sources: Always verify important information

3. Chrome extension: Type "!g" before query to use Google instead

Current Access:

• Available: ChatGPT Plus, Team users

• Coming soon: Enterprise/Edu users

• Rolling out: Free users over coming months

Note: Features and availability as reported by OpenAI, November 2024

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The New Search Landscape

Charlie Guo

, writing about SearchGPT in August, identified the fundamental tension we're now seeing play out:

"Traditional search engines and LLM assistants have fundamentally different UX patterns. Google was designed to send users elsewhere as fast as possible; ChatGPT is designed to generate responses as helpfully as possible. This shift in user interaction could significantly impact how we consume information, potentially leading to longer, more in-depth engagements with AI assistants rather than quick jumps between websites."

This observation perfectly captures what I've come to think of as the distinction between inquisitive AI (which promotes active inquiry) and generative AI (which creates content). The difference is crucial for understanding how these tools might affect learning processes.

The latest developments show how each major player is approaching this tension differently. OpenAI's Adam Fry, demonstrating their new search capabilities, showed how ChatGPT can now handle complex, multi-step queries:

"In a pre-launch demo, OpenAI's ChatGPT search lead, Adam Fry, showcased the feature by searching for Apple's stock and any relevant news. In return, it displayed an interactive stock graph, upcoming earnings information, and news articles with clickable citations linking to original sources."

This interactive, multi-modal approach represents a significant evolution from traditional search, but it also raises questions about how users engage with information. Are they truly exploring and learning, or simply consuming pre-digested knowledge?

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The Technical Architecture

Google's approach with their Gemini API reveals a different philosophy. As reported by TechCrunch, they're offering granular control over how grounding works:

"Turning on grounding is as easy as toggling on a switch and deciding on how often the API should use grounding by making changes to the 'dynamic retrieval' setting. That could be as straightforward as opting to turn it on for every prompt or going with a more nuanced setting that then uses a smaller model to evaluate the prompt and decide whether it would benefit from being augmented with data from Google Search."

This technical detail matters because it shows how the architecture of these systems can influence the balance between inquisitive and generative modes of interaction.

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The Publisher Ecosystem

The relationship between AI search and content creators presents another crucial dimension. Charlie Guo highlights the emerging business model challenges:

"That's why Perplexity is figuring out a new business model, in the form of a Publishers Program. Advertisers can pay to have 'related follow-up questions' (i.e. ads) show up after a user completes a query. At the same time, 'partners' (i.e., media outlets) are paid whenever their content appears alongside a sponsored follow-up."

OpenAI's approach to this challenge, as reported by The Verge, involves direct partnerships:

"OpenAI has secured a litany of media partnerships in the last year, including big names like Hearst, Condé Nast, Axel Springer, and News Corp. While Fry says those partners will get more 'control' over how their content surfaces in ChatGPT, they won't be automatically given higher priority in queries."

These different approaches to content sourcing and attribution directly affect how students might interact with and verify information, compounding issues related to digital access and equity.

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The Hallucination Challenge

A critical concern for educational applications is the accuracy of AI-generated responses. OpenAI's Fry addresses this directly:

"Some amount of hallucinations come from just really not having access to the latest information. And so now that it has access to the latest information, it actually helps it make better decisions around what is the true, factual answer."

However, as The Verge notes, there's still room for concern:

"As for hallucinations — like when Google's AI Overviews infamously told users to put glue on their pizza — Fry believes that ChatGPT search is going to 'increase factual accuracy overall.'" When pressed about the fact that edge cases exist and mistakes are inevitable, he said "we'll try to be transparent" if it does trip up."

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The AI Use Spectrum in Education

This brings us to the heart of the matter for educators. I propose viewing AI use in education along a spectrum:

Google's Shrestha Basu Mallick inadvertently highlights why this distinction matters:

"When I get an LLM answer, I often go to Google Search and check that answer. We're providing a way for them to do this easily, so this is much valued by users."

This instinct to verify and explore beyond the initial AI response is exactly what we should be cultivating in students.

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The Path Forward

Charlie Guo poses the essential question:

"Are we losing something important and immeasurable by running all our consumption through an AI? Do we run the risk of 'dumbing ourselves down' as we increasingly rely on automated summarization?"

As educators, our response should be to develop frameworks that:

1. Balance Efficiency and Engagement:

   • Use AI search as a starting point for inquiry, not an endpoint

   • Teach students to trace AI-provided information back to primary sources

   • Encourage critical engagement with both AI summaries and underlying sources

2. Develop AI Literacy:

   • Help students understand when to use inquisitive vs. generative AI modalities

   • Teach verification strategies for AI-provided information

   • Foster awareness of AI's limitations and potential biases

3. Maintain Academic Rigor:

   • Set clear guidelines for appropriate AI use in different contexts

   • Develop assignments that leverage AI's strengths while promoting critical thinking

   • Create assessment methods that account for AI's capabilities

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Moving Forward

As these new search capabilities roll out, we have an opportunity to shape their educational use. The key isn't to avoid generative AI features entirely - they can be valuable tools for learning and creation. Rather, we need to be intentional about when and how we use different AI modalities, particularly in research and inquiry-based learning.

The goal is to harness these tools in ways that enhance rather than replace critical thinking and active learning. By understanding when to leverage inquisitive vs. generative AI features, we can better guide students toward meaningful engagement with these powerful new tools while maintaining the essential skills of academic inquiry.

Nick Potkalitsky, Ph.D.

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Check out some of my favorite Substacks:

Mike Kentz’s AI EduPathways: Insights from one of our most insightful, creative, and eloquent AI educators in the business!!!

Terry Underwood’s Learning to Read, Reading to Learn: The most penetrating investigation of the intersections between compositional theory, literacy studies, and AI on the internet!!!

Suzi’s When Life Gives You AI: An cutting-edge exploration of the intersection among computer science, neuroscience, and philosophy

Alejandro Piad Morffis’s Mostly Harmless Ideas: Unmatched investigations into coding, machine learning, computational theory, and practical AI applications

Amrita Roy’s The Pragmatic Optimist: My favorite Substack that focuses on economics and market trends.

Michael Woudenberg’s Polymathic Being: Polymathic wisdom brought to you every Sunday morning with your first cup of coffee

Rob Nelson’s AI Log: Incredibly deep and insightful essay about AI’s impact on higher ed, society, and culture.

Michael Spencer’s AI Supremacy: The most comprehensive and current analysis of AI news and trends, featuring numerous intriguing guest posts

Daniel Bashir’s The Gradient Podcast: The top interviews with leading AI experts, researchers, developers, and linguists.

Daniel Nest’s Why Try AI?: The most amazing updates on AI tools and techniques

Riccardo Vocca’s The Intelligent Friend: An intriguing examination of the diverse ways AI is transforming our lives and the world around us.

Jason Gulya’s The AI Edventure: An important exploration of cutting edge innovations in AI-responsive curriculum and pedagogy.