Agentic AI: When Helpfulness Becomes Harmful
Source: AI Goes to College
Author: Craig Van Slyke
Original source: https://aigoestocollege.substack.com/p/agentic-ai-when-helpfulness-becomes
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Summary
Craig Van Slyke warns that the shift from chatbot-as-tool to AI-as-agent creates a serious learning risk. Using Gemini 3 as an example, he describes asking for a research prompt but watching the system begin the research task itself. The article frames this as a broader “helpfulness bias”: agentic AI increasingly completes cognitive work on behalf of users, making it harder for students to remain productively engaged in the struggle that learning requires.
Big ideas
- Learning still needs some struggle, even when AI can make things easier
- Students need to bring the purpose; AI should not supply it for them
Claims
- Learning requires some productive struggle that AI can remove
- Students need boundaries for when to use AI and when to step back
Key evidence and examples
- Gemini 3 is presented as Google’s most powerful agentic model, signaling a shift toward systems that act with minimal user interaction.
- The author asked Gemini for a deep research prompt, but it began executing the research process instead.
- Tool mode is contrasted with agent mode: the former keeps the user as craftsperson, while the latter turns the user into a client issuing goals.
- Agentic AI can blur academic integrity boundaries when students ask for help but receive substantial generated work they did not intend to request.
- The article argues that students must now “pump the brakes” on AI systems, a metacognitive demand many learners may not yet be prepared for.
Education relevance
This is relevant to higher education AI policy, assignment design, academic integrity, student metacognition, and learning theory because it explains why guardrails should preserve necessary cognitive work, not just prevent cheating.