Can Psychology Really Fix Your Broken Chatbot?

Source: ChatGPT modified by NostaLab.

Come here and take a seat on my technology couch.

You ask a chatbot a simple question, and it gives you an unhelpful, robotic response. Or worse, it replies with a kind of artificial, almost creepy warmth that feels more unsettling than reassuring. My sense is what's broken here isn’t just some abstract model-weighting priority. This isn’t just a glitch in the system, it’s a mismatch between human expectations and machine design. A recent Harvard Business Review article makes a similar point—that fixing chatbots is less about engineering and more about understanding human behavior. I agree, but I’d go further.

This isn’t just a customer service issue, it’s a cognitive one. We’re bumping up against something bigger than frustration. What we’re really confronting is this strange chasm between how we think and how machines mimic thought. I’ve spent the last few years exploring the fault lines between AI and human cognition—particularly around expectation, emotion, and the architecture of thought. Many of those ideas are now showing up in real-world challenges. So let’s unpack three core principles that could help make AI not just more useful, but more humanly intuitive.

1. Expectation Management: Don’t Fake Humanity

In my post, AI Isn’t Just a Tool. It’s a Test, I argued that AI often reflects us more than it thinks for itself. We project intelligence, intention, even morality onto systems that are, at their core, probabilistic engines. The result is often disappointment. Chatbots that overpromise and underdeliver don’t always fail because of bad code, it might just be due to misaligned expectations.

The solution is psychological realism. Be honest about what the AI can and can’t do. Design for alignment between user expectations and machine capabilities—not illusion.

2. Emotional Continuity: Emotions Need Consistency, Not Depth

In The Empathy Algorithm, I introduced a triad for understanding emotional engagement in AI that includes depth, reach, and consistency. Of these, consistency is the most overlooked—and sometimes, the most important. People forgive shallow empathy if it feels stable. But a bot that responds warmly one minute and flatly the next can shatter that trust.

Emotional resonance isn’t about simulating therapy, it’s about emotional coherence. Like a good conversation partner, a chatbot should feel predictable, not only in content, but in tone.

3. Cognitive Alignment: Make Thought Easier, Not Louder

Many chatbots mimic language well but miss the cognitive mark. In LLMs as Cognitive Catalysts, I describe how language models can amplify human thought—not by replacing thinking, but by structuring it. This is where many chatbots can fall short. They don’t scaffold insight, they just echo data. And this echo is rather deceptive, conjuring words and ideas that are often more "cognitive theater" than substantial and valuable ideas.

A chatbot that helps you make decisions, synthesize information, or reflect more clearly is doing more than completing sentences. It’s becoming a partner in cognition who completes your sentence, not just amplifies or contorts your words.

AI Performance to Human Psychology

The future of AI-human interaction isn’t just about squeezing more power out of transformers. It’s about tuning our machines to the contours of the human mind. We don’t need chatbots that are just more intelligent—we need ones that are more aligned with how we feel, think, and decide. Perhaps even being about more "human-centric" with an underlying perspective that understands its own technological subordinance.

The promise of AI was never just automation, but augmentation. Yet augmentation only works when it amplifies the right things—our clarity, our empathy, our ability to think and reflect. We’re entering an era where psychological fluency will be an important differentiator for AI. Not just in how it responds, but in how it resonates.

That shift requires us to treat AI design as both an engineering and a human science. Psychology isn’t a soft science in this context—it’s the hard edge of utility. And if we get it right, we don’t just fix chatbots. We fix something that isn't going away—the way humans and machines engage with each other.