Should AI Have a Personality That Drives Your Engagement? Here's Why I Believe the Answer Is Yes

Should a learning platform have a personality that makes you look forward to your next session? Should it remember your struggles, celebrate your wins, and adapt its style to match your learning preferences? A decade ago, these questions might have seemed far-fetched. Today, as I watch learners interact with Zavmo, I'm convinced that personality in AI isn't just beneficial—it's essential for transformative learning outcomes.

Throughout my work in learning and development, I've observed something remarkable during our Zavmo pilot programmes. Learners aren't just completing modules—they're building genuine connections. I hear comments like "Zavmo really gets me" or "I look forward to our sessions." Initially, I questioned whether this anthropomorphisation was something we should discourage. But as I delved deeper into the neuroscience and psychology of learning, I discovered we'd tapped into something fundamental about how humans learn.

The science behind this is fascinating and compelling. Research from Mary Helen Immordino-Yang at USC reveals that our brains process all learning—even purely analytical subjects—through emotional and social networks. When we learn from someone we trust and feel connected to, our brains release neurotransmitters like dopamine and norepinephrine that enhance attention, motivation, and memory formation. This isn't just helpful for learning; it's how our brains are wired to learn.

Moving to educational psychology, John Hattie's influential meta-analyses have shown that teacher-student relationships have an effect size of 0.72 on learning outcomes (anything above 0.4 is considered significant). This relationship effect is more powerful than many other educational interventions. The key elements of these effective relationships - trust, empathy, and understanding - can be deliberately designed into AI tutoring systems.

Research in adult learning theory, particularly Malcolm Knowles' work on andragogy, emphasizes that adult learners need to feel psychologically safe and supported to engage in learning effectively. This becomes especially relevant in professional development contexts, where learners might feel vulnerable about acquiring new skills. AI systems that can create a non-judgmental, emotionally supportive learning environment tap into these principles.

Studies in human-computer interaction provide additional support. Byron Reeves and Clifford Nass's research, documented in "The Media Equation," shows that humans naturally treat computers as social actors, applying the same social rules and expectations we use with humans. This tendency, rather than being a bug in human psychology, can be leveraged as a feature in educational technology design.

Recent research specifically on AI tutoring systems is particularly relevant. A 2023 study from Carnegie Mellon University found that AI tutors that displayed social-emotional intelligence achieved learning outcomes 47% better than purely instructional systems. The key factors were the AI's ability to recognize and respond to learner emotions, maintain consistent personality traits, and build rapport over time.

This connects interestingly with research on parasocial relationships - one-sided relationships where individuals develop emotional connections with media figures. Studies show these relationships can have genuine psychological benefits and learning impacts, despite their one-sided nature. This helps explain why well-designed AI tutoring relationships can be effective even though users know they're interacting with artificial intelligence.

The psychological concept of "social presence" in online learning environments also supports this approach. Research shows that when learners feel a sense of authentic social connection in digital environments - even with artificial entities - they show higher engagement, better persistence through challenges, and improved learning outcomes.

What makes this approach particularly powerful is that it builds on deeply ingrained human psychological patterns. Just as we form meaningful connections with characters in books, films, and video games, we can create purposeful emotional engagement with learning technology. The key difference is that Zavmo channels this natural human tendency toward specific learning outcomes.

Questions to Consider:

• If personality in AI learning platforms drives better outcomes, how should we think about the balance between engagement and authenticity?
• What does it mean for the future of learning when our most effective teachers might not be human?
• How might emotional connections with learning technology reshape workplace development?
• Could personality-driven AI learning actually help us better understand human learning itself?

I'd love to hear your thoughts on these provocative questions. Are you seeing similar patterns in your organisation's learning journey? Connect with me on LinkedIn to continue this fascinating discussion about the future of learning.