My palms were sweating as I stared at the screen, a client's AI-generated personality insights challenging everything I thought I knew about static labels. What happens when a machine sees your growth before you do?
AI offers a revolutionary approach to personality insights, moving beyond static MBTI tests to provide dynamic feedback on how our preferences evolve. By analyzing real-time communication, AI can highlight emerging traits and challenge self-perceptions, encouraging a more fluid and integrated understanding of personal growth, though it requires careful ethical consideration and human interpretation.
I’ll be honest with you: the first time I saw an AI system confidently contradict a client’s lifelong self-identification, a cold knot formed in my stomach. Not because the AI was wrong – it wasn't, not entirely – but because I felt a flicker of that old, familiar shame. The shame of being a therapist, a researcher, someone dedicated to understanding human complexity, and realizing I might have been contributing to the very issue I'd hoped to avoid: putting people into boxes.
My palms are sweating as I write this, recalling the moment. We rely so heavily on these frameworks, don't we? Myers-Briggs has been a foundation for so many, a language to understand ourselves and others. But what happens when that language becomes a cage, and a machine starts speaking a different truth? A more fluid, dynamic truth?
Meet Elias Thorne. Thirty-eight years old, Senior Project Manager at a sprawling tech firm. Elias was, by all accounts, an archetypal ISTJ. He lived by processes, by logic, by the steadfast belief that emotional outbursts were inefficiencies to be managed, not felt. His office was minimalist, his calendar meticulously color-coded. He saw himself as the calm, rational anchor in a sea of creative chaos – a badge he wore with quiet pride.
For years, Elias used his ISTJ label as a shield, sometimes even as an excuse. “I’m an ISTJ, so don’t expect me to be touchy-feely,” he’d joke, deflecting any requests for emotional vulnerability in team meetings. “My strength is in the details, the plan, the execution.” And he was exceptional at it. His projects consistently delivered on time, under budget. His team, though sometimes exasperated by his rigidity, respected his competence.
Then, his company rolled out a new AI-driven personalized growth program.
It promised to optimize team collaboration, pulling dynamic insights from communication patterns – emails, chat messages, even transcribed meeting notes. (Yes, with full consent.)
Elias, ever the pragmatist, saw it as a tool for efficiency. He expected it to confirm his ISTJ strengths, perhaps offering tips on how to more effectively manage those less logical colleagues. He was ready for data, for confirmation. He was not ready for a reckoning.
His initial AI profile was, indeed, textbook ISTJ. Heavy on Si (Introverted Sensing), strong Te (Extraverted Thinking). Predictable. But then, after three months of continuous analysis, the weekly reports started shifting. Small, almost imperceptible at first. Then, undeniable.
The AI began to flag an emerging pattern: a significant uptick in what it termed 'empathetic language constructs' and 'adaptability indicators.' It was picking up on phrases in his emails like, “I understand this might be challenging for you, let’s find a way,” or, “Given the team’s feedback, we can adjust the timeline here.” It noted instances where he’d unexpectedly pivot a project plan mid-stream, not because of a logical flaw, but because a team member was struggling or needed support.
Elias felt a profound sense of dissonance. Misunderstood. The AI was wrong. He was the ISTJ. He’d spent decades solidifying that identity. Yet, the data – his own words, his own actions – told a different story. He came to my office, bewildered, clutching a printout of his latest AI report. “Dr. Connelly,” he said, “it says I’m… developing my Feeling function. And my Perceiving preference. It’s like it’s trying to tell me I’m not me anymore.”
He felt exposed, threatened. His entire sense of self, built on the solid ground of his perceived type, was crumbling under the relentless, unbiased gaze of an algorithm.
So, the nitty-gritty of it. Traditional MBTI posits that our dominant and auxiliary functions are well-established, with tertiary and inferior functions developing later in life, often with conscious effort. Elias, as an ISTJ, would lead with Introverted Sensing (Si) and Extraverted Thinking (Te).
The AI wasn’t saying Elias had suddenly become an ENFP. Not even close. What it noticed was a natural, albeit suppressed, growth in his tertiary Introverted Feeling (Fi) and inferior Extraverted Intuition (Ne). These aren't new functions; they're always there, just less developed, often unconscious. The AI, with its capacity for granular pattern recognition, was noticing the expression of these functions in his daily interactions, particularly as his leadership role demanded more nuanced interpersonal skills.
Think about it: how many of us, when we’re under stress or in a new environment, default to our well-worn cognitive paths, even when they’re not serving us? The AI provided a mirror, reflecting back subtle shifts in Elias’s cognitive usage, not just his innate preference. This distinction is crucial, and it’s something static assessments simply can't capture.
So I went back to the data. Not just Elias’s, but broader research on AI and personality. MosaicAI Research (2025) reported their systems could achieve 80% accuracy for MBTI preferences and 85% for emotional expression from chat messages. That's significant. But here’s the rub: David Saeteros and colleagues from the University of Barcelona (2025), publishing in PLOS One, cautioned that while AI models can detect personality traits from written texts, MBTI prediction often relied more on linguistic artifacts – like specific keywords or grammatical structures – than a true understanding of cognitive functions. They observed that Big Five traits were more reliably detected by AI.
This gave Elias and me something to chew on. Was the AI seeing a genuine shift in his Fi and Ne, or was it just picking up on learned behaviors, new ways of speaking that mimicked those functions? And does it even matter, if the outcome is growth? I wrestle with this question myself, honestly.
The real friction, not just for Elias, but for so many of us exploring AI in personal growth, comes from this: we cling to our labels. We invest in them. They become part of our story, our defense mechanism, our comfort zone.
Real Talk: I’ve done it myself. Told myself, “Oh, I’m an INFJ, so of course I’ll overthink this.” It’s a convenient shortcut, a way to explain away our struggles or justify our preferences. We tell ourselves personality types are static, immutable blueprints. But AI is starting to show us something different: while our core preferences might remain, their expression – and our development of less preferred functions – is incredibly dynamic.
Elias felt betrayed by his own type – and by the AI for revealing this 'betrayal.' He was used to the concrete, the defined. The AI was offering ambiguity, growth, a challenge to his very sense of self. It was like his internal GPS was suddenly showing him an alternate route, one he hadn't programmed.
This is a critical competitor gap that few are addressing: the psychological impact of continuous AI personality analysis. What does it do to our self-perception when an algorithm tells us we’re changing, often before we've consciously acknowledged it ourselves? Does it foster self-awareness, or create anxiety about not fitting our own 'label' anymore?
The turning point for Elias wasn’t immediately accepting the AI’s new 'typing.' It was much more nuanced. It came from a series of reflective exercises I guided him through, focusing on the behaviors the AI was flagging, rather than the labels.
We started with specific dialogue snippets the AI had highlighted. For example, a chat exchange where a junior team member, Sarah, was struggling with a coding bug. Elias’s initial response, before the AI feedback, would have been: “Refer to the documentation. Deadline is Friday.” But the AI showed a shift. Here’s a snippet of the dialogue the AI flagged:
Sarah: “I’m really stuck on this. Feeling pretty overwhelmed.”
Elias: “I hear you, Sarah. That’s tough. Take a breath. Have you tried X, Y, Z? I’m here if you want to walk through it, even if it’s just to vent.”
That last sentence – “I’m here if you want to walk through it, even if it’s just to vent” – was a stark departure from his old patterns. It showed a burgeoning Fi, a recognition of her emotional state, and an offer of support beyond just task completion. It was a step towards Extraverted Intuition (Ne) too, in its openness to an undefined, less structured interaction (“even if it’s just to vent”). He was allowing for possibility, not just process.
We discussed how this behavior felt. He admitted, “It felt… right. I didn’t think about my type. I just thought about Sarah.” And there it was – the cognitive shift happening in real-time, beneath the surface of his conscious typing. It wasn't about changing his type, but expanding his repertoire of responses.
This experience aligned with fascinating research from ETH Zurich, BASF SE, Cledar, and IDEAS Research Institute (2025). They developed an 'MBTI-in-Thoughts' framework where AI agents, primed with specific MBTI types, exhibited aligned behavior. Crucially, their 'Feeling' agents produced significantly more empathetic, personal, and optimistic stories in creative writing tasks than 'Thinking' types. This suggests AI can not only detect these expressions, but even simulate them, offering a glimpse into how these dynamic insights could be used for personalized growth – not just identifying a trait, but coaching its conscious development.
Elias began to see the AI not as a re-labeling machine, but as a sophisticated mirror, showing him the parts of himself that were growing, evolving, often in response to the demands of his life and leadership role. It wasn't challenging his core; it was challenging his definition of core. It allowed him to reframe his 'ISTJ' identity not as a fixed state, but as a starting point, a preferred style he could consciously stretch beyond.
This goes beyond Elias, and even beyond MBTI. It’s about how we relate to our own identities, especially when confronted with new data. It's about the courage to let go of what we think we are, to embrace who we’re becoming. And AI, for all its algorithmic coldness, is proving to be a surprisingly warm and insightful force for that personal path.
It’s about understanding that dynamic insights aren't just about AI predicting your type better. It’s about AI revealing your growth trajectory. My therapist just looked at me during one of my own moments of identity crisis and said, “Sarah, you’re a mess – a beautiful, evolving mess.” And you know what? It’s true for all of us. AI just gives us more detailed maps of our beautiful, evolving messes.
What if the real question isn't how accurately AI can label us, but how effectively it can show us the ways we are already transcending our labels?
This technology isn't without its challenges – privacy concerns, the potential for over-reliance, the risk of AI reflecting stereotypes rather than genuine cognitive understanding. These are real, and we, as researchers and therapists, have a responsibility to address them head-on. But we also have a responsibility to explore the profound potential for self-discovery.
So, I challenge you: Where are you clinging to an old label, an old story about yourself? What new data – from a system, from a loved one, from your own quiet reflection – might be whispering a different truth?
Courage isn't about having all the answers. Courage is about having the willingness to ask new questions, especially about ourselves, and to sit with the discomfort of not knowing. It lives in our bodies, in the quiet strength to be vulnerable, to be an evolving mess, and to keep showing up.
Research psychologist and therapist with 14 years of clinical practice. Sarah believes the most honest insights come from the hardest moments — including her own. She writes about what the data says and what it felt like to discover it, because vulnerability isn't a detour from the research. It's the point.
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