Personal AI
“Personalized” Doesn’t Have To Be a Buzzword
“We can’t let old doomsdayers colonize the future with their misery.”
—Cat Tully, The School of International Futures
Journey back in time with me as I share three challenges I’ve been engaged with in the education and workforce arenas, and I’ll then reveal how generative artificial intelligence (genAI) could help us move forward in stunning leapfrog fashion.
Problem #1: The Two-Sigma Problem
When I was working with Clayton Christensen and his Institute for Disruptive Innovation, I wrote about how advancements in technology could potentially help us center on students’ needs in a way that even Benjamin Bloom thought unfathomable back in 1984 when he described the 2-sigma problem.
Bloom knew that when students were tutored one-on-one in mastery learning, they were able to perform at approximately two standard deviations above the average of the control class. The revelation was startling: “[A]bout 90% of the tutored students and 70% of the mastery learning students attained the level of summative achievement reached by only the highest 20% of the students under conventional instruction conditions.” Moreover, tutored students’ abilities to problem-solve, apply principles, analyze, and be creative, or what Bloom called their Higher Mental Process achievement was also two standard deviations above the control students.
What this indicated was truly thrilling: nearly every student had the potential to reach very high levels of learning.
The major problem, however, was that one-to-one tutoring was simply “too costly for most societies to bear on a large scale.”
Problem #2: AI Solutions for Only a Select Few
In my book, Long Life Learning: Preparing for Jobs that Don’t Even Exist Yet, I explain how as work lives extend, mid- and late-career working learners face a particularly daunting challenge: Our systems and structures were never set up for seamless on- and off-ramps in and out of learning and work. So, how will we access long-life learning in order to redefine our roles in both careers and society?
I did share what I called a few “seeds of innovation,” some really exciting solutions emerging: Certain forward-thinking enterprises were adopting new AI-powered platforms that could serve as “skills compasses” for their own employees. These solutions helped employees:
Identify the skills they already had
Discover other fields where they could deploy those same skills
Surface the skills they needed to acquire to move in that direction
These models could estimate, for example, that a person was 85 percent of the way there to switch into a role in human resources or 30 percent of the way there to skill up toward being a network analyst.
Unfortunately, however, these really intriguing tools were not only extremely expensive but also only available to a subset of workers who were employed by the enterprises that licensed these particular solutions.
It was inevitably kind of heartbreaking when I would share with audiences that these solutions actually existed, but they were just out of reach and not easily accessible.
Problem #3: Learning & Employment Records (LERs) Can’t Scale
Over the last decade, there has been a movement afoot to enable more people to gain access to their own data, or their own Learning and Employment Records (LERs). Rather than, for instance, a college or an employee owning your skills data, what might it look like if each of us had our own interoperable learning and employment record that validated the specific skills and knowledge and capabilities we bring to the table? That profile informed by rich data and information would be portable into any domain, and all providers would be incentivized to share the right kinds of data to further enrich our profiles.
Many different organizations and innovators have been developing solutions around this concept of a “skills carrier” that could help more people move to better economic opportunities by having access to this portable record with verifiable experiences.
Unfortunately, however, LERs have failed to gain widespread traction as a movement. Most employers wouldn’t adopt a more costly option and switch to LERs when pdf resumes do the job well enough for the time being.
So, to sum up the challenges:
In 1984, the idea of scaling a mastery-based tutorial experience had been unfathomable.
There have not been easily accessible consumer-facing solutions enabling us to understand where we are today (in terms of our skill sets) relative to where we want to go.
LERs have been a very exciting idea but one that has failed to convince employers to change their hiring behaviors.
Today, however, is a different kind of moment. GenAI is now rapidly evolving to the point where we may be able to imagine a new way forward.
We can begin to imagine solutions truly tailored for each of us as individuals, our own personal AI (pAI). pAI could unify various silos of information to construct far richer and more holistic and dynamic views of ourselves as long-life learners. A pAI could become our own personal career navigator, skills coach, and storytelling agent.
Three particular areas emerge when we think about tapping into the richness of our own data:
Personalized Learning Pathways & Dynamic Skill Assessment: Set up with the right data infrastructure, a pAI could constantly monitor our skills, strengths, and learning style by analyzing work, all formal and informal learning, as well as feedback from peers or employers. With a more dynamic profile, the AI could recommend personalized learning pathways, whether that's enrolling in a specific course, taking on a new project at work, or practicing a particular skill. Our personal agent would adapt recommendations in real time, adjusting to new industry trends, personal interests, or even career pivots. It could also analyze labor market data to suggest in-demand skills, certifications, or fields that align with our learning and work goals. Ideally, with a fuller view of the person, the pAI would illuminate potential future career pathways that we could never have imagined for ourselves. A pAI would help us envision a better way forward.
Storytelling for Employers: A pAI could automatically create and update our profile and portfolio by tracking our progress, skills, projects, and accomplishments. It could generate personalized resumes and cover letters that highlight the most relevant skills and experiences for each job application, leveraging AI-driven insights on what each employer or industry prioritizes. More importantly, a pAI would serve as a helpful reflection tool for us to better understand the skills, knowledge, capabilities, and assets we bring to the table so we might understand how to tell our own story and journey of growth to others, especially prospective employers. pAI could also simulate interviews, offer feedback on answers, and help us craft stories that showcase our skills in the best possible light.
Ongoing Mentorship and Feedback: A pAI could serve as a virtual accountability coach by mentoring and offering continuous feedback, encouragement, as well as tactical next steps. Our personal AI agent could analyze performance metrics, both qualitative (feedback from managers or peers) and quantitative (project outcomes), and provide insights on how to improve. And beyond technical skills-building, a pAI could help with low-stakes scenario-based learning, simulations, and even role-playing exercises to help with the human and character skills building required for a longer future of work.
The idea of our own personal AI offers us a way into imagining an intellectual and reflective space for all of us as long-life learners to explore the next phase of our lives, reframing aging as a time of growth and opportunity — designed to cultivate wisdom, resilience, and adaptability.
So grateful to my colleague and friend Connie Yowell who has been an incredible thought partner in this work.
Don’t forget to check out my latest TEDx talk:
Dr. Michelle Weise is the author of Long Life Learning: Preparing for Jobs that Don’t Even Exist Yet and consults as an outsourced Chief Innovation Officer for businesses and higher education institutions. For more information, please visit: michelleweise.com.