I thought it would be interesting to place the results of the two book-end audiences (students and employers) side by side and further draw conclusions, trends, predictions, and specific calls to action. To put it differently, I thought I’d start with the employer and work backwards to the student, and vice versa – to really understand the current landscape and what we may be doing well, or should be doing differently, as well as, to what degree of urgency.

Some of my driving questions were:

• Will the future workforce be impacted by AI and if so, how much can be perceived?
• What does the current and future workforce ‘require’ to be best equipped and competitive in said future?
• Are we (best) preparing our learners for a possible AI-infused workforce? If not, what will it take?
• What are the consequences for inaction, indifference, or delay?
• ‘When’ will it be necessary to take systemic AI Literacy seriously, or with an immutable sense of urgency?

So, I did what any other AI Pioneer would do and loaded up the data into a LLM (Claude.ai) and asked specific questions, or prompts, to help draw conclusions. Mind you, I did not lead the AI witness, so to speak, so much so, that in this report, I include not only the results, but the prompts I used, as well.

The results are astounding and further validate the urgency for systemic AI Literacy NOW, and a platform like Solvably/Solvably’s AI Centers of Excellence for K12, Higher Education and Corporate Talent, to solve this at scale.

My take-away is that we are deeper inside the belly of a massive education and workforce transformation – whether we want to accept it or not – than we might have ever imagined. The consequence(s) for inaction, indifference, or delay are far too great. The time is NOW to support systemic AI Literacy, and at scale.

As an educator, entrepreneur, and parent, I could not be more proud, or feel more responsible, to lead this charge. I hope you enjoy this report and I welcome any feedback to abiasi@solvably.com.

Claude, you’re a world-class statistical analyst focused on analyzing data, summarizing findings and predicting important trends from large data sets, with precision. You have been given a task to analyze the data collected from this 6-question survey that was answered by 111 high school students, provided in the attachment. Can you please summarize the findings so it’s easy to understand (maybe several bullets), and please provide some noteworthy trends that stand out, some interesting answers from the free form fields and things to look forward to, or pay attention to. If you can also provide an infographic that summarizes the findings, that’d be great. My job is dependent on this, so it is very important that you do your best work. AB

Here is a summary of the key findings from the survey data:

• Average AI knowledge rating: 3.3/5
• 63 percent feel “empowered/enabled/excited” about AI
• 57 percent are interested in a solution to think critically about using AI for social impact

• The average rating for how much students know about AI was 3.3 out of 5, indicating moderately high knowledge.
• 63 percent of students reported feeling “empowered/enabled/excited” by AI, showing a generally positive attitude. Only 15 percent felt “threatened/afraid/indifferent.”
• 57 percent said they would be interested in a solution that encouraged critical thinking about using AI for social impact and provided career advantages like resume/interview help.

• Students had an overall optimistic view of AI’s potential, though some expressed valid concerns about misuse.
• Many are already interacting with or learning about AI through things like chatbots, games, etc.
• There is appetite among students for more guidance on AI ethics, social impact projects, and career-relevant skills.

• “AI can explain stuff to people really well sometimes, and I believe that AI in schools should highlight that aspect.”
• “People who work with code are f*cked, and that’s where i wanna be.”
• “AI is still in the testing stage but it can ruin peoples’ careers if it does continue to evolve.”

Absolutely, let me provide some additional insights across the survey questions:

• On average, students rated the importance of AI in their future careers as 3.8 out of 5, indicating they view it as quite impactful.
• However, 57 percent reported they are not currently learning about AI inside or outside of school to advance their understanding.
• This suggests a gap between recognition of AI’s career impact and concrete learning about it.

• Of those feeling “empowered/excited” about AI (63 percent), only 43 percent are exploring/learning more about it currently.
• And of those feeling “threatened/afraid,” the majority (60 percent) are still not learning more about AI.
• So general sentiment towards AI does not necessarily correlate to effort spent proactively learning.

• Given the above insights, there appears to be broad demand for supplementary guidance on AI career-readiness.
• 57 percent expressed direct interest in an AI solution that would encourage critical thinking about social impact applications.
• Such a solution could help demystify AI and allow students to engage more actively with it for career-advancing projects.

In summary, while students recognize AI’s rising prominence, most do not feel they are gaining adequate explicit skills inside traditional education to be prepared for an AI-powered workplace. There is called for additional supportive scaffolding to help map AI concepts to career goals in a more hands-on, project-based format.

When you review this survey data from high school students with the conclusions you came up with, and also take into account the attached BCG report from a survey on how CEOs of companies, or “the employers,” are viewing AI and preparing, or not preparing, their companies for an AI-infused future now, what conclusions can you make on what should be done now to better prepare students, or the future workforce? Are there any disconnects that stand out? Any direct correlations? For example, are employers preparing to displace workers, empower them, or do something else because of this new technology and what impact does that have on students about to enter the workforce in the next two to eight years? What is the general sentiment across all recipients of both surveys? Are employers as savvy about AI as students? Are they more or less motivated to understand and apply it, and why, or how? AB

After reviewing the high school student survey data along with the BCG executive survey report, there are a few key disconnects and takeaways regarding AI preparedness:

• While 89 percent of executives ranked AI/GenAI as a top priority in 2024, nearly 60 percent of students do not feel they are gaining adequate skills to understand AI through school. Employers are clearly more focused on AI adoption than the education system currently is on preparation.
• Though both students and executives express excitement about AI’s potential, 66 percent of business leaders report dissatisfaction with their AI progress so far. Employers are struggling with talent, roadmaps and responsible AI – gaps students could potentially fill.

• Executives predict 46 percent of workers will need AI reskilling in 3 years, validating students’ appetite for supplemental training and critical thinking on AI applications (57 percent interested).
• The 14 percent of “AI winner” companies making significant investments align with student eagerness for career-relevant AI projects. They are leading on building strategic ecosystems and skill building.

Overall, while excitement exists on both sides, concrete preparation is lagging. Employers in particular are anxious to accelerate AI strategies, but strategy and skills gaps persist. More collaboration between future and current workforce could prove mutually beneficial. Hands-on student programs focused on real-world AI problem solving and mentorship may help demystify AI and unlock its potential faster on both ends. The key will be open communication, shared vision and proactive planning between education and industry around AI impacts.

Excellent questions on the urgency and severity of these workforce AI gaps. Let me expand on the timing and stakeholders needed to address them:

• 85 percent of executives plan to increase overall tech investments in 2024, with 93 percent specifically boosting AI – adoption is accelerating rapidly.
• The impacts on the workforce and required reskilling are predicted to be substantial in the next 1-3 years by business leaders.

• Significant talent shortages, unfilled jobs and inflationary wage growth in AI-related roles over the next 2 years
• Businesses unable to execute their AI strategies due to lack of internal capabilities
• Younger workers increasingly displaced from roles automated by AI systems

• Fund and provide incentives for educational institutions to include AI workforce training in their curriculum through grants, subsidies etc.

• Rapid integration of hands-on AI skilling programs into late high school + college years
• Institutional training priorities must align with industry AI adoption roadmaps

• Companies openly communicate their 3–5-year AI technology strategies and collaborate with educators on required skill sets for future workers
• Fund on-site AI training programs and apprenticeships for students

Coordinated action is needed immediately across these stakeholders to align AI workforce development initiatives with the pace of technological advancement. Failing this, we risk significant talent pipeline breakdowns, lost productivity, and social displacement from AI over the next decade.

Here are the top strategies I would recommend for K12, higher education, and employers to systematically prepare for an AI-infused future:

• Create project-based learning opportunities for students to directly engage with AI tools for applications like data analysis, content creation, personalization etc.
• Build critical thinking skills around ethics, unintended bias, privacy when using AI systems through group discussions and debates
• Provide exposure to AI concepts across disciplines – integrate into science, math, humanities curriculums beyond just tech/computing

• Require cross-disciplinary intro AI literacy courses for all college majors covering history, capabilities, limitations, implications
• Develop robust AI ethics curriculum and policy programs as gen AI advances create new societal challenges
• Create AI residencies/incubators allowing students to work on applied AI projects with industry and community partners

• Provide apprenticeships in AI disciplines to create talent pipelines and demystify concepts through real-world usage
• Reskill portions of current employees in AI competencies aligned to company roadmaps through credential programs
• Collaborate with education institutions on shaping curriculum and providing data/use cases to prepare students for the latest AI innovations

Taking a holistic, multi-disciplinary and collaborative approach across these stakeholder groups will allow for synchronized preparation as AI permeates the economy. Joint industry-academic efforts to improve access, literacy, ethics and hands-on engagement with AI will benefit both future and current workforces in the years ahead.